JP2025530659A - Method and system for kinship assessment for missing persons and disaster/conflict victims - Google Patents

Abstract

The present disclosure in some aspects relates to performing DNA-based kinship analysis involving the analysis of between 2,000 and 50,000 SNPs, including sample preparation and sequencing techniques and methods that can be used to calculate the degree of relatedness of a DNA profile from a person of interest (e.g., a missing person or a victim of conflict or disaster) to one or more reference DNA profiles in a reference set of DNA profiles that includes at least one DNA profile from a relative of the person of interest.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63/398,512, entitled "METHODS AND SYSTEMS FOR KINSHIP EVALUATION FOR MISSING PERSONS AND DISASTER/CONFLICT VICTIMS," filed August 16, 2022, and to U.S. Provisional Application No. 63/445,541, entitled "METHODS AND SYSTEMS FOR KINSHIP EVALUATION FOR MISSING PERSONS AND DISASTER/CONFLICT VICTIMS," filed February 14, 2023, the contents of which are incorporated by reference in their entireties.
Field

The present disclosure, in some aspects, relates to methods and systems for DNA-based kinship assessment of persons of interest, such as missing persons and victims of conflict and disasters.

BACKGROUND Current methods for generating DNA profiles for comparison in genetic databases include genotyping using high-density SNP microarrays and whole genome sequencing (WGS), followed by linking evidentiary samples to distant relatives in the database. These require large amounts of high-quality DNA samples and are not designed for family search or forensic purposes. Forensic casework samples are generally small and low-quality and typically require querying publicly accessible genetic databases to confirm family relationships. However, in the context of missing persons or victims of disasters or conflicts, family members may be hesitant to provide genetic samples that would help identify such persons if their genetic samples were to be uploaded to publicly accessible databases. Therefore, there is a need for new and improved methods for generating DNA-based profile analyses that enable confirmation of family relationships of persons of interest, such as missing persons or victims of disasters or conflicts, without requiring the use of publicly accessible genetic databases.

SUMMARY Provided herein are methods for performing DNA-based kinship analysis, the method comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising a plurality of between at least or between about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; generating a nucleic acid library from the amplified products; sequencing the nucleic acid library generated from the amplified products; analyzing the sequences of the amplified products; genotyping the plurality of SNPs, thereby generating a DNA profile; and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that comprises one or more reference DNA profiles from relatives of the person of interest.

Also provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; generating a nucleic acid library from the amplified products; sequencing the nucleic acid library generated from the amplified products; genotyping the plurality of SNPs, thereby generating a DNA profile; and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

In some of any such embodiments, the sequencing is performed using massively parallel sequencing (MPS). In some of any such embodiments, the sequencing does not include whole genome sequencing (WGS).

In some of any of such embodiments, the method further includes generating a family tree that includes DNA profiles related to the one or more DNA profiles.

Also provided herein are methods for constructing a nucleic acid library for a person of interest, comprising: providing a nucleic acid sample from the person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000-50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions. In some embodiments, the method further comprises sequencing the amplified products to produce a DNA profile of the person of interest.

Also provided herein is a method for constructing a nucleic acid library for a reference DNA sample, the method comprising: providing a nucleic acid sample from a relative of a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising the amplified products, wherein the amplification is performed in one or more multiplex PCR reactions.

In some embodiments, the relative is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest. In some of any of such embodiments, the relative is a first-, second-, or third-degree relative of the person of interest.

In some of such embodiments, the nucleic acid sample comprises genomic DNA. In some of such embodiments, the nucleic acid sample comprises one or more enzyme inhibitors. In some of such embodiments, the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. In some of such embodiments, the nucleic acid sample comprises low-quality and/or low-abundance nucleic acid molecules. In some of such embodiments, the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA. In some of any of such embodiments, the low quality nucleic acid molecules are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or a Degradation Index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some of any such embodiments, low quality nucleic acid molecules have a DI of at least 1 and up to 158.3 or less. In some of any such embodiments, the nucleic acid sample comprises high quality nucleic acid molecules. In some of such embodiments, high quality nucleic acid molecules have a DI of less than 1.

In some of such embodiments, the person of interest is a missing person. In some of such embodiments, the person of interest is a victim of a disaster or conflict.

In some of such embodiments, the nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bone, or skin. In some of such embodiments, the nucleic acid sample is derived from saliva, blood, or semen. In some of such embodiments, the nucleic acid sample is derived from bone or hair. In some of such embodiments, the nucleic acid sample is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, semen, or other bodily fluid, or containing hair or skin cells.

In some of any of such embodiments, the nucleic acid sample comprises 3 pg to 100 ng or about 3 pg to 100 ng of genomic DNA. In some of such embodiments, the nucleic acid sample comprises between 100 pg to 5 ng or about 100 pg to 5 ng of genomic DNA, between 50 pg to 5 ng or about 50 pg to 5 ng of genomic DNA, or between 3 pg to 5 ng or about 3 pg to 5 ng of genomic DNA. In some of such embodiments, the nucleic acid sample comprises 1 ng or about 1 ng of genomic DNA.

In some of any such embodiments, the plurality of SNPs comprises kinship SNPs (kiSNPs). In some of any such embodiments, the plurality of SNPs comprises Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs comprises kiSNPs and Y-SNPs. In some of such embodiments, the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs comprises SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. In some of any of such embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are related SNPs.

In some of any of such embodiments, the reference set of DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are each first-, second-, third-, fourth-, or fifth-degree relatives. In some of any such embodiments, at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.

In some of any such embodiments, each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. In some of any such embodiments, each relative of the person of interest in the reference set of DNA profiles is a first-, second-, or third-degree relative of the person of interest, respectively. In some of any such embodiments, the identity of each relative of the person of interest in the reference set of DNA profiles is known. In some of any such embodiments, the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known. In some of any such embodiments, the reference set of DNA profiles is in a database. In some embodiments, the database is not publicly accessible.

In some of such embodiments, the sequencing comprises a sequencing plexity of up to 40-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of up to 32-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of 12-plex to 32-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of 24-plex to 32-plex. In some of any of such embodiments, the sequencing is at or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In some embodiments, the sequencing comprises a sequencing plex of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In some embodiments, the sequencing comprises a sequencing plex of 8-16-plex or about 8-16-plex for post-mortem samples, and/or the sequencing comprises a sequencing plex of 24-40-plex or about 24-40-plex for ante-mortem samples. In some embodiments, the sequencing comprises a sequencing plex of 12-plex or about 12-plex for post-mortem samples, and/or the sequencing comprises a sequencing plex of 32-plex or about 32-plex for ante-mortem samples. In some of any of such embodiments, the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex, or about 30-plex, 31-plex, or 32-plex.

In some of any such embodiments, the method further includes verifying the identity of the person of interest.

Also provided herein is a method for calculating degree of relatedness, comprising obtaining a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs, where the DNA profile is from a person of interest, and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

Also provided herein is a method for calculating degree of relatedness, the method comprising generating a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs, where the DNA profile is from a person of interest, and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

In some of any such embodiments, the relatedness is calculated using a kinship model. In some of such embodiments, the relatedness is calculated using a kinship model that is trained using a PCA method. In some embodiments, the PCA method for training the kinship model is or includes PCA. In some of such embodiments, the PCA method is PC-AiR. In some embodiments, PC-AiR includes the steps of: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally, in a training DNA profile, where pairings with a relatedness coefficient >0.025 are confirmed as closely related and pairings with a relatedness coefficient <-0.025 are confirmed as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating this set as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to the samples in the unrelated sample set, thereby designating this set as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i).

In some of such embodiments, the PCA method is a modified PC-Air. In some of such embodiments, the modified PC-Air includes: (1) estimating relatedness coefficients between all pairs of samples of training DNA profiles, as appropriate, in a training database, where pairings with relatedness coefficients >0.01 are identified as closely related and pairings with relatedness coefficients <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).

In some of any such embodiments, calculating the degree of relatedness includes calculating the coefficient of relatedness using PC-Relate. In some of such embodiments, the degree of relatedness is calculated by providing a DNA profile of the person of interest as input to PC-Relate. In some of such embodiments, the degree of relatedness is calculated by providing a kinship model and a DNA profile of the person of interest as input to PC-Relate. In some of such embodiments, one or more reference DNA profiles are further provided as input to PC-Relate.

In some of any of such embodiments, calculating the degree of relatedness includes calculating the coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively.

In some of any of such embodiments, calculating the degree of association includes calculating a likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs, including related SNPs, from among the plurality of SNPs between the DNA profile and one or more reference DNA profiles.

In some of any of such embodiments, calculating the likelihood ratio includes dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles are related by the probability that the DNA profile and the reference DNA profile are unrelated, based on the genotypes of the plurality of SNPs.

In some of such embodiments, the likelihood ratio (LR) is calculated as follows:
where D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated.

In some of such embodiments, LR is calculated as follows:
where 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

In some of any of such embodiments, the person of interest is biologically male, and the method further includes calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome includes comparing a set of SNPs, including one or more Y-SNPs, between the DNA profile and one or more reference DNA profiles.

In some of any of such embodiments, the one or more Y-SNPs are included within the plurality of SNPs. In some embodiments, the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some of such embodiments, the one or more Y-SNPs include 85 Y-SNPs.

In some of any of such embodiments, calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome, based on the genotypes of the one or more Y-SNPs.

In some of any such embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of the disaster or conflict. In some of any such embodiments, each of the DNA profiles in the reference set of DNA profiles is from a relative of a missing person or victim of the disaster or conflict.

In some of any of such embodiments, the reference set of DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In some of any of such embodiments, the reference set of DNA profiles includes up to 100 reference DNA profiles.

In some of any such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. In some of any such embodiments, at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.

In some of any of such embodiments, each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.

In some of any such embodiments, the identity of each relative of the person of interest in the reference set of DNA profiles is known. In some of such embodiments, the identity of each relative of the person of interest in the reference set of DNA profiles is known. In some of such embodiments, the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known.

In some of any such embodiments, the reference set of DNA profiles is in a database. In some embodiments, the database is not publicly accessible. In some of such embodiments, the database is not accessible by third-party genealogy services.

Also provided herein are nucleic acid libraries constructed using any of the methods described herein.

Also provided herein are a plurality of primers that specifically hybridize to a plurality of target sequences comprising at least 2,000 to 50,000 or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest, where amplification of the nucleic acid sample using the plurality of primers in one or more multiplex PCR reactions results in amplification products.

Also provided herein are a plurality of primers that specifically hybridize to a plurality of target sequences comprising at least 2,000 to 50,000 or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples, wherein the one or more reference samples include samples from relatives of the person of interest, and wherein amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the one or more reference samples using the plurality of primers in one or more multiplex PCR reactions results in amplification products.

In some of any of such embodiments, the nucleic acid sample from the person of interest comprises genomic DNA.

In some of any such embodiments, the nucleic acid sample from the person of interest comprises one or more enzyme inhibitors. In some of any such embodiments, the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. In some of any such embodiments, the nucleic acid sample from the person of interest comprises low-quality and/or low-abundance nucleic acid molecules. In some embodiments, the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA. In some of any of such embodiments, the low quality nucleic acid molecules are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or a Degradation Index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some of any of such embodiments, the low quality nucleic acid molecules have a DI of at least 1 and up to or below 158.3. In some of any of such embodiments, the nucleic acid sample from the person of interest and/or the nucleic acid sample from the one or more reference samples comprises high quality nucleic acid molecules. In some of any of such embodiments, high-quality nucleic acid molecules have a DI of less than 1.

In some of such embodiments, the person of interest is a missing person. In some of such embodiments, the person of interest is a victim of a disaster or conflict.

In some of any of such embodiments, the nucleic acid sample from the person of interest is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, or other bodily fluid, or containing hair or skin cells.

In some of any such embodiments, the nucleic acid sample from the person of interest comprises between 3 pg and 100 ng or about 3 pg and 100 ng of genomic DNA. In some of any such embodiments, the nucleic acid sample from the person of interest comprises between 100 pg and 5 ng or about 100 pg and 5 ng of genomic DNA, between 50 pg and 5 ng or about 50 pg and 5 ng of genomic DNA, or between 3 pg and 5 ng or about 3 pg and 5 ng of genomic DNA. In some of any such embodiments, the nucleic acid sample from the person of interest comprises 1 ng or about 1 ng of genomic DNA.

In some of such embodiments, the plurality of SNPs includes kinship SNPs (kiSNPs). In some of such embodiments, the plurality of SNPs includes Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs includes kiSNPs and Y-SNPs. In some of such embodiments, the plurality of SNPs includes kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs includes SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

In some of any of such embodiments, at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are, or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the SNPs are related SNPs. In some of any of such embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of the disaster or conflict.

In some of any such embodiments, each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict. In some of such embodiments, the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are each from first-, second-, third-, fourth-, or fifth-degree relatives. In some of any of such embodiments, at least 50% of the one or more reference samples are from relatives of the person of interest. In some of any such embodiments, each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. In some of any such embodiments, each relative of the person of interest in the one or more reference samples is a first-, second-, or third-degree relative of the person of interest, respectively. In some of any such embodiments, the identity of each relative of the person of interest in the one or more reference samples is known. In some of such embodiments, the identity of each of the one or more reference samples is known.

Also provided herein is a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; sequencing the amplified products; and genotyping the plurality of SNPs, thereby generating a DNA profile.

Also provided herein is a method for constructing a DNA profile, the method comprising: providing a nucleic acid sample from a person of interest; providing a nucleic acid sample from a relative of the person of interest; amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions; sequencing the amplification products; and genotyping the plurality of SNPs, thereby generating a DNA profile for the person of interest and the relative of the person of interest.

In some of any such embodiments, the sequencing does not include whole genome sequencing (WGS). In some of such embodiments, the nucleic acid sample comprises genomic DNA. In some of such embodiments, the nucleic acid sample of the person of interest and/or the nucleic acid sample of a relative of the person of interest comprises genomic DNA.

In some of any of such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises one or more enzyme inhibitors. In some embodiments, the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

In some of any of such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative, comprises low quality and/or low abundance nucleic acid molecules. In some embodiments, the low quality nucleic acid molecules are degraded and/or fragmented genomic DNA. In some of such embodiments, the low quality nucleic acid molecules comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or a Degradation Index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some of any of such embodiments, the low quality nucleic acid molecules have a DI of at least 1 and up to 158.3 or less. In some of such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises high quality nucleic acid molecules. In some embodiments, the high quality nucleic acid molecules have a DI of less than 1.

In some of such embodiments, the person of interest is a missing person. In some of such embodiments, the person of interest is a victim of a disaster or conflict.

In some of any such embodiments, the blood relatives of the person of interest are first-, second-, third-, fourth-, or fifth-degree relatives. In some of any such embodiments, the blood relatives of the person of interest are first-, second-, or third-degree relatives.

In some of any of such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative is derived from a buccal swab, paper, fabric or other substrate or object impregnated with saliva, blood or other bodily fluid, or containing hair or skin cells.

In some of any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises between 3 pg and 100 ng or between about 3 pg and 100 ng of genomic DNA. In some of any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises between 100 pg and 5 ng or between about 100 pg and 5 ng of genomic DNA, between 50 pg and 5 ng or between about 50 pg and 5 ng of genomic DNA, or between 3 pg and 5 ng or between about 3 pg and 5 ng of genomic DNA. In some of any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises 1 ng or about 1 ng of genomic DNA.

In some of such embodiments, the plurality of SNPs includes kinship SNPs. In some of such embodiments, the plurality of SNPs includes Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs includes kiSNPs and Y-SNPs. In some of such embodiments, the plurality of SNPs includes kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs). In some of such embodiments, the plurality of SNPs includes SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. In some of any of such embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are related SNPs.

In some of such embodiments, the sequencing comprises a sequencing plexity of up to 40-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of up to 32-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of 12-plex to 32-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of 24-plex to 32-plex. In some of such embodiments, the sequencing comprises a sequencing plexity of 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex or 45-plex, or about 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex including a sequencing complexity of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex or 45-plex. In some of any of such embodiments, the sequencing is at or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In some embodiments, the sequencing comprises a sequencing plex of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In some embodiments, the sequencing comprises a sequencing plex of 8-16-plex or about 8-16-plex for post-mortem samples, and/or the sequencing comprises a sequencing plex of 24-40-plex or about 24-40-plex for ante-mortem samples. In some embodiments, the sequencing comprises a sequencing plex of 12-plex or about 12-plex for post-mortem samples, and/or the sequencing comprises a sequencing plex of 32-plex or about 32-plex for ante-mortem samples. In some of any of such embodiments, the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex, or about 30-plex, 31-plex, or 32-plex.

Also provided herein is a method for identifying genetic relatives of a DNA profile, the method comprising: calculating a degree of relatedness between a DNA profile according to any one of claims 127 to 161 and one or more reference DNA profiles, the one or more reference DNA profiles being included within a reference set of DNA profiles comprising one or more reference DNA profiles from relatives of the person of interest; and generating a pedigree comprising the DNA profile in relation to the one or more reference DNA profiles.

In some embodiments, one or more reference DNA profiles are part of a database.

In some of any of such embodiments, the reference set of DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. In some of any such embodiments, at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. In some of any such embodiments, each relative of the person of interest is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.

In some of such embodiments, the identity of each relative of the person of interest in the reference set of DNA profiles is known. In some of such embodiments, the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known.

In some of any such embodiments, the reference set of DNA profiles is in a database. In some embodiments, the database is not publicly accessible. In some of such embodiments, the database is not accessible by third-party genealogy services.

Also provided herein is a method for confirming the identity of a DNA profile, the method comprising: calculating a degree of relatedness between a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs and one or more reference DNA profiles, where the DNA profile is from a person of interest and the one or more reference DNA profiles are included within a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest; and generating a pedigree that includes the DNA profile in relation to the one or more reference DNA profiles.

In some embodiments, the DNA profile is generated by any of the methods for generating a DNA profile described herein.

In some of any such embodiments, the relatedness is calculated using a kinship model. In some of such embodiments, the relatedness is calculated using a kinship model that is trained using a PCA method. In some embodiments, the PCA method for training the kinship model is or includes PCA. In some of such embodiments, the PCA method is PC-AiR. In some of such embodiments, PC-AiR includes: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally in a training DNA profile, where pairings with a relatedness coefficient >0.025 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating this set as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to the samples in the unrelated sample set, thereby designating this set as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i).

In some of such embodiments, the PCA method is a modified PC-Air. In some embodiments, the modified PC-Air includes: (1) estimating relatedness coefficients between all pairs of samples, optionally training DNA profiles, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).

In some of such embodiments, calculating the degree of relatedness includes calculating the coefficient of relatedness using PC-Relate. In some embodiments, the degree of relatedness is calculated by providing a DNA profile of the person of interest as input to PC-Relate. In some of such embodiments, the degree of relatedness is calculated by providing a kinship model and a DNA profile of the person of interest as input to PC-Relate.

In some of any of such embodiments, one or more reference DNA profiles are further provided as input to PC-Relate.

In some of any of such embodiments, calculating the degree of relatedness includes calculating the coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively.

In some of any of such embodiments, calculating the degree of association includes calculating a likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs, including related SNPs, from among the plurality of SNPs between the DNA profile and one or more reference DNA profiles.

In some of any of such embodiments, calculating the likelihood ratio includes dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles are related by the probability that the DNA profile and the reference DNA profile are unrelated, based on the genotypes of the plurality of SNPs.

In some of such embodiments, the likelihood ratio (LR) is calculated as follows:
where D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated. In some of such embodiments, LR is calculated as follows:
where 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

In some of any such embodiments, the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs comprising one or more Y-SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, the one or more Y-SNPs are included within a plurality of SNPs. In some of any such embodiments, the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some of any such embodiments, the one or more Y-SNPs include 85 Y-SNPs.

In some of any of such embodiments, calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome, based on the genotypes of the one or more Y-SNPs, by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome.

In some of any of such embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of the disaster or conflict.

In some of any such embodiments, each of the one or more reference samples is from a relative of a missing person or a victim of the disaster or conflict. In some of such embodiments, the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA samples.

In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest. In some of any of such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles, respectively, are first-, second-, third-, fourth-, or fifth-degree relatives. In some of any such embodiments, at least 50% of the reference DNA profiles in the one or more reference samples are from relatives of the person of interest. In some of any such embodiments, each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. In some of any such embodiments, each relative of the person of interest in the one or more reference samples is a first-, second-, or third-degree relative of the person of interest, respectively.

In some of any such embodiments, the identity of each relative of the person of interest in one or more reference samples is known. In some of any such embodiments, the identity of each of the one or more reference samples is known.

Also provided herein are kits comprising at least one container means, wherein the at least one container means comprises any of a plurality of primers described herein. In some of any of such embodiments, the plurality of SNPs is selected from the group consisting of between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, and between 8,000 and 13,000 SNPs. SNPs between 7,000 and 11,000, SNPs between 8,000 and 15,000, SNPs between 8,000 and 14,000, SNPs between 8,000 and 13,000, SNPs between 8,000 and 12,000, SNPs between 8,000 and 11,000, SNPs between 9,000 and 15,000, SNPs between 9,000 and 14,000, SNPs between 9,000 and 13,000, SNPs between 9,000 and 12,000, or SNPs between 9,000 and 11, 000 SNPs, or between about 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, 7,000 In some of such embodiments, the plurality of SNPs comprises between 10,230 SNPs. In some of any of such embodiments, the plurality of SNPs is between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, SNPs between 7,000 and 11,000, SNPs between 8,000 and 15,000, SNPs between 8,000 and 14,000, SNPs between 8,000 and 13,000, SNPs between 8,000 and 12,000, SNPs between 8,000 and 11,000, SNPs between 9,000 and 15,000, SNPs between 9,000 and 14,000, SNPs between 9,000 and 13,000, SNPs between 9,000 and 12,000, or SNPs between 9,000 and 11, 000 SNPs, or between about 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, 7,000 The plurality of SNPs may include between 10,230 SNPs and 11,000 SNPs, between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs. In some of any of such embodiments, the plurality of SNPs includes 10,230 SNPs.

In some of any of such embodiments, the method further includes generating a pedigree tree that includes the DNA profile in relation to one or more DNA profiles included in the reference set of DNA profiles. In some embodiments, the pedigree tree includes the DNA profile in relation to one or more DNA profiles from blood relatives of the person of interest.

FIG. 1 shows an exemplary schematic of a method for generating a sequenceable library.

Figure 2 shows the results of the number of loci identified using various input dosage adjustments of genomic DNA including 5 ng, 2.5 ng, 1 ng, 500 pg, 250 pg, 100 pg and 50 pg.

FIG. 3 shows the percentage of loci (call rate) detected in degraded DNA using the assay described herein compared to the microarray (GSA) call rate.

FIG. 4 shows the number of loci detected in the presence of the inhibitors hematin, humic acid, indigo, and tannic acid compared to the reference control.

FIG. 5 shows an exemplary pedigree generated by the methods described herein.

FIG. 6 shows the expected and observed coefficients of kinship calculated using the algorithm described herein.

FIG. 7 shows the results of the 1:many search algorithm in an exemplary case study.

FIG. 8 shows an exemplary pedigree generated from the results of the 1:many search algorithm.

FIG. 9 is a table summarizing the number and types of loci detected using various input dosage adjustments of genomic DNA, including 5 ng, 2.5 ng, 1 ng, 500 pg, 250 pg, 100 pg, and 50 pg.

FIG. 10 is a table summarizing the number and types of loci detected using DNA in the presence of the inhibitors hematin, humic acid, tannic acid, and indigo compared to the positive amplification control in the absence of inhibitors.

Figure 11 is a table summarizing the number and types of loci detected for two samples of DNA obtained 9 and 22 hours after simulated sexual assault. DNA was isolated from the sperm fraction by differential extraction, with 500 pg of DNA input.

FIG. 12 shows the number of loci detected in saliva samples with increasing phenol (a known inhibitor of PCR amplification) content from the phenol-chloroform-isoamyl alcohol (PCIA) extraction method.

FIG. 13 shows the number of loci detected in blood samples isolated from different substrates or methods commonly performed in forensic laboratories, including blood with rust, blood on denim, blood on a swab, and blood with varying levels of heme (a known PCR amplification inhibitor) carryover from Chelex™ extraction.

FIG. 14 shows an exemplary schematic diagram of a method for assessing kinship for an individual of interest, e.g., a missing person or a victim of a conflict or disaster, comprising analyzing kinship using DNA profiles, e.g., SNP reports, uploaded to a local server containing at least one DNA profile from a kin.

FIG. 15 shows the total number of SNPs detected for each individual sample in an exemplary set of true post-mortem samples.

FIG. 16 shows the total number of SNPs detected for individual samples within an exemplary related mock postmortem sample set, which includes samples artificially degraded by boiling or are low-input samples with a DI of 0 and less than 1 ng of input DNA.

Figure 17 shows the total number of SNPs detected for individual samples in an exemplary related premortem private family sample set including samples from CEPH/Utah involving up to second-degree relationships validated with Coriell, as well as three unrelated samples.

Figures 18A-18E show receiver operating characteristic (ROC) curves of the results for 2,000 SNPs (Figure 18A), 4,000 SNPs (Figure 18B), 6,000 SNPs (Figure 18C), 8,000 SNPs (Figure 18D), and 10,000 SNPs (Figure 18E) of first-, second-, and third-degree relatives. Figures 18A-18E show receiver operating characteristic (ROC) curves of the results for 2,000 SNPs (Figure 18A), 4,000 SNPs (Figure 18B), 6,000 SNPs (Figure 18C), 8,000 SNPs (Figure 18D), and 10,000 SNPs (Figure 18E) of first-, second-, and third-degree relatives.

19A-19E show the resulting ROC curves for 2,000 SNPs (FIG. 19A), 4,000 SNPs (FIG. 19B), 6,000 SNPs (FIG. 19C), 8,000 SNPs (FIG. 19D), and 10,000 SNPs (FIG. 19E) of fourth- or fifth-degree relatives. Figures 19A-19E show the resulting ROC curves for 2,000 SNPs (Figure 19A), 4,000 SNPs (Figure 19B), 6,000 SNPs (Figure 19C), 8,000 SNPs (Figure 19D), and 10,000 SNPs (Figure 19E) of fourth- or fifth-degree relatives.

FIG. 20A shows the distribution of the number of SNPs typed in downsampled datasets of libraries sequenced at 16-plex and 30-plex. Figures 20B and 20C show the number of SNPs typed in sequenced library samples for simulated ante-mortem (AM) samples (Figure 20B), where libraries were generated using 1 ng of intact DNA, with 3 (3-plex), 12 (12-plex), 16 (16-plex), 24 (24-plex), and 32 (32-plex) sample runs, and for simulated post-mortem (PM) samples (Figure 20C), where libraries were generated from degraded DNA samples from cremated, buried, and incinerated bone, dental debris, whole blood, and low-input DNA samples of 50, 100, 250, and 500 pg input DNA, with 3 (3-plex) and 12 (12-plex) sample runs.

21A and 21B show allele concordance and heterozygosity of simulated antemortem (mock AM) (FIG. 21A) and simulated postmortem (mock PM) (FIG. 21B) samples from modern teeth, blood, buried bone, modern bone, or low-input DNA. 21A and 21B show allele concordance and heterozygosity of simulated antemortem (mock AM) (FIG. 21A) and simulated postmortem (mock PM) (FIG. 21B) samples from modern teeth, blood, buried bone, modern bone, or low-input DNA.

22A-22D show graphical representations of the sensitivity and specificity of the overall relatedness coefficients for de-identified GEDMatch samples for first-, second-, third-, fourth-, and fifth-degree relationships, with the number of classified SNPs being 2,000 (FIG. 22A), 4,000 (FIG. 22B), 6,000 (FIG. 23C), or 8,000 (FIG. 22D). 22A-22D show graphical representations of the sensitivity and specificity of the overall relatedness coefficients for de-identified GEDMatch samples for first-, second-, third-, fourth-, and fifth-degree relationships, with the number of classified SNPs being 2,000 (FIG. 22A), 4,000 (FIG. 22B), 6,000 (FIG. 23C), or 8,000 (FIG. 22D). 22A-22D show graphical representations of the sensitivity and specificity of the overall relatedness coefficients for de-identified GEDMatch samples for first-, second-, third-, fourth-, and fifth-degree relationships, with the number of classified SNPs being 2,000 (FIG. 22A), 4,000 (FIG. 22B), 6,000 (FIG. 23C), or 8,000 (FIG. 22D). 22A-22D show graphical representations of the sensitivity and specificity of the overall relatedness coefficients for de-identified GEDMatch samples for first-, second-, third-, fourth-, and fifth-degree relationships, with the number of classified SNPs being 2,000 (FIG. 22A), 4,000 (FIG. 22B), 6,000 (FIG. 23C), or 8,000 (FIG. 22D).

Figure 23 shows the pedigree of the Utah/CEPH 1463 family, consisting of grandparents, parents, and siblings, thereby representing first- and second-degree relationships. Samples were sequenced in 12-, 16-, 24-, and 32-sample pooled libraries.

FIG. 24 shows the distribution of the number of typed SNPs across samples within the Utah/CEPH 1463 family when sequenced with four different numbers of samples per run: 12 samples per run (12-plex), 16 samples per run (16-plex), 24 samples per run (24-plex), and 32 samples per run (32-plex).

Figures 25A and 25B show the distribution of relatedness coefficients (Figure 25A) and log base 10 likelihood ratios (LogLR) (Figure 25B) for all pairwise combinations taken from the Utah/CEPH 1463 family and 100 randomly selected samples from the 1000 Genomes Project, which represented unrelated controls. Samples include samples from grandparents (G), parents (P), siblings (S), unrelated controls (U), unrelated grandparents (GU), unrelated parents (PU), and unrelated siblings (SU).

FIG. 26 depicts a family tree of informal relatives (RF) consisting of parents, aunts/aunts, cousins, children of cousins (1C1R), and second cousins.

Figure 27A shows the distribution of the number of SNPs classified for private relatives (RF) carrying first-, second-, third-, fourth-, and fifth-degree relationships using 12-plex degraded/low-input DNA samples and 30-plex intact samples. Figure 27B shows the kinship coefficients for pairs of individuals from private relatives (RF), with the corresponding log-likelihood ratios shown above each bar.

DETAILED DESCRIPTION The practice of the techniques described herein may employ, unless otherwise indicated, conventional techniques and descriptions of molecular biology, cell biology, biochemistry, and sequencing techniques, which are within the skill of those in the art. Specific illustrations of suitable techniques can be had by reference to the examples herein.

All publications, including patent documents, scientific articles, and databases, referred to in this application are incorporated by reference in their entirety for all purposes to the same extent as if each individual publication was individually incorporated by reference. To the extent that a definition set forth herein contradicts or otherwise conflicts with a definition set forth in a patent, application, published application, or other publication incorporated herein by reference, the definition set forth herein shall take precedence over the definition incorporated herein by reference.

The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
overview

Samples from missing persons or victims of disasters or conflicts can be highly degraded and may not be suitable for whole genome sequencing (WGS), microarray, or short tandem repeat (STR) analysis. While mitochondrial analysis is sensitive and may be suitable in certain situations, it only considers maternal inheritance, thereby limiting its use in kinship analysis. Furthermore, current methods for generating DNA profiles for comparison in genetic databases, including genotyping using high-density SNP microarrays and WGS followed by linking of evidentiary samples to distant relatives in databases, require large amounts of high-quality DNA samples and are not designed for use in family searches or for identifying missing persons or victims of disasters or conflicts. These samples may be small and of low quality, for example, containing degraded DNA, and data from current methods require extensive imputation to generate results that can be uploaded to search databases. Finally, relatives of missing persons or victims of disasters or conflicts often do not want their genetic data uploaded to public databases. The new and improved methods provided herein overcome these limitations by enabling the use of small amounts and low-quality, e.g., degraded, DNA for the generation of nucleic acid profiles, without the need to upload genetic data to publicly accessible genetic databases, for more efficient genetic analysis than alternative approaches such as WGS or SNP microarrays. Furthermore, the new and improved methods provided herein also include improved methods for performing kinship analysis that require fewer computations to calculate accurate kinship.

Identification of victims of fatal accidents is necessary for both humanitarian and legal reasons: when deaths are not accidental, as in civil and criminal cases, it provides families with a sense of resolution and legitimacy in their search for missing family members. Identification of victims of mass fatality incidents (MFIs) can be difficult given the large number of victims and the impact of the disaster on the victims' physical integrity. MFIs can be caused by accidental events such as disease/famine, earthquakes/tsunamis/hurricanes, plane/train/car crashes, or fires, or by human intent such as war, terrorist attacks, or human rights violations/genocide. Recent events such as the September 11, 2001, terrorist attacks at the World Trade Center in New York City or the December 26, 2004, Boxing Day tsunami caused by an earthquake off the west coast of northern Sumatra, caused unimaginable loss of life and demonstrated the need for efficient procedures and methods for recovering and cataloging the remains of victims, preserving information about the remains, and identifying them.

The most common methods for identification (called disaster victim identification, or DVI) are fingerprinting, dental comparison (dental examination or radiology), item breakdown, autopsy examination for evidence of surgical scars/procedures and tattoos, and DNA analysis. Traditional methods such as fingerprinting and dental comparison are often utilized as a first line of defense due to the low labor and cost required and speed of the procedure; however, these methods require antemortem records of fingerprints and dental images for identification. Items can be misleading, as victims may have similar jewelry or other personal items. Comparison of surgical procedures and tattoos also requires antemortem medical records or documentation of tattoos or other alterations. Finally, these methods require the victim's remains to be relatively intact. Some MFIs can result in fragmentation and commingling of remains. In cases of fragmentation, DNA analysis of postmortem (PM) samples can not only aid in the identification of missing persons but also help assign multiple remains or body parts to specific individuals. DNA analysis requires an ante-mortem (AM) sample, such as a razor, shaver, toothbrush, or hairbrush, from the missing person for comparison. If an AM sample from the missing person is unavailable, samples donated by close family members can assist in identification. DNA analysis takes longer than traditional methods and has specific requirements for laboratory cleanliness and chain of custody tracking for the samples analyzed, which can be difficult in field situations.

When traditional methods fail or the remains are not intact, the success of DNA analysis depends on sample collection, the timing of that collection, storage conditions, and the quantity of sample obtained (as is the case in cases of deterioration and decay). DNA identification relies on short tandem repeats (STRs), non-coding DNA markers used in forensic genomics, including the set of 20 autosomal core loci included in the Combined DNA Index System (CODIS), mitochondrial DNA for maternal lineages, or STRs on the Y chromosome (Y-STRs) for paternal lineages. Autosomal markers are preferred in large-scale disasters due to the complexity that multiple family members sharing either the same maternal or paternal lineage may be missing.

STR analysis has been used successfully for many years for identification in criminal, missing person, and paternity cases. The success of this type of analysis is a result of the highly polymorphic nature of these markers and the number of markers that can be multiplexed together for a single analysis. These markers have also been successfully utilized for DVI. In MFI, software solutions are useful for supporting multiple pairwise comparisons of profiles from PM (victim) and AM (autologous or related) samples and statistical calculations of the degree of relatedness. Several software packages are available for the analysis of autosomal and Y-STR and mitochondrial DNA data. Not all samples from MFI are suitable for STR analysis. Highly degraded DNA samples do not amplify larger STR markers in commonly used capillary electrophoresis-based (CE) kits, limiting the number of markers that can be typed to perform identification. The development of CE kits utilizing smaller amplicons has assisted CE analysis of degraded DNA (Butler, 2014). Next-generation sequencing (NGS or massively parallel sequencing) STR assays allow for smaller amplicon sizes and allow for the analysis of more markers within a single assay, both of which improve the recovery of information from degraded DNA samples.

Utilizing STR data is particularly appropriate when AM DNA samples are available from the missing person or very close family members, such as first-degree relatives (parents, children, or siblings). Due to the number of false-positive identifications that can occur in unrelated individuals, STR analysis is less successful when only more distant family members are available for comparison. This is particularly true when the MFI occurred many years ago and very close family members from the missing person have died. When DNA from more distant relatives (second- and third-degree relatives, such as nieces, nephews, grandchildren, and great-grandchildren) is available for comparison, utilizing more markers, such as single nucleotide polymorphisms (SNPs), can aid in identification.

The method described herein was developed to interrogate 10,230 forensically relevant SNPs for purposes such as solving cold cases, including identifying missing persons. Previous approaches involved analyzing amplified DNA using NGS for up to three samples per sequencing run. Such approaches were initially designed to classify enough SNPs to detect relationships up to the fifth degree when searching DNA microarray databases, such as GEDmatch PRO, to assist law enforcement in solving cold cases. To facilitate DVI, which requires a cost-effective, high-throughput solution, a new and improved method was developed that can sequence amplified DNA at a higher plexity to classify enough SNPs with significant overlap to determine relationships to the third degree. As shown in Example 13, simulated PM samples sequenced at a multiplicity of 12 samples per run and simulated AM samples sequenced at a multiplicity of 32 samples per run generated enough classified locus data to confirm relationships up to the third degree without false positive identifications using the method and kinship algorithm described herein.

The goals of this kinship algorithm were threefold: to confirm relationships in the absence of genealogical or relationship information, which can be important when attempting to assign multiple remains to a single individual or when the victim's relatives are unavailable or unknown; to reduce the false positive rate in confirming relationships, which is often high when calculating likelihood ratios using STR (Alonso et al., Croat Med J 2005, 46, 540-548, the contents of which are incorporated herein by reference in their entirety); and to reduce the false positive rate when confirming relationships, which is often high when calculating likelihood ratios using programming languages such as R, local builds of likelihood ratio software such as Familias (Kling et al., Forensic Sci Int Genet 2014, 13, 121-127, doi:10.1016/j.fsigen.2014.07.004, the contents of which are incorporated herein by reference in their entirety), or Bonaparte's personal account (Slooten Maintaining the privacy of MFI victims (only up to two degrees of kinship can be confirmed) requires knowledge of the law enforcement database (see, for example, [David] E. et al., Forensic Science International: Genetics 2011, 5, 308-315, doi:10.1016/j.fsigen.2010.06.005, the contents of which are incorporated herein by reference in their entirety). Because MFI victims and their families often require privacy in the identification of remains, maintaining genotype information on a private server is necessary. Thus, in some embodiments, the kinship algorithms described herein are localized on a private server to confirm relationships between samples prepared as described herein. The local software did not upload results to a law enforcement database; rather, it maintained the results for review on the private server. Furthermore, in some embodiments, the kinship algorithms described herein can efficiently confirm relationships with perfect sensitivity and specificity, including up to three degrees of kinship, for degraded/low-input mock PM samples sequenced at a plexity of 12 and mock reference or AM samples sequenced at a plexity of 32.

Accordingly, disclosed herein is a method of identifying a person of interest by performing a DNA-based kinship analysis using a DNA profile from the person of interest, e.g., a missing person or a victim of a disaster or conflict, to determine the degree of relatedness between the DNA profile and one or more reference DNA profiles comprising known relatives of the person of interest, thereby identifying the person of interest.

Provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; generating a nucleic acid library from the amplified products; sequencing the nucleic acid library generated from the amplified products; analyzing the sequences of the amplified products; genotyping the plurality of SNPs, thereby generating a DNA profile; and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

Also provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; generating a nucleic acid library from the amplified products; sequencing the nucleic acid library generated from the amplified products; genotyping the plurality of SNPs, thereby generating a DNA profile; and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

Also provided herein is a method for constructing a nucleic acid library of a person of interest, comprising: providing a nucleic acid sample from the person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising the amplified products, wherein the amplification is performed in one or more multiplex PCR reactions.

Also provided herein are methods for constructing a nucleic acid library for a reference DNA sample, the method comprising: providing nucleic acid samples from relatives of a person of interest; amplifying the nucleic acid samples with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000-50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising the amplified products; wherein the amplification is performed in one or more multiplex PCR reactions. In some embodiments, the relatives are first-, second-, third-, fourth-, or fifth-degree relatives of the person of interest. In some embodiments, the relatives are first-, second-, or third-degree relatives of the person of interest.

Also provided herein is a method for calculating degree of relatedness, comprising obtaining a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs, where the DNA profile is from a person of interest, and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

Also provided herein is a method for calculating degree of relatedness, comprising generating a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs, where the DNA profile is from a person of interest, and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

Also provided herein are nucleic acid libraries constructed using any of the methods described herein, for example, any of the methods for constructing a nucleic acid library described herein.

Also provided herein are a plurality of primers that specifically hybridize to a plurality of target sequences comprising at least 2,000 to 50,000 or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest, where amplification of the nucleic acid sample using the plurality of primers in one or more multiplex PCR reactions results in amplification products.

Also provided herein are a plurality of primers that specifically hybridize to a plurality of target sequences comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples, wherein the one or more reference samples include samples from blood relatives of the person of interest, and wherein amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the one or more reference samples using the plurality of primers in one or more multiplex PCR reactions results in amplification products.

Also provided herein is a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplified products, wherein the amplification is performed in one or more multiplex PCR reactions; sequencing the amplified products; and genotyping the plurality of SNPs, thereby generating a DNA profile.

Also provided herein is a method for constructing a DNA profile, the method comprising: providing a nucleic acid sample from a person of interest; providing a nucleic acid sample from a relative of the person of interest; amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions; sequencing the amplification products; and genotyping the plurality of SNPs, thereby generating a DNA profile for the person of interest and the relative of the person of interest.

Also provided herein are DNA profiles constructed using any of the methods described herein, for example, any of the methods for constructing a DNA profile described herein.

Also provided herein is a method for identifying genetic relatives of a DNA profile, the method comprising: calculating a degree of relatedness between any of the DNA profiles described herein and one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest; and generating a pedigree tree that includes the DNA profile in relation to the one or more reference DNA profiles.

Also provided herein is a method for confirming the identity of a DNA profile, the method comprising: calculating a degree of relatedness between a DNA profile comprising genotypes for at least or between about 2,000 and 50,000 SNPs and one or more reference DNA profiles, where the DNA profile is from a person of interest and the one or more reference DNA profiles are included within a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest; and generating a pedigree that includes the DNA profile in relation to the one or more reference DNA profiles.

Also provided herein are kits comprising at least one container means, the at least one container means containing any of the plurality of primers described herein.

In some of any such embodiments, the relatedness is calculated using a kinship model. In some of such embodiments, the relatedness is calculated using a kinship model that is trained using a PCA method. In some of such embodiments, the PCA method for training the kinship model is or includes PCA.

In some of such embodiments, the PCA method is PC-AiR. In some embodiments, PC-AiR includes the steps of: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally, in a training DNA profile, where pairings with a relatedness coefficient >0.025 are confirmed as closely related and pairings with a relatedness coefficient <-0.025 are confirmed as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating this set as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to the samples in the unrelated sample set, thereby designating this set as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i).

In some of such embodiments, the PCA method is a modified PC-Air. In some embodiments, the modified PC-Air includes: (1) estimating relatedness coefficients between all pairs of samples, optionally training DNA profiles, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).

In some of such embodiments, calculating the degree of relatedness includes calculating a coefficient of relatedness using PC-Relate. In some embodiments, the degree of relatedness is calculated by providing a DNA profile of the person of interest as input to PC-Relate. In some of such embodiments, the degree of relatedness is calculated by providing a kinship model and a DNA profile of the person of interest as input to PC-Relate. In some of such embodiments, calculating the degree of relatedness includes calculating a likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs, including kinship SNPs, from among the plurality of SNPs between the DNA profile and the one or more reference DNA profiles. In some embodiments, the person of interest is biologically male, and the method further includes calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs comprising one or more Y-SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, the one or more Y-SNPs are included within the plurality of SNPs. In some embodiments, the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs include 85 Y-SNPs. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and the reference DNA profile from among the one or more reference DNA profiles share a Y chromosome, based on the genotypes of the one or more Y-SNPs, by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome.

In some embodiments, calculating the likelihood ratio includes dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles are related by the probability that the DNA profile and the reference DNA profile are unrelated, based on the genotypes of the plurality of SNPs.

In some of any such embodiments, calculating the likelihood of sharing chromosome Y includes calculating the log-likelihood by providing a DNA profile of the person of interest and a match profile as input to identify matching chromosomes.
Samples and sample processing

In some embodiments, the samples disclosed herein can be or include any suitable biological sample, or a sample derived therefrom. In some embodiments, the samples described herein are processed and amplified using any known suitable method to complement the methods described herein. Exemplary samples, sample processing methods, and sample amplification methods are described below.
A. Nucleic Acid Samples

The nucleic acid sample disclosed herein can be derived from any biological sample, for example, any biological sample from a person of interest. The biological sample can be derived from blood, buccal swabs, hair, teeth, bone, skin, tissue, and/or semen, or any other source for obtaining DNA from the person of interest. In some embodiments, the nucleic acid sample is derived from a biological sample that is or contains blood, hair, teeth, bone, semen, skin, or sperm. In some embodiments, the nucleic acid sample is derived from a tissue sample. In some embodiments, the biological sample is a DNA sample. In some embodiments, the nucleic acid sample comprises DNA. In some embodiments, the DNA is genomic DNA (gDNA). In some embodiments, the nucleic acid sample from the person of interest comprises genomic DNA, and/or a reference DNA sample, for example, a nucleic acid sample from a relative of the person of interest, comprises genomic DNA. In some embodiments, the nucleic acid sample of the person of interest and/or the nucleic acid sample of a relative of the person of interest comprises genomic DNA. The DNA from which the nucleic acid sample can be derived can be intact or partially degraded. The DNA from which the nucleic acid sample is obtained may be damaged, degraded, or inhibited due to, but not limited to, degradation of raw materials, variations in extraction, storage procedures, or environmental exposure. In some embodiments, the DNA is damaged due to calcium inhibition, cremation, incineration, and embalming. In some embodiments, the methods described herein include providing a nucleic acid sample from a person of interest.

In some embodiments, the DNA from which the nucleic acid sample can be obtained is a low-quality and/or low-quality DNA sample. In some embodiments, the DNA from which the nucleic acid sample can be obtained is a low-quality and low-quality DNA sample. In some embodiments, the low-quality DNA sample comprises low-quality nucleic acid molecules. In some embodiments, the low-quality nucleic acid molecules are degraded DNA, e.g., genomic DNA, and/or fragmented DNA, e.g., genomic DNA.

The quality of a nucleic acid, e.g., DNA, sample can be determined by calculating the degradation index (DI). DI is calculated by dividing the concentration of small DNA targets by the concentration of large DNA targets (DI = concentration of small DNA targets / concentration of large DNA targets). In general, a DI value of less than 1 typically indicates that the nucleic acid, e.g., DNA, is not degraded, is not a low-quality sample, and/or is a high-quality sample; a DI value of 1-10 typically indicates that the nucleic acid, e.g., DNA, has a low to moderate amount of degradation; and a DI value of greater than 10 typically indicates that the nucleic acid, e.g., DNA, is highly degraded.

In some embodiments, low quality nucleic acid molecules have a D of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or more. or has a DI of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more. In some embodiments, the low quality nucleic acid molecules are at least 2 and 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or or greater, or less than 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or more. In some embodiments, low quality nucleic acid molecules have a DI of 2 or more, or at least 2 or more. In some embodiments, low quality nucleic acid molecules have a DI of 5 or more, or at least 5 or more. In some embodiments, low quality nucleic acid molecules have a DI of 10 or more, or at least 10 or more. In some embodiments, low quality nucleic acid molecules have a DI of 20 or more, or at least 20 or more. In some embodiments, low quality nucleic acid molecules have a DI between 1 and 200. In some embodiments, low quality nucleic acid molecules have a DI between 1 and 175. In some embodiments, low quality nucleic acid molecules have a DI of at least 1 and 158.3 or less than 158.3. In some embodiments, low quality nucleic acid molecules have a DI between 2 and 200. In some embodiments, low quality nucleic acid molecules have a DI between 2 and 175. In some embodiments, low quality nucleic acid molecules have a DI of at least 2 and 158.3 or less than 158.3. In some embodiments, low quality nucleic acid molecules have a DI between 5 and 200. In some embodiments, low quality nucleic acid molecules have a DI between 5 and 175. In some embodiments, low quality nucleic acid molecules have a DI of at least 5 and 158.3 or less than 158.3. In some embodiments, low quality nucleic acid molecules have a DI between 10 and 200. In some embodiments, low quality nucleic acid molecules have a DI between 10 and 175. In some embodiments, low quality nucleic acid molecules have a DI of at least 10 and 158.3 or less than 158.3.

In some embodiments, low-quality nucleic acid molecules have a DI of between or about 1-10, between or about 1-50, between or about 1-50, between or about 1-100, between or about 1-100, between or about 1-200, between or about 2-10, between or about 2-10, between or about 2-50, between or about 2-50, between or about 2-100, between or about 2-100, between or about 200, between or about 5-10, between or about 5-10, between or about 5-50, between or about 5-50, between or about 5-100, between or about 5-100, between or about 5-200.

In some embodiments, the DNA from which the nucleic acid sample is obtained is a high-quality nucleic acid sample. In some embodiments, a high-quality nucleic acid sample has a DI of less than 1.

In some embodiments, the nucleic acid sample comprises one or more enzyme inhibitors. In some embodiments, the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, humic acid (e.g., heme), humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. In some embodiments, the one or more enzyme inhibitors comprise heme.

In some embodiments, the nucleic acid sample is from a person of interest, such as a missing person or a victim of a disaster or conflict. In some embodiments, the person of interest is a missing person. A missing person may be missing for any reason and may be voluntarily or involuntarily missing. For example, in some embodiments, the missing person is involuntarily missing and has been kidnapped or abducted. In some embodiments, the missing person is voluntarily missing and has run away, evaded detection, or is otherwise in hiding.

In some embodiments, the nucleic acid sample is from a reference DNA sample. In some embodiments, the reference DNA sample is from a blood relative of the person of interest. Thus, in some embodiments, the nucleic acid sample is from a blood relative of the person of interest, such as a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest. In some embodiments, one or more of the one or more reference DNA profiles are derived from a reference DNA sample, e.g., a reference DNA sample from a blood relative of the person of interest.

In some embodiments, the person of interest is a victim of a disaster or conflict. The victim of a disaster or conflict can be a victim of any type of disaster or conflict. For example, in some embodiments, the victim of a disaster or conflict is a victim of a disaster such as a hurricane, tornado, storm, fire including wildfire/forest fire, tsunami, earthquake, flood, volcanic eruption, avalanche, etc. In some embodiments, the disaster is a natural disaster. As used herein, "natural disaster" refers to any disaster resulting from the Earth's natural processes, such as those associated with meteorological and/or geological events, e.g., hurricanes, floods, storms, tsunamis, earthquakes, volcanic eruptions, etc. In some embodiments, the disaster is a non-natural disaster. As used herein, "non-natural disaster" refers to any disaster other than a natural disaster, including those caused by human influence, including disasters involving motor vehicles, airplanes, ships, and trains, disasters involving the collapse of buildings, roads, mines, and bridges, and disasters involving the burning of buildings, among other disasters caused by human influence. In some embodiments, the disaster or conflict victim is a victim of a conflict, such as a war or other conflict between groups of people. As used herein, "conflict" refers to any conflict between different countries or states, or between different groups within a country or state, e.g., a military conflict, e.g., war, or a terrorist attack, or any other conflict between groups that results in the death and/or injury of persons.

In some embodiments, the person of interest is biologically female. In some embodiments, the person of interest is biologically male.

In some embodiments, the nucleic acid sample is derived from a buccal swab, paper, fabric, e.g., denim, or other substrate or object impregnated with saliva, blood, sperm, or other bodily fluids, or containing hair or skin cells. In some embodiments, the object impregnated with saliva, blood, sperm, or other bodily fluids, or containing hair or skin cells, is a personal object, such as a toothbrush or hairbrush. In some embodiments, the nucleic acid sample is derived from an object containing hair or skin cells, e.g., a hairbrush or toothbrush. In some embodiments, the nucleic acid sample is derived from a personal object, e.g., a toothbrush or hairbrush. In some embodiments, the nucleic acid sample is derived from a toothbrush or hairbrush. In some embodiments, the personal object is an object used by and/or associated with the person from whom the nucleic acid sample is derived, such that the person's nucleic acids are present on or within the object.

In some embodiments, the nucleic acid sample is from a crime scene, e.g., a murder, an assault, e.g., a sexual assault, or a burglary, or any other crime where participant identification is required. In some embodiments, the nucleic acid sample is from a sexual assault.

In some embodiments, the nucleic acid sample is obtained at or about 30 minutes, at or about 1 hour, or at or about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours or more after the sample containing the nucleic acid sample is deposited by its source, e.g., a human subject. In some embodiments, the nucleic acid sample is purified within about 3 hours, 9 hours, 12 hours, 15 hours, 18 hours, 21 hours, 22 hours, 24 hours, 36 hours, 48 hours, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year after the sample containing the nucleic acid sample is deposited by its source, e.g., a human subject. , 2, 3, or 4 years or more, or less than about 3 hours, 9 hours, 12 hours, 15 hours, 18 hours, 21 hours, 22 hours, 24 hours, 36 hours, 48 hours, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, or 4 years or more. In some embodiments, the nucleic acid sample is obtained 24 hours or less, e.g., 22 hours or less, after the sample containing the nucleic acid sample is deposited by its source, e.g., a human subject.

In some embodiments, the nucleic acid sample comprises 3 pg to 100 ng or about 3 pg to 100 ng of DNA, e.g., genomic DNA, or 50 pg to 100 ng or about 50 pg to 100 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample comprises 100 pg to 5 ng or about 100 pg to 5 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample comprises 1 ng or about 1 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample comprises between 3 pg to 100 ng or about 3 pg to 100 ng of DNA, e.g., genomic DNA.

In some embodiments, the nucleic acid sample comprises 3 pg to 100 ng or about 3 pg to about 100 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample comprises 10 pg to 100 ng or about 10 pg to about 100 ng of DNA, e.g., genomic DNA, or 10 pg to 5 ng or about 10 pg to about 5 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample comprises 10 pg to 10 ng or about 10 pg to 10 ng, 10 pg to 5 ng or about 10 pg to 5 ng, 25 pg to 10 ng or about 25 pg to 10 ng, 25 pg to 5 ng or about 25 pg to 5 ng, 50 pg to 10 ng or about 50 pg to 10 ng, or 50 pg to 5 ng or about 50 pg to 5 ng of DNA, e.g., genomic DNA.

In some embodiments, the nucleic acid sample comprises 3 pg to 5 ng or about 3 pg to about 5 ng of DNA, e.g., genomic DNA, hi some embodiments, the nucleic acid sample comprises 50 pg to 5 ng or about 50 pg to about 5 ng of DNA, e.g., genomic DNA. In some embodiments, the nucleic acid sample contains 2.5 pg, 3 pg, 4 pg, 5 pg, 6 pg, 7 pg, 8 pg, 9 pg, 10 pg, 15 pg, 20 pg, 25 pg, 30 pg, 35 pg, 40 pg, 45 pg, 50 pg, 55 pg, 60 pg, 70 pg, 75 pg, 80 pg, 85 pg, 90 pg, 95 pg, 100 pg, 125 pg, 150 pg, 175 pg, 200 pg, 225 pg, 250 pg, 275 pg, 300 pg, 325 pg, 350 pg, 375 pg, 400 pg, 420 pg, 4 25pg, 450pg, 475pg, 500pg, 600pg, 700pg, 800pg, 900pg, 1ng, 1.1ng, 1.2ng, 1.3ng, 1.4ng, 1.5ng, 1.6ng, 1.7ng, 1.8ng, 1.9ng, 2ng, 2.1ng, 2.2ng, 2.3ng, 2.4ng, 2.5ng, 2.6ng, 2.7ng, 2.8ng, 2.9ng, 3ng, 3.25ng, 3.5ng, 3.75ng, 4ng, 4.25ng, 4.5ng, 4.75ng or 5ng, or about 2.5 pg, 3pg, 4pg, 5pg, 6pg, 7pg, 8pg, 9pg, 10pg, 15pg, 20pg, 25pg, 30pg, 35pg, 40pg, 45pg, 50pg, 55pg, 60pg, 70pg, 75pg, 80pg, 85pg, 90pg, 9 5pg, 100pg, 125pg, 150pg, 175pg, 200pg, 225pg, 250pg, 275pg, 300pg, 325pg, 350pg, 375pg, 400pg, 420pg, 425pg, 450pg, 475pg, 500pg, Including 600pg, 700pg, 800pg, 900pg, 1ng, 1.1ng, 1.2ng, 1.3ng, 1.4ng, 1.5ng, 1.6ng, 1.7ng, 1.8ng, 1.9ng, 2ng, 2.1ng, 2.2ng, 2.3ng, 2.4ng, 2.5ng, 2.6ng, 2.7ng, 2.8ng, 2.9ng, 3ng, 3.25ng, 3.5ng, 3.75ng, 4ng, 4.25ng, 4.5ng, 4.75ng or 5ng of DNA, e.g., genomic DNA, or between any of the aforementioned values. In some embodiments, the nucleic acid sample is at or about 3pg to 10ng, 3pg to 5ng, 3pg to 4ng, 3pg to 3ng, 3pg to 2ng, 10pg to 10ng, 10pg to 5ng, 10pg to 4ng, 10pg to 3ng, 10pg to 3ng, 10pg to 2ng, 25pg to 10ng, 25pg to 5 ... 25pg to 4ng or about 25pg to 4ng, 25pg to 3ng or about 25pg to 3ng, 25pg to 2ng or about 25pg to 2ng, 40pg to 10ng or about 40pg to 10ng, 40pg to 5ng or about 40pg to 5ng, 40pg to 4ng or about 40pg to 4ng, 40pg to 3ng or about 40pg to 3ng, 40pg to 2ng or about 40pg to 2ng, 50pg to 10ng or about 50pg to 10ng, 50pg to 5ng or about 50pg to 5ng, 50pg to 4ng or about 50pg to 4ng, 50pg to 3ng or about 50pg to 3ng, 50pg to 2ng or about 50pg to 2 ng, 10 pg to 2 ng or about 10 pg to 2 ng, 10 pg to 1.5 ng or about 10 pg to 1.5 ng, 10 pg to 1 ng or about 10 pg to 1 ng, 20 pg to 2 ng or about 20 pg to 2 ng, 20 pg to 1.5 ng or about 20 pg to 1.5 ng, 20 pg to 1 ng or about 20 pg to 1 ng, 25 pg to 2 ng or about 25 pg to 2 ng, 25 pg to 1.5 ng or about 25 pg to 1.5 ng, 25 pg to 1 ng or about 25 pg to 1 ng, 30 pg to 2 ng or about 30 pg to 2 ng, 30 pg to 1.5 ng or about 30 pg to 1.5 ng, 30 pg to 1 ng or about 30 pg to 1 ng , 35 pg to 2 ng or about 35 pg to 2 ng, 35 pg to 1.5 ng or about 35 pg to 1.5 ng, 35 pg to 1 ng or about 35 pg to 1 ng, 40 pg to 2 ng or about 40 pg to 2 ng, 40 pg to 1.5 ng or about 40 pg to 1.5 ng, 40 pg to 1 ng or about 40 pg to 1 ng, 4 Including 5 pg to 2 ng or about 45 pg to 2 ng, 45 pg to 1.5 ng or about 45 pg to 1.5 ng, 45 pg to 1 ng or about 45 pg to 1 ng, 50 pg to 2 ng or about 50 pg to 2 ng, 50 pg to 1.5 ng or about 50 pg to 1.5 ng, and 50 pg to 1 ng or about 50 pg to 1 ng.
B. Sample Processing and Amplification

In some embodiments, methods provided herein are methods for performing DNA-based kinship analysis, comprising providing a nucleic acid sample from a person of interest; amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences collectively comprising at least or about 2,000 to 50,000 single nucleotide polymorphisms (SNPs), thereby producing amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

Various steps were performed to prepare or process nucleic acid samples for and/or during the assay. Unless otherwise indicated, the preparation or processing steps described below generally can be combined in any manner and in any order to appropriately prepare or process a particular sample for analysis and/or sequencing as disclosed herein.

In some embodiments, the amount of nucleic acid sample provided is 1 ng of genomic DNA, about 1 ng of genomic DNA, or less than 1 ng of genomic DNA. In some embodiments, the methods disclosed herein include amplification of the genomic DNA. In some embodiments, the amplification of the genomic DNA includes one or more multiplex polymerase chain reactions (PCRs) including a plurality of primers, thereby generating an amplification product. In some embodiments, the amplification of the genomic DNA includes a single multiplex PCR reaction. In some embodiments, the amplification of the genomic DNA includes two multiplex PCR reactions. In some embodiments, the amplification of the genomic DNA includes three multiplex PCR reactions. In some embodiments, the amplification of the genomic DNA includes four multiplex PCR reactions.

In some embodiments, one or more primers in the plurality of primers are designed according to the atypical design strategy described in WO 2015/126766, which is incorporated herein by reference in its entirety. In some embodiments, one or more primers in the plurality of primers are at least 24 nucleotides in length, and/or have a melting temperature of less than 60°C, and/or are AT-rich with an AT content of at least 60%. In some embodiments, one or more primers in the plurality of primers comprise at least 24 nucleotides in length that hybridize to the target sequence, and/or have a melting temperature of 50°C to 60°C, and/or are AT-rich with an AT content of at least 60%. In some embodiments, one or more primers in the plurality of primers have a melting temperature of less than 58°C or less than 54°C.

In some embodiments, genomic DNA can be amplified multiple times using multiple primers that hybridize to and/or tag multiple target sequences collectively comprising at least 2,000-50,000 or about 2,000-50,000 single nucleotide polymorphisms (SNPs), or at least 5,000-50,000 or about 5,000-50,000 SNPs. In some embodiments, genomic DNA may be amplified for multiple cycles using multiple primers that hybridize to and/or tag multiple target sequences that collectively comprise at least between or about 2,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, genomic DNA may be amplified for multiple cycles using multiple primers that hybridize to and/or tag multiple target sequences that collectively comprise at least between 5,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs, or between about 5,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, genomic DNA can be amplified for multiple cycles using multiple primers that hybridize to and/or tag multiple target sequences that collectively comprise between at least 10,000-11,000 SNPs, or between about 10,000-11,000 SNPs. In some embodiments, the genomic DNA contains at least between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and 11,000 SNPs. 000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs or 9,000 to 11,000 SNPs, or about 2,000 to 11,000 SNPs. NPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and 11,000 SNPs, between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs The target sequence may be amplified for multiple cycles using multiple primers that hybridize to and/or tag a plurality of target sequences that collectively comprise between 0 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs. In some embodiments, genomic DNA can be amplified for multiple cycles using multiple primers that hybridize to and/or tag a plurality of target sequences that collectively comprise at least, or between about, 6,000 and 11,000 SNPs. In some embodiments, the plurality of SNPs comprises 2,639 or about 2,639 SNPs. In some embodiments, genomic DNA can be amplified for multiple cycles using multiple primers that hybridize to and/or tag a plurality of target sequences that collectively comprise at least, or about, 10,230 SNPs.

In some embodiments, the plurality of SNPs includes at least between 2,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs, or between about 2,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, the plurality of SNPs comprises at least between 5,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs, or between about 5,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, the plurality of SNPs comprises at least between 6,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs, or between about 6,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, the plurality of SNPs comprises at least between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, SNPs between 7,000 and 11,000, SNPs between 8,000 and 15,000, SNPs between 8,000 and 14,000, SNPs between 8,000 and 13,000, SNPs between 8,000 and 12,000, SNPs between 8,000 and 11,000, SNPs between 9,000 and 15,000, SNPs between 9,000 and 14,000, SNPs between 9,000 and 13,000, SNPs between 9,000 and 12,000 or SNPs between 9,000 and 11,000 0 SNPs, or between about 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and In some embodiments, the plurality of SNPs comprises between 11,000 SNPs, between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs. In some embodiments, the plurality of SNPs comprises at or about 2,639 SNPs. In some embodiments, the plurality of SNPs comprises 10,230 or about 10,230 SNPs. In some embodiments, the plurality of SNPs comprises at least between 2,000 and 50,000 SNPs, between 5,000 and 50,000 SNPs, between 5,000 and 45,000 SNPs, between 5,000 and 40,000 SNPs, between 5,000 and 35,000 SNPs, between 5,000 and 30,000 SNPs, between 5,000 and 25,000 SNPs, between 5,000 and 20,000 SNPs. , between 6,000 and 50,000 SNPs, between 6,000 and 45,000 SNPs, between 6,000 and 40,000 SNPs, between 6,000 and 35,000 SNPs, between 6,000 and 30,000 SNPs, between 6,000 and 25,000 SNPs, between 6,000 and 20,000 SNPs, between 7,000 and 50,000 SNPs, between 7,000 and 45,000 SNPs, 7,000 Between 7,000 and 40,000 SNPs, Between 7,000 and 35,000 SNPs, Between 7,000 and 30,000 SNPs, Between 7,000 and 25,000 SNPs, Between 7,000 and 20,000 SNPs, Between 8,000 and 50,000 SNPs, Between 8,000 and 45,000 SNPs, Between 8,000 and 40,000 SNPs, Between 8,000 and 35,000 SNPs, Between 8,000 and 3 Between 8,000 and 25,000 SNPs, between 8,000 and 20,000 SNPs, between 9,000 and 50,000 SNPs, between 9,000 and 45,000 SNPs, between 9,000 and 40,000 SNPs, between 9,000 and 35,000 SNPs, between 9,000 and 30,000 SNPs, between 9,000 and 25,000 SNPs or between 9,000 and 20, 000 SNPs, or between about 2,000 and 50,000 SNPs, between 5,000 and 50,000 SNPs, between 5,000 and 45,000 SNPs, between 5,000 and 40,000 SNPs, between 5,000 and 35,000 SNPs, between 5,000 and 30,000 SNPs, between 5,000 and 25,000 SNPs, between 5,000 and 20,000 SNPs, between 6,000 and 50,000 SNPs 0 SNPs, 6,000-45,000 SNPs, 6,000-40,000 SNPs, 6,000-35,000 SNPs, 6,000-30,000 SNPs, 6,000-25,000 SNPs, 6,000-20,000 SNPs, 7,000-50,000 SNPs, 7,000-45,000 SNPs, 7,000-40,000 SNPs SNPs, between 7,000 and 35,000 SNPs, between 7,000 and 30,000 SNPs, between 7,000 and 25,000 SNPs, between 7,000 and 20,000 SNPs, between 8,000 and 50,000 SNPs, between 8,000 and 45,000 SNPs, between 8,000 and 40,000 SNPs, between 8,000 and 35,000 SNPs, between 8,000 and 30,000 SNPs , between 8,000 and 25,000 SNPs, between 8,000 and 20,000 SNPs, between 9,000 and 50,000 SNPs, between 9,000 and 45,000 SNPs, between 9,000 and 40,000 SNPs, between 9,000 and 35,000 SNPs, between 9,000 and 30,000 SNPs, between 9,000 and 25,000 SNPs, or between 9,000 and 20,000 SNPs.

In some embodiments, the plurality of SNPs includes at least between 2,000 and 11,000 SNPs, between 2,500 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 3,500 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 4,500 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,550 and 11,000 SNPs SNPs, between 6,000 and 11,000 SNPs, between 6,500 and 11,000 SNPs, between 7,000 and 11,000 SNPs, between 7,500 and 11,000 SNPs, between 8,000 and 11,000 SNPs, between 8,500 and 11,000 SNPs, between 9,000 and 11,000 SNPs, between 9,500 and 11,000 SNPs or between 10,000 and 11,000 SNPs 0 SNPs, or between about 2,000 and 11,000 SNPs, between 2,500 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 3,500 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 4,500 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,550 and 11,000 SNPs, between 6,000 and 1 Includes between 1,000 SNPs, between 6,500 and 11,000 SNPs, between 7,000 and 11,000 SNPs, between 7,500 and 11,000 SNPs, between 8,000 and 11,000 SNPs, between 8,500 and 11,000 SNPs, between 9,000 and 11,000 SNPs, between 9,500 and 11,000 SNPs, or between 10,000 and 11,000 SNPs.

In some embodiments, the plurality of SNPs includes SNPs selected from one or more of the group consisting of kinship SNPs, ancestry SNPs, identity SNPs, phenotype SNPs, X-SNPs, and Y-SNPs. In some embodiments, the plurality of SNPs includes kinship SNPs, ancestry SNPs, identity SNPs, phenotype SNPs, X-SNPs, and Y-SNPs. In some embodiments, the plurality of SNPs includes kinship SNPs. In some embodiments, the plurality of SNPs includes Y-SNPs. In some embodiments, the plurality of SNPs includes kinship SNPs and Y-SNPs.

In some of any such embodiments, the plurality of SNPs comprises one or more microhaplotypes. Thus, in some embodiments, a microhaplotype is a type of SNP included in the plurality of SNPs. In some embodiments, each microhaplotype comprises one or more SNPs shared in a single amplicon or shared close to each other on the genome. Generally, a microhaplotype is a biomarker, typically less than 300 nucleotides in length, that represents a combination of multiple alleles, e.g., multiple SNP-based allelic markers.

In some embodiments, the SNPs do not include SNPs with known medical associations, e.g., SNPs associated with known medical conditions, or SNPs with low minor allele frequency. By excluding SNPs with known medical associations, e.g., SNPs associated with known medical conditions, or SNPs with low minor allele frequency, privacy concerns are limited and genetic health data is protected.

In some embodiments, the SNPs include SNPs filtered across multiple genotype samples. In some embodiments, the SNPs are selected from categories including ancestry SNPs, identity SNPs, kinship SNPs, phenotype SNPs, X-SNPs, and Y-SNPs. In some embodiments, the ancestry SNPs include 10-100 or about 10-100 SNPs. In some embodiments, the identity SNPs include 10-200 or about 10-200 SNPs. In some embodiments, the kinship SNPs include 7,000-12,000 or about 7,000-12,000 SNPs. In some embodiments, the phenotype SNPs include 1-50 or about 1-50 SNPs. In some embodiments, the X-SNPs include 10-200 or about 10-200 SNPs. In some embodiments, the Y-SNPs comprise 10-200 or about 10-200 SNPs. In some embodiments, the ancestral SNPs comprise 0-10% or about 0-10% of the total number of SNPs. In some embodiments, the identity SNPs comprise 0-10% or about 0-10% of the total number of SNPs. In some embodiments, the relatedness SNPs comprise 80-100% or about 80-100% of the total number of SNPs. In some embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the SNPs are related SNPs. In some embodiments, at least 85% or at least about 85% of the SNPs are related SNPs. In some embodiments, at least 90% or at least about 90% of the SNPs are related SNPs. In some embodiments, at least 95% or at least about 95% of the plurality of SNPs are consanguineous SNPs. In some embodiments, at least 99% or at least about 99% of the plurality of SNPs are consanguineous SNPs. In some embodiments, 100% of the plurality of SNPs are consanguineous SNPs. In some embodiments, phenotypic SNPs comprise 0-5% or about 0-5% of the total number of SNPs. In some embodiments, X-SNPs comprise 0-5% or about 0-5% of the total number of SNPs. In some embodiments, Y-SNPs comprise 0-5% or about 0-5% of the total number of SNPs. In some embodiments, the SNPs do not include medically informative SNPs or minor allele frequency SNPs. The tag region can be any sequence, such as a universal tag region, a capture tag region, an amplification tag region, a sequencing tag region, or a UMI tag region.

In some embodiments, target sequences are purified and enriched to generate a library of the original DNA sample, also referred to as a nucleic acid library. In some embodiments, purification combines purification beads with enzymes to purify the amplified target from other reaction components. In some embodiments, purified target sequences are enriched by amplifying the DNA and adding UDI adapters and sequences required for cluster generation. The UDI adapters can tag the DNA with a unique combination of sequences that identify each sample for analysis.

In some embodiments, a nucleic acid library is generated from amplification products, including amplification products produced by any of the methods or embodiments described herein. Thus, in some embodiments, the nucleic acid library comprises amplification products generated by amplifying a nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of at least 5,000-50,000, or about 5,000-50,000, or at least 2,000-50,000, or about 2,000-50,000 SNPs.

In some embodiments, nucleic acid or DNA libraries are normalized for quantification and quality checks and pooled by combining equal amounts of normalized libraries to create a pool of libraries that can be sequenced together on the same flow cell. In some embodiments, quantification involves the use of fluorescent quantification methods. In some embodiments, quantification involves quantitative PCR methods. After the DNA libraries are pooled, they can be denatured and diluted using sodium hydroxide (NaOH)-based methods, and sequencing controls can be added.

In some embodiments, the nucleic acid library is quantified, normalized, denatured, and diluted according to the instructions provided in the Forenseq Kintelligence Kit User Guide (Verogen PN: V16000120, the entire contents of which are incorporated herein by reference).

In some embodiments, the nucleic acid library of the DNA library is prepared for sequencing using massively parallel sequencing using any known suitable method to complement the methods described herein.

In some embodiments, also provided herein are nucleic acid libraries constructed using any of the methods described herein.

In some embodiments, the methods provided herein include generating a nucleic acid library from the amplification products.
Sequencing and analysis

In some aspects, the nucleic acid or DNA libraries described herein can be sequenced using any known suitable method to complement the methods described herein, and are not limited to any particular sequencing platform. In some aspects, the samples disclosed herein can be analyzed using any known suitable method to complement the methods described herein. Exemplary methods of sequencing and analysis are described below.
A. Sequencing

In some embodiments, the methods provided herein include sequencing a nucleic acid library generated from the amplification products.

In some embodiments, techniques for sequencing nucleic acid or DNA libraries produced by practicing the methods described herein include the use of polymerase-by-synthesis sequencing, ligation-based sequencing, pyrosequencing, or polymerase-based sequencing methods.

In some embodiments, the nucleic acid library is sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (e.g., Document No. VD2018006, the entire contents of which are incorporated herein by reference). In some embodiments, the nucleic acid library to be sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (e.g., Document No. VD2018006) is denatured.

In some aspects, the sequencing methods disclosed herein include the use of massively parallel sequencing (MPS). In some aspects, the sequencing methods disclosed herein do not include the use of whole genome sequencing (WGS). In some aspects, the sequencing methods disclosed herein do not include the use of microarrays.

In some embodiments, the sequencing methods disclosed herein detect 90% or about 90% of SNP loci.

In some embodiments, the sequencing methods disclosed herein generate an output report that includes the results of sequencing an amplification product containing multiple SNPs.

In some embodiments, the sequencing comprises a sequencing plexity of up to 40-plex. In some embodiments, the sequencing comprises a sequencing plexity of 2-plex to 40-plex. In some embodiments, the sequencing comprises a sequencing plexity of 12-plex to 40-plex. In some embodiments, the sequencing comprises a sequencing plexity of 12-plex to 32-plex. In some embodiments, the sequencing comprises a sequencing plexity of 12-plex to 30-plex. In some embodiments, the sequencing comprises a sequencing plexity of 24-plex to 40-plex. In some embodiments, the sequencing comprises a sequencing plexity of 24-plex to 32-plex. In some embodiments, the sequencing comprises a sequencing plexity of 28-plex to 32-plex. In some embodiments, the sequencing comprises a sequencing plexity of 2-plex, 3-plex, 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, or 32-plex. In some embodiments, the sequencing comprises a sequencing plexity of 30-plex or about 30-plex. In some embodiments, the sequencing comprises a sequencing complexity of 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40-plex. In some embodiments, the sequencing comprises a sequencing plexity of 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, or 45-plex. Sequencing plexity refers to the number of individual samples that are sequenced together, for example, in a flow cell.

In some embodiments, sequencing comprises sequencing the postmortem sample at a sequencing plexity of between 6-plex and 16-plex, or between about 6-plex and 16-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at a sequencing plexity of between 8-plex and 14-plex, or between about 8-plex and 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at a sequencing plexity of between 10-plex and 14-plex, or between about 10-plex and 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at a sequencing plexity of 10-plex, 11-plex, 12-plex, 13-plex, or 14-plex, or at a sequencing plexity of about 10-plex, 11-plex, 12-plex, 13-plex, or 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at a sequencing plexity of 12-plex or about 12-plex.

In some embodiments, sequencing comprises sequencing the ante-mortem sample at a sequencing plexity of between or between about 24-plex and 40-plex. In some embodiments, sequencing comprises sequencing the post-mortem sample at a sequencing plexity of between or between about 26-plex and 38-plex. In some embodiments, sequencing comprises sequencing the post-mortem sample at a sequencing plexity of between or between about 28-plex and 36-plex. In some embodiments, sequencing comprises sequencing the post-mortem sample at a sequencing plexity of at or between about 28-plex and 36-plex. In some embodiments, sequencing comprises sequencing the post-mortem sample at a sequencing plexity of 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, or 34-plex. In some embodiments, the sequencing comprises sequencing the post-mortem sample at a sequencing plexity of 32-plex or about 32-plex.
B. analysis

In some embodiments, the methods provided herein include analyzing the sequence of the amplification product.

In some aspects, the methods disclosed herein involve the use of an analysis module that automatically initiates analysis upon completion of sequencing of a sample (i.e., an amplified product). In some embodiments, the analysis module comprises universal analysis software (UAS).

In some embodiments, the analytical methods disclosed herein generate an output report that includes the results of sequencing an amplification product containing multiple SNPs.

In some embodiments, the sequencing results are analyzed using any suitable sequence analysis software available in the art.

In some embodiments, the sequencing results are analyzed using Forenseq Universal Analysis Software, such as version 2.2 or later (Verogen, San Diego, CA), according to the instructions outlined in the Forenseq Universal Analysis Software Reference Guide, e.g., document number VD2019002, the contents of which are incorporated herein by reference in their entirety.
Genotype and DNA profile determination

In some embodiments, the methods provided herein include a step of genotyping a plurality of SNPs, thereby generating a DNA profile.

In some embodiments, the DNA profile is generated by genotyping multiple SNPs.

In some aspects, an output report containing the results of sequencing an amplification product containing multiple SNPs generated by any of the methods described herein can be used to genotype a sample using any known suitable method to complement the methods described herein. In some aspects, an output report containing the results of sequencing an amplification product containing multiple SNPs generated by any of the methods described herein can be used to generate a DNA profile using any known suitable method to complement the methods described herein.

In some embodiments, the DNA profile comprises genotypes for each of a plurality of SNPs. In some embodiments, the DNA profile comprises genotypes for at least, or at least about, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs. In some embodiments, the DNA profile comprises genotypes for at least 85% or at least about 85% of the plurality of SNPs. In some embodiments, the DNA profile comprises genotypes for at least 90% or at least about 90% of the plurality of SNPs. In some embodiments, the DNA profile comprises genotypes for at least 95% or at least about 95% of the plurality of SNPs. In some embodiments, the DNA profile includes genotypes for at least 99%, or at least about 99%, or about 100% of the SNPs.

In some embodiments, the methods disclosed herein include determining hair color, eye color, and biogeographic ancestry.
Determining relevance

In some embodiments, the methods provided herein include calculating the degree of relatedness of the DNA profile to one or more reference DNA profiles, where the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

In some aspects, the relevance of the DNA profiles described herein can be calculated with reference to one or more reference DNA profiles using any known suitable method to complement the methods described herein.

In some embodiments, the one or more reference DNA profiles are included within a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

In some embodiments, a reference set of DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In some embodiments, a reference set of DNA profiles includes up to 1000 reference DNA profiles. In some embodiments, a reference set of DNA profiles includes up to 500 reference DNA profiles. In some embodiments, a reference set of DNA profiles includes up to 250 reference DNA profiles. In some embodiments, a reference set of DNA profiles includes up to 150 reference DNA profiles. In some embodiments, a reference set of DNA profiles includes up to 100 reference DNA profiles. In some embodiments, the reference set of DNA profiles includes up to 75 reference DNA profiles. In some embodiments, the reference set of DNA profiles includes up to 50 reference DNA profiles. In some embodiments, the reference set of DNA profiles includes up to 25 reference DNA profiles. In some embodiments, the reference set of DNA profiles includes up to 15 reference DNA profiles. In some embodiments, the reference set of DNA profiles includes between 1 and 1,000 reference DNA profiles, between 1 and 500 reference DNA profiles, between 1 and 400 reference DNA profiles, between 1 and 300 reference DNA profiles, between 1 and 250 reference DNA profiles, between 1 and 200 reference DNA profiles, between 1 and 150 reference DNA profiles, between 1 and 100 reference DNA profiles, between 1 and 75 reference DNA profiles, between 1 and 50 reference DNA profiles, between 1 and 25 reference DNA profiles, between 1 and 20 reference DNA profiles, between 1 and 15 reference DNA profiles, between 1 and 10 reference DNA profiles, or between 1 and 5 reference DNA profiles.

In some embodiments, the reference set of DNA profiles includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or 25 reference DNA profiles, and up to 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.

In some embodiments, the reference set of DNA profiles includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, or 20 reference DNA profiles.

In some embodiments, the reference set of DNA profiles includes DNA profiles from blood relatives of the person of interest. In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from blood relatives of the person of interest. In some embodiments, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or or 100%, or about 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% are from relatives of the person of interest. In some embodiments, 100% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. In some embodiments, at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.

In some embodiments, each of the one or more reference DNA profiles from relatives of the person of interest is an antemortem sample. In some embodiments, one or more of the one or more reference DNA profiles from relatives of the person of interest is an antemortem sample. In some embodiments, one or more of the one or more reference DNA profiles from relatives of the person of interest is a postmortem sample. In some embodiments, the one or more reference DNA profiles from relatives of the person of interest comprise a postmortem sample and an antemortem sample.

In some embodiments, each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. For example, in an embodiment including a reference set of DNA profiles comprising three reference DNA profiles from relatives of the person of interest, each of the three reference DNA profiles may independently be from a first-, second-, third-, fourth-, or fifth-degree relative; e.g., the first reference DNA profile may be from a first-degree relative, the second reference DNA profile may be from a third-degree relative, and the third reference DNA profile may be from a first-degree relative.

In some embodiments, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference DNA profiles in the reference set of DNA profiles are relatives, and each of the one or more reference DNA profiles is, independently, from a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative with respect to each of the other one or more reference DNA profiles in the reference set of DNA profiles.

In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are each from first-, second-, third-, fourth-, or fifth-degree relatives.

In some embodiments, the identity of each relative of the person of interest in the reference set of DNA profiles is known. In some embodiments, the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known.

In some embodiments, the reference set of DNA profiles includes DNA profiles derived from a sample from a person of interest. For example, in some embodiments, the reference set of DNA profiles includes DNA profiles derived from a sample from a person of interest, e.g., before the disappearance of the person of interest if the person of interest is a missing person, or before the person of interest becomes a victim of a disaster or conflict. In some embodiments, a DNA profile derived from a sample from a person of interest, e.g., before the disappearance of the person of interest if the person of interest is a missing person, or before the person of interest becomes a victim of a disaster or conflict, is used as a positive control for the person of interest, because the sample was obtained before death or before the person of interest disappeared or became a victim, and is a sample that is confirmed to be derived from the person of interest. Thus, in some embodiments, the reference set of DNA profiles includes DNA profiles derived from a sample from a person of interest, e.g., before the disappearance of the person of interest if the person of interest is a missing person, or before the person of interest becomes a victim of a disaster or conflict, known to be derived from the person of interest before being amplified and/or sequenced.

In some embodiments, the reference set of DNA profiles is in a database, e.g., a genetic database. In some embodiments, the database is not publicly accessible, i.e., not accessible by the public. In some embodiments, the database is not a public database, such as a public database accessible by law enforcement or a third-party genealogy service. In some embodiments, the database is not publicly accessible via a subscription service. In some embodiments, the database is not accessible by a third-party genealogy service.

In some embodiments, calculating the degree of relatedness between a DNA profile and one or more reference DNA profiles does not involve accessing a publicly accessible database, e.g., a publicly accessible genetic database. In some embodiments, calculating the degree of relatedness between a DNA profile and one or more reference DNA profiles does not require internet access to access a database containing the reference set of DNA profiles. In some embodiments, calculating the degree of relatedness between a DNA profile and one or more reference DNA profiles involves use of a local database containing the reference set of DNA profiles. As used herein, "local database" refers to a database that is stored and accessible only locally and is not accessible by the public, e.g., third parties, attempting to query the database.

In some embodiments, the reference set of DNA profiles includes DNA profiles from two or more unrelated families (i.e., family members that are not related to each other), e.g., each family member includes one or more relatives of a missing person and/or disaster or conflict victim. For example, if a disaster or conflict results in multiple casualties from multiple unrelated families, one or more family members from each family member may contribute a reference DNA profile in the reference set of DNA profiles. This local reference set of DNA profiles may then be used locally to identify a disaster or conflict victim from among multiple unrelated families.

In some embodiments, a reference set or database of DNA profiles, e.g., a genetic database or a local database, includes one or more DNA profiles from individuals of a desired ethnicity. In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from the desired ethnicity. In some embodiments, at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from the desired ethnicity. In some embodiments, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from the ethnicity of interest. In some embodiments, at least 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from the ethnicity of interest. In some embodiments, at least 95%, 95%, 97%, 98%, or 99% of the reference DNA profiles in the reference set of DNA profiles are from the ethnicity of interest. In some embodiments, 100% of the reference DNA profiles in the reference set of DNA profiles are from the ethnicity of interest.

In some embodiments, the person of interest is of a desired ethnicity.

In some embodiments, the ethnicity of interest can be any ethnicity, for example, any ethnicity from any location.

In some embodiments, the ethnicity of interest is a rare ethnicity. In some embodiments, a rare ethnicity is represented by less than 0.01%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4%, or 5% of the population of the target country or worldwide, or less than 0.01%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4%, or 5% of the population of the target country or worldwide. In some embodiments, the ethnicity of interest is any ethnicity in the target country. In some embodiments, the ethnicity of interest is a dominant ethnicity in the target country. In some embodiments, the ethnicity of interest is a minority ethnicity in the target country. In some embodiments, the person of interest is from the target country.

The target country may be any target country. In some embodiments, the target country is Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cape Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Republic of the Congo), Costa Rica, Croatia, Cuba, Cyprus, or Czech Republic. , Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini (formerly known as Swaziland), Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Holy See, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Belarus, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar (formerly Burma), Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Korea, North Macedonia, Norway, Oman, Pakistan, Palau, Palestine, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vince The target country is selected from the group consisting of: the United States, the Republic of the Congo, the Republic of the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Korea, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, the United Arab Emirates, the United Kingdom, the United States, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, and Zimbabwe. In some embodiments, the target country is the United States.

In some embodiments, the DNA-based kinship analysis described herein includes the use of a local database. In some embodiments, the DNA-based kinship analysis described herein allows for report generation with minimal user input. In some embodiments, the DNA-based kinship analysis described herein includes the use of an algorithm to calculate a coefficient of kinship. In some embodiments, the coefficient of kinship determines the relatedness status of a sample or DNA profile with a reference DNA profile on a database. For example, in some embodiments, the coefficient of kinship indicates whether each of one or more identified genetic relatives is likely to be a great-great-grandmother, great-great-grandfather, great-grandfather, great-grandmother, grandmother, grandfather, cousin, child of a cousin, or second cousin based on the relative value of the coefficient of kinship. In some embodiments, the reference DNA profile is part of a genealogy database.

In some embodiments, the DNA-based kinship analysis described herein involves identifying genetic relatives to the first, second, third, fourth, or fifth degree of kinship, or about the first, second, third, fourth, or fifth degree of kinship. In some embodiments, the DNA-based kinship analysis described herein involves identifying genetic relatives to the first, second, third, fourth, or greater than fifth degree of kinship. In some embodiments, the DNA-based kinship analysis described herein involves identifying a degree of relatedness between the person of interest and one or more of the one or more reference DNA profiles in a reference set of DNA profiles. For example, in some embodiments, the method involves independently identifying the person of interest as a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of one or more of the one or more reference DNA profiles. A person's first-degree relatives are their parents (e.g., father or mother), full siblings (e.g., sisters or brothers), or children (e.g., sons or daughters). A person's second-degree relatives are those who share approximately 25% of their genes, such as their grandparents, aunts, uncles, nieces, nephews, grandchildren, or half-siblings. A person's third-degree relatives are those who share approximately 12.5% of their genes, such as great-grandparents, cousins, and great-grandchildren. Fourth-degree relatives include, for example, the children of cousins, half-great-uncles, half-great-aunts, half-nieces, half-nephews, and half-cousins. Fifth-degree relatives include, for example, second cousins, children of half-cousins, and grandchildren of first cousins.

In some embodiments, the DNA-based kinship analysis described herein involves generating a family tree that includes DNA profiles related to one or more DNA profiles. The family tree can be generated using any available means or methodology.

In some embodiments, the DNA-based kinship analysis described herein involves verifying suspects through common ancestry.

In some embodiments, calculating the degree of relatedness comprises calculating the degree of relatedness between the DNA profile, i.e., the DNA profile from the person of interest, and one or more reference DNA profiles included within a reference set of DNA profiles, e.g., a reference set of DNA profiles including one or more reference DNA profiles from blood relatives of the person of interest.

In some embodiments, calculating the degree of relatedness comprises calculating the degree of relatedness between a DNA profile, i.e., a DNA profile from the person of interest, and one or more reference DNA profiles contained within a reference set of DNA profiles, for example a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of the person of interest, by comparing a set of SNPs that are or include one or more Y-SNPs. In some embodiments, the one or more Y-SNPs include 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs, or at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs are or include 85 Y-SNPs. By comparing the set of SNPs including the Y-SNPs between the DNA profile from the biological male and one or more reference DNA profiles from the biological male, a likelihood ratio for the male lineage can be determined.

The likelihood ratio (LR) and kinship values can be calculated using any approach or algorithm(s) known in the art. In some embodiments, the likelihood ratio is calculated using the algorithms pedprobr (Brustad et al., Int. J. Legal Med., 2021, 135:117-129, the contents of which are incorporated herein by reference in their entirety) and dvir (Vigeland et al., Scientific Reports, 2021, 11:13661, the contents of which are incorporated herein by reference in their entirety). In some embodiments, the population frequency average from the Genome Aggregated Database (gnoMAD) (Karczewski et al., Nature, 2020, 581:434-443, the contents of which are incorporated herein by reference in their entirety) v3.0 is used for the LR calculation. In some embodiments, no mutation model is used, and theta is set to 0 when the SNPs selected for analysis have low linkage disequilibrium (Karczewski et al., supra, the contents of which are incorporated herein by reference in their entirety). In some embodiments, LR is calculated as follows:
where D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated. The relatedness hypothesis is indicated by a pedigree in which unidentified individuals are tested as relatives. The unrelatedness hypothesis is indicated by a Hardy-Weinberg equilibrium calculation. In some embodiments, LR values are calculated for each locus and then multiplied across loci to obtain a final LR of relationship. To improve computational efficiency, each locus LR can be logarithm-transformed and the locus LRs can be summed. However, due to the high complexity of this platform and often the high degradation levels and/or low input of PM samples in mass fatality incident (MFI) cases, stochastic effects during PCR can cause allele dropout, resulting in situations where allele combinations between two individuals at a locus appear impossible. This typically occurs in parent-offspring relationships where each individual in the relationship is genotyped as homozygous for different alleles, but one or both are actually heterozygous (e.g., a parent has genotype AA and a child has genotype BB, when both the parent or parent and child are actually AB). Genotyping errors and de novo mutations are also possible causes in such cases. This situation can result in an LR of 0, and the entire LR being 0, given that the final LR is the product of the locus LRs. To ensure that the locus LR is not 0, some embodiments add a correction to pedprobr. Thus, in some embodiments, the likelihood ratio for a locus in these cases is calculated as follows:
where 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2 (Galvan-Femenia et al., Heredity, 2021, 126:537-547, the contents of which are incorporated herein by reference in their entirety). Allele 1 and allele 2 are also referred to as the first allele and second allele or locus, and can be any two alleles of interest for a particular locus.

In some embodiments, LR is calculated as described in Galvan-Femenia et al., Heredity, 2021, 126:537-547, the entire contents of which are incorporated herein by reference.

In some embodiments, calculating the degree of relatedness comprises calculating a likelihood ratio of sharing a Y chromosome between a DNA profile, i.e., a DNA profile from the person of interest, and one or more reference DNA profiles contained within a reference set of DNA profiles, e.g., a reference set of DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs that are or include one or more Y-SNPs. In some embodiments, the one or more Y-SNPs include 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs, or at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs are or include 85 Y-SNPs.

In some embodiments, calculating the likelihood ratio of sharing a Y chromosome includes calculating a coefficient of relatedness based on one or more Y-SNPs from among the plurality of SNPs. In some embodiments, calculating the likelihood of sharing a Y chromosome includes calculating a log-likelihood by providing a DNA profile and one or more reference DNA profiles included in a reference set of DNA profiles as inputs, e.g., as inputs to PC-Relate. In some embodiments, calculating the likelihood of sharing a Y chromosome can allow for the identification of matching Y chromosomes shared between DNA profiles, e.g., the DNA profile of the person of interest, and one or more of the one or more reference DNA profiles, which can then be used to determine a likelihood ratio for the male lineage of the person of interest.

In some embodiments, this involves the use of a coefficient of kinship to calculate a measure of relatedness between two samples, e.g., between a DNA profile and one of one or more reference DNA profiles. In some embodiments, the methods provided herein involve calculating a coefficient of kinship using a kinship model constructed from, e.g., a public genealogy database, e.g., using PC-AiR or modified PC-AiR methods, and determining kinship on a local set of target samples, e.g., using PC-Relate, rather than on the public database, using an expanded set of publicly accessible samples. This also involves, in some embodiments, calculating a likelihood ratio (LR) for each comparison. The likelihood ratio (LR) is a standard measure of relatedness, e.g., in the field of forensic science.

In some embodiments, PC-Relate (Conomos et al., American Journal of Human Genetics, 2016, 98:127-148, the contents of which are incorporated herein by reference in their entirety) and PC-AiR (Conomos et al., Genetic Epidemiology, 2015, 39:276-293, the contents of which are incorporated herein by reference in their entirety), such as those described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61:102769, the contents of which are incorporated herein by reference in their entirety, are used to calculate the genome-wide relatedness coefficient, shared cM, and longest segment cM. This method is useful when the relationship between two individuals is unknown, thereby eliminating the need for genealogy, and is applicable to situations such as missing persons or victims of conflict.

In some embodiments, the PC-AiR method first takes a set of genotyped individuals and separates them into two non-overlapping subsets: one set contains unrelated individuals representing the ancestry of all individuals (the unrelated subset), and the other set contains individuals who have at least one relative in the first subset (the related subset). To construct the unrelated subset, modifications were made to the original PC-AiR method to improve computational efficiency in building the model. Samples with no relatives or the fewest relatives are added to the unrelated subset, while samples with more relatives are excluded from the unrelated subset. This is similar to the method described by Conomos et al. This is done by calculating a kinship value for each pair and classifying each individual as related or unrelated based on a strict threshold, as in [Paragraph 1], Genetic Epidemiology, 2015, 39:276-293 (the contents of which are incorporated herein by reference in their entirety): kinship values greater than 0.01 are considered related, and kinship values less than -0.025 are considered unrelated. Samples with less than 5% missing SNP data are excluded. Principal component analysis (PCA) is then performed on the unrelated subset, which then predicts values along components of variation for all individuals in the related subset based on their genetic similarity to individuals in the unrelated subset. The resulting components represent a model that can be used in place of static population frequencies to confirm fit in an unknown set of individuals.

In some embodiments, the PC-Relate method uses principal components from PC-AiR to separate genetic correlation into two components: one for sharing of identical alleles by descent from a recent common ancestor, and one for allele sharing by a more distant common ancestor. The components from PC-AiR are used to estimate allele frequencies based on an individual's ancestral background using linear regression instead of static population frequencies such as those from gnoMAD. Then, for two individuals I and j, the relatedness coefficient
is calculated from the PC-AiR model as follows:
Calculate using:

where s is the SNP in S SNPs classified in both individuals;
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively. This algorithm is called "whole-genome kinship" because it considers the whole genome as one segment of relatedness, indicated by the whole-genome relatedness coefficient. This whole-genome relatedness coefficient is used to confirm relatedness when referring to the whole-genome kinship algorithm. If the number of SNPs shared between two individuals is less than 6000, this study requires a whole-genome relatedness coefficient of greater than 0.031 between two individuals to be considered related.

Thus, in some embodiments, calculating the degree of relatedness comprises calculating the coefficient of relatedness using a whole-genome kinship algorithm as follows:
The person of interest and reference DNA profiles are i and j,
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively.

In some embodiments, calculating relatedness involves calculating kinship coefficients using a "windowed kinship" approach. See Snedecor et al., Forensic Sci Int Genet 2022, 61, 102769, doi: 10.1016/j.fsigen.2022.102769, the contents of which are incorporated herein by reference in their entirety. Windowed kinship involves calculating a genome-wide window of kinship to find shared related segments. This is performed by enumerating all possible windows within each chromosome and calculating the kinship coefficients for all windows. These windows are then filtered by a minimum kinship threshold and included in the shared cMs calculation. The filtered segments are then iterated, and stretches of SNPs that share at least one allele and two alleles are separately classified. The total shared cMs are then calculated across all segments. The total shared cM and the longest segment of cM are used to confirm relationship when referring to the windowed kinship algorithm. If the number of SNPs shared between two individuals is between 6,000 and 8,000, the shared cM value must be greater than 180 and the longest segment of cM must be greater than 30 to consider related. If the number of SNPs shared between two individuals is between 8,000 and 9,000, the shared cM value must be greater than 150 and the longest segment of cM must be greater than 30 to consider related. If the number of SNPs shared between two individuals is 9,000 or more, the shared cM value must be greater than 140 and the longest segment of cM must be greater than 30 to consider related. The whole-genome relatedness coefficient can be used to filter by any number of shared SNPs. However, Snedecor et al., supra, observed higher specificity when filtering by shared cM and longest segment cM (e.g., using windowed kinship) when the SNP overlap was greater than 6000, especially for higher degrees of relatedness.

More simply, the number of SNPs classified between two individuals (SNP overlap) can be used to determine when to use a genome-wide relatedness algorithm (<6000 SNP overlap) and when to use a windowed relatedness algorithm (>6000 SNP overlap). Once an algorithm is determined based on the SNP overlap, a value or set of values is used to filter the data and identify relationships, depending on the algorithm selected. As demonstrated in Snedecor et al., supra, cutoffs for both genome-wide relatedness and windowed relatedness were selected to ensure high sensitivity, but more importantly, high specificity. Lowering these thresholds may capture more relationships (i.e., increasing sensitivity), but is expected to result in more false-positive hits, especially for more distant relationships (e.g., fourth- and fifth-degree relatives).

In some embodiments, calculating the degree of relatedness includes calculating a coefficient of relatedness for a DNA profile, e.g., a DNA profile from the person of interest, and one of the one or more reference DNA profiles. In some embodiments, a degree of relatedness, e.g., a coefficient of relatedness, is calculated for each of the DNA profile and one or more reference DNA profiles. In some embodiments, a likelihood ratio is calculated by dividing the probability that a query, e.g., a DNA profile from the person of interest, and a target, e.g., one of the one or more reference DNA profiles, are related by the probability that the query and target are unrelated based on the observed genotypes in the two samples. The results can then be filtered based on the coefficient of relatedness and LR to confirm the most likely relationship(s) and eliminate false matches among the one or more reference DNA profiles included in the reference set of DNA profiles.

In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the degree of relatedness includes calculating a coefficient of relatedness based on related SNPs from within the plurality of SNPs. In some embodiments, calculating the degree of relatedness includes calculating a coefficient of relatedness based on related SNPs from within the plurality of SNPs and calculating a coefficient of relatedness based on Y-SNPs from within the plurality of SNPs. In some embodiments, calculating the degree of relatedness includes calculating a coefficient of relatedness based on Y-SNPs from within the plurality of SNPs. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs including related SNPs from among the plurality of SNPs between the DNA profile and one or more reference DNA profiles.

In some embodiments, the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs comprising one or more Y-SNPs between the DNA profile and the one or more reference DNA profiles. In some embodiments, the one or more Y-SNPs are included within a plurality of SNPs. In some embodiments, the one or more Y-SNPs include at least 25, 50, 75, or 100 Y-SNPs. In some embodiments, the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs include 85 Y-SNPs. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome, based on the genotypes of one or more Y-SNPs, by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome.

In some embodiments, calculating the relatedness, e.g., the coefficient of kinship, comprises the use of a principal component analysis (PCA) method. In some embodiments, the relatedness is calculated using a kinship model. In some embodiments, the relatedness is calculated using a kinship model trained using a PCA method. In some embodiments, the PCA method for training the kinship model is PCA. In some embodiments, the PCA method for training the kinship model comprises PCA. In some embodiments, the PCA method for training the kinship model is a method that can account for relatedness of the sample, e.g., known or cryptic relatedness that may arise from the family structure of the entire sample. In some embodiments, the PCA method is PC-AiR, which can enable ancestry determination in the presence of known or cryptic relatedness. See, e.g., Conomos et al. See, "Robust Inference of Population Structure for Ancestry Prediction and Correction of Stratification in the Presence of Relatedness," Genet Epidemiol., 2015, 39(4):276-293, the entire contents of which are incorporated herein by reference. In some embodiments, the PCA method is a modified PC-AiR method as described herein.

In some embodiments, the kinship model is constructed using a training database. In some embodiments, the training database is a genetic database. In some embodiments, the training database is a genealogy database. In some embodiments, the training database is a publicly accessible database. In some embodiments, the training database contains between 1 and 10 million or more training DNA profiles. In some embodiments, the training database contains between 1 and 10 million or more training DNA profiles. 00, 250,000, 275,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000 0, or about 1, 5, 25, 50, 75, 100, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000 00, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000, or at least 1, 5, 2 5, 50, 75, 100, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000, or at least about 1, 5, 25, 50, 75, 10 0, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500 ,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000 training DNA profiles, or a range between any two of the foregoing values. In some embodiments, the training database may contain up to or up to about 100, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000 training DNA profiles. In some embodiments, the training database includes between 5,000 and 500,000, or between 10,000 and 500,000, or between 15,000 and 500,000, or between 20,000 and 500,000, or between 25,000 and 500,000, or between 25,000 and 400,000, or between 25,000 and 300,000, or between 25,000 and 250,000, or between 50,000 and 500,000, or between 50,000 and 400,000, or between 50,000 and 300,000, or between 50,000 and 250,000 training DNA profiles.

In some embodiments, the PCA method is PC-AiR and the training database includes at least 1 and up to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, 1,500, 1,600, 1,700, 1,800, 1,900, 2,000, 2,100, 2,200, 2,300, 2,400, 2,500, 2,600, 2,700, 2,800, 2,900, 3,000, 3,500, 4,000, 4,500, or 5,000 training DNA profiles, or a range between any two of the foregoing values.

In some embodiments, the PCA method is a modified PC-Air method and the training database is 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 0 or 10,000,000, or about 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300, 000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000, or At least 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500, 000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000, or at least about 3,000, 4,000 0, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, 300,000, 400,000, 500,000, 600,000, 700, 000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000 or 10,000,000 training DNA profiles, or a range between any two of the foregoing values.

In some embodiments, accessing the training database does not require internet access. In some embodiments, training the kinship model does not require internet access. In some embodiments, the training database is locally accessible.

In some embodiments, the kinship model is trained by applying a PCA method to a training database. In some embodiments, the training DNA profile includes genotypes for a plurality of SNPs. In some embodiments, the kinship model includes principal components (PCs) obtained for the training database using the PCA method.

In some aspects, given a training database of training DNA profiles, both PC-AiR and modified PC-AiR methods can identify a sufficiently acceptable set of unrelated samples of training DNA profiles from the training database that is as close to large as possible while still adequately sampling all ancestral backgrounds present in the training database. In some embodiments, both PC-AiR and modified PC-AiR methods can identify a set of unrelated samples, e.g., training DNA profiles, within the training database. In some embodiments, the set of unrelated samples samples all or nearly all ancestral backgrounds present in the training database.

In some embodiments, both the PC-AiR and modified PC-AiR methods include an initial step of estimating the kinship relationships between all pairs of samples in the training database. In some embodiments, the kinship coefficients are estimated. In some embodiments, the kinship coefficients are estimated using a simplified kinship estimation method called "KING-Robust."

In some embodiments, PC-AiR then proceeds to subsequent steps, including: (1) initializing a set "U" with all samples from the training database; (2) scanning the set and calculating, for each sample, how many samples in U it is related to (called "R") and how many samples in U it is "ancestrally branched" from (called "D"); (3) selecting the sample with the highest R, or if there are multiple samples with the highest R, selecting the sample with the highest R and lowest D; (4) removing the selected sample from U; and (5) repeating from step (2). For example, using PC-AiR, if there are 50,000 samples, the process might look at ^50,000² data points in the first iteration, ^49,999² data points in the second iteration, and repeat until there are no more related samples in the set, e.g., going up to ^20,000² or ^10,000² data points. In some embodiments, this procedure continues until U contains only irrelevant samples.

In some embodiments, PC-AiR considers samples related based on estimated relatedness. In some embodiments, samples with an estimated relatedness coefficient of ≥ 0.025 are considered related.

In some embodiments, PC-AiR considers samples to be ancestrally diverged based on estimated kinship. In some embodiments, samples with estimated kinship coefficients <0.025 are considered to be ancestrally diverged.

In some embodiments, PC-AiR includes the steps of: (1) estimating relatedness coefficients between all pairs of samples (e.g., training DNA profiles) in a training database, where pairings with a relatedness coefficient >0.025 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating it as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to the samples in the unrelated sample set, thereby designating it as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i).

In some embodiments, the modified PC-AiR method includes one or more adjustments compared to PC-AiR. In some embodiments, whether a sample is related is more strictly defined in the modified PC-AiR method. In some embodiments, the modified PC-AiR method considers a sample to be related if the estimated coefficient of relatedness is ≧0.01.

In some embodiments, the modified PC-AiR method considers samples to be ancestrally diverged based on estimated relatedness coefficients. In some embodiments, samples with estimated relatedness coefficients <0.025 are considered to be ancestrally diverged.

In some embodiments, the modified PC-AiR method involves removing all samples with >5% missing genotypes (e.g., more than 5% of the SNPs in the DNA profile) to ensure that each sample is fully informative.

In some embodiments, the modified PC-AiR method includes the steps of: (1) calculating, for each sample, "R," the total number of related samples in the training database; "D," the number of ancestral branch samples in the database; and "S," the set of related samples; (2) ranking all samples by R (ascending) and D (descending); (3) iterating through the list of ranked samples, and (i) if the sample is not in the "related" set, adding it to the unrelated set and adding all samples from S (i.e., DNA profiles related to the sample) to the related set; or (ii) if the sample is in the "related" set, ignoring the sample and moving on to the next sample. In some aspects, this modified PC-AiR method allows for a process of near-linear complexity (i.e., execution time scales linearly with the number of samples), rather than exponential.

In some embodiments, the modified PC-AiR method includes the steps of: (1) estimating relatedness coefficients between all pairs of samples (e.g., DNA profiles) in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).

In some embodiments, after determining the unrelated sample set using either the PC-AiR method or the modified PC-AiR method, PCA is applied to the unrelated sample set to train a kinship model. In some embodiments, the kinship model further includes PC values calculated for the related sample set. In some embodiments, the PC values for the related sample set are determined based on the PCs obtained for the unrelated sample set.

In some embodiments, PCA is applied to the entire training database to build the kinship model.

In some embodiments, the provided method includes training a kinship model.

In some embodiments, the provided method does not include training a kinship model. In some embodiments, the kinship model is trained before calculating the relatedness, e.g., the relatedness coefficient.

In some embodiments, accessing the kinship model does not require internet access. In some embodiments, the kinship model is accessible locally.

In some embodiments, the degree of relatedness, e.g., the coefficient of relatedness, is calculated using a kinship model. In some embodiments, the degree of relatedness is calculated using the PCs of the kinship model. In some embodiments, calculating the degree of relatedness includes obtaining PC values of a DNA profile, e.g., a DNA profile of the person of interest. In some embodiments, calculating the degree of relatedness includes obtaining PC values of a reference DNA profile(s). In some embodiments, the degree of relatedness is calculated using the PC values of the DNA profile. In some embodiments, the degree of relatedness is calculated using the PC values of the DNA profile and the reference DNA profile(s).

In some embodiments, the degree of relatedness, e.g., the coefficient of kinship, is calculated using PC-Relate. See, e.g., Conomos et al., Model-free Estimation of Recent Genetic Relatedness, Am. J. Hum. Genet., 98(1):127-148 (2016), the entire contents of which are incorporated herein by reference. In some embodiments, the degree of relatedness is calculated by providing a DNA profile, e.g., a DNA profile of a person of interest, as input to PC-Relate. In some embodiments, the degree of relatedness is calculated by providing a kinship model, e.g., a PC, and a DNA profile as input to PC-Relate. In some embodiments, a reference DNA profile(s) is/are further provided as input to PC-Relate.

In some embodiments, the relatedness, e.g., the relatedness coefficient, is calculated locally. In some embodiments, calculating the relatedness does not require internet access.

In some embodiments, the methods described herein further include identifying the person of interest. In some embodiments, identifying the person of interest includes identifying the person of interest by the person's legal name. In some embodiments, identifying the person of interest includes identifying the person of interest by the person's family relationship to one or more known persons in the reference set of DNA profiles. For example, in some embodiments, identifying the person of interest includes confirming that the person of interest is a son or daughter of a particular known person and/or a full sibling of a particular known person.
kit

Provided herein are kits containing any of the primers, reagents, or compositions described herein, which may further include instructions regarding how to use the kit, such as the uses described herein. The kits described herein may also include other materials desirable from a commercial and user standpoint, including other buffers, diluents, filters, and package inserts containing instructions for performing the methods described herein.

In some embodiments, provided herein are kits that comprise at least one container means, the at least one container means containing any of a plurality of primers described herein.
Illustrative Embodiments

Among the exemplary embodiments provided herein are the following:
1. A method for performing DNA-based kinship analysis, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
generating a nucleic acid library from the amplification products;
sequencing the nucleic acid library generated from the amplification products;
analyzing the sequence of the amplification product;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
calculating a degree of relatedness between said DNA profile and one or more reference DNA profiles, said one or more reference DNA profiles being included in a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of said person of interest;
A method comprising:
2. A method for performing DNA-based kinship analysis, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
generating a nucleic acid library from the amplification products;
sequencing the nucleic acid library generated from the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
calculating a degree of relatedness between said DNA profile and one or more reference DNA profiles, said one or more reference DNA profiles being included in a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of said person of interest;
A method comprising:
3. The method of embodiment 1 or embodiment 2, wherein the sequencing is performed using massively parallel sequencing (MPS).
4. The method of any one of embodiments 1 to 3, wherein said sequencing does not include whole genome sequencing (WGS).
5. The method of any one of embodiments 1-4, further comprising generating a pedigree tree comprising said DNA profile associated with one or more DNA profiles.
6. A method for constructing a nucleic acid library for a person of interest, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions;
A method comprising:
7. The method of embodiment 6, further comprising sequencing the amplification products to produce a DNA profile of the person of interest.
8. A method for constructing a nucleic acid library for a reference DNA sample, comprising:
providing nucleic acid samples from relatives of the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions;
A method comprising:
9. The method of embodiment 8, wherein the relative is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest.
10. The method of embodiment 8 or embodiment 9, wherein the relative is a first-, second-, or third-degree relative of the person of interest.
11. The method of any one of embodiments 1 to 10, wherein the nucleic acid sample comprises genomic DNA.
12. The method of any one of embodiments 1 to 11, wherein the nucleic acid sample comprises one or more enzyme inhibitors.
13. The method of embodiment 12, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.
14. The method of any one of embodiments 1 to 13, wherein the nucleic acid sample comprises low quality and/or low abundance nucleic acid molecules.
15. The method of embodiment 14, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA.
16. The low quality nucleic acid molecules have a degradation index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least ...20, 25, 20, 25, 20, 25, 20, 25, 2 , 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200.
17. The method of embodiment 14 or embodiment 15, wherein the low-quality nucleic acid molecules have a DI of at least 1 and at most 158.3 or less.
18. The method of any one of embodiments 1 to 13, wherein the nucleic acid sample comprises high quality nucleic acid molecules.
19. The method of embodiment 18, wherein the high-quality nucleic acid molecules have a DI of less than 1.
20. The method of any one of embodiments 1-19, wherein the person of interest is a missing person.
21. The method of any one of embodiments 1-19, wherein the person of interest is a victim of a disaster or conflict.
22. The method of any one of embodiments 1 to 21, wherein the nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bone, or skin.
23. The method of embodiment 22, wherein the nucleic acid sample is derived from saliva, blood, or semen.
24. The method of embodiment 22, wherein the nucleic acid sample is derived from bone or hair.
25. The method of any one of embodiments 1-21, wherein the nucleic acid sample is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, semen, or other bodily fluid.
26. The method of any one of embodiments 1 to 25, wherein the nucleic acid sample comprises between 3 pg and 100 ng of genomic DNA or between about 3 pg and 100 ng.
27. The method of any one of embodiments 1-26, wherein the nucleic acid sample comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.
28. The method of embodiment 26 or embodiment 27, wherein the nucleic acid sample comprises 1 ng or about 1 ng of genomic DNA.
29. The method of any one of embodiments 1-28, wherein the plurality of SNPs comprises kinship SNPs (kiSNPs).
30. The method of any one of embodiments 1-29, wherein said plurality of SNPs comprises Y chromosome SNPs (Y-SNPs).
31. The method of any one of embodiments 1 to 30, wherein the plurality of SNPs comprises kiSNPs and Y-SNPs.
32. The method of any one of embodiments 1-31, wherein the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs).
33. The method of any one of embodiments 1-28, wherein said plurality of SNPs comprises SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.
34. The method of any one of embodiments 1-33, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of said plurality of SNPs are related SNPs.
35. The method of any one of embodiments 1-34, wherein said reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.
36. The method of any one of embodiments 1-35, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
37. The method of any one of embodiments 1-36, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are first-, second-, third-, fourth-, or fifth-degree relatives.
38. The method of any one of embodiments 1-37, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
39. The method of any one of embodiments 36-38, wherein each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.
40. The method of embodiment 39, wherein each relative of the person of interest in the reference set of DNA profiles is a first-degree, second-degree, or third-degree relative of the person of interest, respectively.
41. The method of any one of embodiments 1-40, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known.
42. The method of any one of embodiments 1-41, wherein the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known.
43. The method of any one of embodiments 1 to 42, wherein the reference set of DNA profiles is in a database.
44. The method of embodiment 43, wherein the database is not publicly accessible.
45. The method of any one of embodiments 1-44, wherein said sequencing comprises a sequencing complexity of up to 40-plex.
46. The method of any one of embodiments 1-44, wherein said sequencing comprises a sequencing complexity of up to 32-plex.
47. The method of any one of embodiments 1 to 44, wherein said sequencing comprises a sequencing complexity of 12-plex to 32-plex.
48. The method of any one of embodiments 1 to 44, wherein said sequencing comprises a sequencing complexity of 24-plex to 32-plex.
49. The sequencing is performed at or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. 45. The method of any one of embodiments 1 to 44, comprising a sequencing complexity of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex.
50. The method of any one of embodiments 1-49, wherein said sequencing comprises a sequencing complexity of at or about 8-16 plex for post-mortem samples, and/or said sequencing comprises a sequencing complexity of at or about 24-40 plex for ante-mortem samples.
51. The method of any one of embodiments 1-50, wherein said sequencing comprises a sequencing complexity of at or about 12-plex for post-mortem samples, and/or said sequencing comprises a sequencing complexity of at or about 32-plex for ante-mortem samples.
52. The method of any one of embodiments 1-51, wherein said sequencing comprises a sequencing complexity of, or about, 30-plex, 31-plex, or 32-plex.
53. The method of any one of embodiments 1-52, further comprising identifying the person of interest.
54. A method for calculating relatedness, comprising:
obtaining a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest;
and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from blood relatives of the person of interest.
55. A method for calculating relatedness, comprising:
generating a DNA profile comprising genotypes of at least or between about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest;
and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from blood relatives of the person of interest.
56. The method of any one of embodiments 1-55, wherein the relatedness is calculated using a kinship model.
57. The method of any one of embodiments 1-56, wherein the relatedness is calculated using a kinship model trained using a PCA method.
58. The method of embodiment 57, wherein the PCA method for training the kinship model is or comprises PCA.
59. The method of embodiment 57 or embodiment 58, wherein the PCA method is PC-AiR.
60. The method of embodiment 59, wherein the PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally in a training DNA profile, wherein pairings with a relatedness coefficient >0.025 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating it as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to samples in the unrelated sample set, thereby designating it as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i).
61. The method of embodiment 57 or embodiment 58, wherein the PCA method is modified PC-Air.
62. The method of embodiment 61, wherein the modified PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples, optionally training DNA profiles, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).
63. The method of any one of embodiments 1-62, wherein said calculating said degree of relatedness comprises calculating a coefficient of relatedness using PC-Relate.
64. The method of embodiment 63, wherein the relatedness is calculated by providing the DNA profile of the person of interest as input to PC-Relate.
65. The method of any one of embodiments 57-64, wherein said relatedness is calculated by providing said kinship model and said DNA profile of said person of interest as input to PC-Relate.
66. The method of any one of embodiments 63-65, wherein said one or more reference DNA profiles are further provided as input to PC-Relate.
67. The calculating the degree of relatedness includes calculating the coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
67. The method of any one of embodiments 1-66, wherein i and j are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively.
68. The method of any one of embodiments 1-67, wherein said calculating said relevance comprises calculating a likelihood ratio.
69. The method of embodiment 68, wherein said calculating said likelihood ratio comprises comparing said plurality of SNPs between said DNA profile and said one or more reference DNA profiles.
70. The method of embodiment 68, wherein said calculating said likelihood ratio comprises comparing a set of SNPs comprising kinship SNPs from among said plurality of SNPs between said DNA profile and said one or more reference DNA profiles.
71. The method of any one of embodiments 68-70, wherein calculating said likelihood ratio comprises dividing the probability that said DNA profile and a reference DNA profile from among said one or more reference DNA profiles are related by the probability that said DNA profile and said reference DNA profile are unrelated, based on the genotypes of said plurality of SNPs.
72. The likelihood ratio (LR) is calculated as follows:
72. The method of any one of embodiments 68-71, wherein D represents said genotype, H _r represents the hypothesis that said individuals are related, and H _u represents the hypothesis that said individuals are unrelated.
73. The LR is calculated as follows:
72. The method of any one of embodiments 68-71, wherein 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.
74. The method of any one of embodiments 1-73, wherein the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles.
75. The method of embodiment 74, wherein said calculating a likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs comprising one or more Y-SNPs between said DNA profile and said one or more reference DNA profiles.
76. The method of embodiment 75, wherein the one or more Y-SNPs are included within the plurality of SNPs.
77. The method of embodiment 75, wherein said one or more Y-SNPs comprise at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs.
78. The method of any one of embodiments 75-77, wherein said one or more Y-SNPs comprise 85 Y-SNPs.
79. The method of any one of embodiments 75-78, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome, based on the genotypes of the one or more Y-SNPs.
80. The method of any one of embodiments 1-79, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of disasters or conflicts.
81. The method of any one of embodiments 1-80, wherein each of the DNA profiles in the reference set of DNA profiles is from a relative of a missing person or a victim of a disaster or conflict.
82. The method of any one of embodiments 1-81, wherein said reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.
83. The method of any one of embodiments 1 to 82, wherein said reference set of DNA profiles comprises up to 100 reference DNA profiles.
84. The method of any one of embodiments 1-83, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
85. The method of any one of embodiments 1-84, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
86. The method of embodiment 84 or embodiment 85, wherein each relative of the person of interest in the reference set of DNA profiles is a first-degree, second-degree, third-degree, fourth-degree, or fifth-degree relative of the person of interest, respectively.
87. The method of any one of embodiments 1-86, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known.
88. The method of any one of embodiments 1-87, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known.
89. The method of any one of embodiments 1-88, wherein the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known.
90. The method of any one of embodiments 1-89, wherein the reference set of DNA profiles is in a database.
91. The method of embodiment 90, wherein the database is not publicly accessible.
92. The method of embodiment 90 or embodiment 91, wherein the database is not accessible by a third-party genealogy service.
93. A nucleic acid library constructed using the method of any one of embodiments 6 to 92.
94. A plurality of primers that specifically hybridize to a plurality of target sequences comprising at least or about 2,000-50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest, wherein amplification of the nucleic acid sample using the plurality of primers in one or more multiplex PCR reactions results in amplification products.
95. A plurality of primers that specifically hybridize to a plurality of target sequences comprising between at least or between about 2,000 and 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest and one or more reference samples,
the one or more reference samples comprise samples from relatives of the person of interest;
A plurality of primers, wherein amplifying said nucleic acid sample from said person of interest and said nucleic acid sample from one or more reference samples using said plurality of primers in one or more multiplex PCR reactions results in amplification products.
96. The plurality of primers of embodiment 94 or embodiment 95, wherein the nucleic acid sample from the person of interest comprises genomic DNA.
97. The plurality of primers of any one of embodiments 94-96, wherein the nucleic acid sample from the person of interest comprises one or more enzyme inhibitors.
98. The plurality of primers of embodiment 97, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.
99. The plurality of primers of any one of embodiments 94-98, wherein the nucleic acid sample from the person of interest comprises low quality and/or low abundance nucleic acid molecules.
100. The plurality of primers of embodiment 99, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA.
101. The low quality nucleic acid molecules have a degradation index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200.
102. The plurality of primers of any one of embodiments 99-101, wherein said low quality nucleic acid molecules have a DI of at least 1 and at most 158.3 or less.
103. The plurality of primers of any one of embodiments 94-102, wherein the nucleic acid sample from the person of interest and/or the nucleic acid sample from one or more reference samples comprises high quality nucleic acid molecules.
104. The plurality of primers of embodiment 103, wherein the high-quality nucleic acid molecules have a DI of less than 1.
105. The plurality of primers of any one of embodiments 94-104, wherein the person of interest is a missing person.
106. The plurality of primers of any one of embodiments 94-104, wherein the person of interest is a victim of a disaster or conflict.
107. The plurality of primers of any one of embodiments 94-106, wherein the nucleic acid sample from the person of interest is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, or other bodily fluid, or containing hair or skin cells.
108. The plurality of primers of any one of embodiments 94-107, wherein the nucleic acid sample from the person of interest comprises at or about 3 pg to 100 ng of genomic DNA.
109. The plurality of primers of any one of embodiments 94-108, wherein the nucleic acid sample from the person of interest comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.
110. The plurality of primers of embodiment 108 or embodiment 109, wherein the nucleic acid sample from the person of interest comprises 1 ng or about 1 ng of genomic DNA.
111. The plurality of primers of any one of embodiments 94-110, wherein the plurality of SNPs comprises kinship SNPs (kiSNPs).
112. The plurality of primers of any one of embodiments 94-111, wherein the plurality of SNPs comprises Y chromosome SNPs (Y-SNPs).
113. The plurality of primers of any one of embodiments 94-112, wherein the plurality of SNPs comprises kiSNPs and Y-SNPs.
114. The plurality of primers of any one of embodiments 94-113, wherein the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs).
115. The plurality of primers of any one of embodiments 94-111, wherein said plurality of SNPs comprises SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.
116. The plurality of primers of any one of embodiments 94-115, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the SNPs of said plurality are related SNPs.
117. The plurality of primers of any one of embodiments 94-116, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of disaster or conflict.
118. The plurality of primers according to any one of embodiments 94-116, wherein each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.
119. The plurality of primers of any one of embodiments 94-118, wherein the one or more reference samples comprise up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples.
120. The plurality of primers of any one of embodiments 94-119, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest.
121. The plurality of primers of any one of embodiments 94-120, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are first-, second-, third-, fourth-, or fifth-degree relatives.
122. The plurality of primers according to any one of embodiments 94 to 121, wherein at least 50% of the one or more reference samples are from relatives of the person of interest.
123. The plurality of primers of any one of embodiments 120-122, wherein each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.
124. The plurality of primers of embodiment 123, wherein each relative of the person of interest in the one or more reference samples is a first-degree, second-degree, or third-degree relative of the person of interest, respectively.
125. The plurality of primers of any one of embodiments 94-124, wherein the identity of each relative of the person of interest in the one or more reference samples is known.
126. The plurality of primers of any one of embodiments 94-125, wherein the identity of each of the one or more reference samples is known.
127. A method for constructing a DNA profile, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
sequencing the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
A method comprising:
128. A method for constructing a DNA profile, comprising:
providing a nucleic acid sample from the person of interest;
providing a nucleic acid sample from a relative of the person of interest;
amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relatives using a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of at least or between about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
sequencing the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile for the person of interest and the relatives of the person of interest;
A method comprising:
129. The method of embodiment 127 or embodiment 128, wherein said sequencing does not include whole genome sequencing (WGS).
130. The method of embodiment 127 or embodiment 129, wherein the nucleic acid sample comprises genomic DNA.
131. The method of embodiment 128 or embodiment 129, wherein the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative of the person of interest comprises genomic DNA.
132. The method of any one of embodiments 127-131, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative comprises one or more enzyme inhibitors.
133. The method of embodiment 132, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.
134. The method of any one of embodiments 127 to 133, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative comprises low quality and/or low abundance nucleic acid molecules.
135. The method of embodiment 134, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA.
136. The low-quality nucleic acid molecules have a degradation index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least 1, 2, 136. The method of embodiment 134 or embodiment 135, having a Deterioration Index (DI) of 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200.
137. The method of any one of embodiments 134-136, wherein said low quality nucleic acid molecules have a DI of at least 1 and at most 158.3 or less.
138. The method of any one of embodiments 127-133, wherein said nucleic acid sample, said nucleic acid sample of said person of interest and/or said nucleic acid sample of said relative comprises high quality nucleic acid molecules.
139. The method of embodiment 138, wherein the high-quality nucleic acid molecules have a DI of less than 1.
140. The method of any one of embodiments 127-139, wherein the person of interest is a missing person.
141. The method of any one of embodiments 127-139, wherein the person of interest is a victim of a disaster or conflict.
142. The method of any one of embodiments 128, 129, and 131-141, wherein said relative of said person of interest is a first-, second-, third-, fourth-, or fifth-degree relative.
143. The method of any one of embodiments 128, 129, and 131-141, wherein the relative of the person of interest is a first-, second-, or third-degree relative.
144. The method of any one of embodiments 127-143, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative is derived from a buccal swab, paper, fabric or other substrate or object impregnated with saliva, blood or other bodily fluid, or containing hair or skin cells.
145. The method of any one of embodiments 127-144, wherein said nucleic acid sample, said nucleic acid sample of said person of interest and/or said nucleic acid sample of said relative comprises at or about 3 pg to 100 ng of genomic DNA.
146. The method of any one of embodiments 127-145, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.
147. The method of embodiment 145 or embodiment 146, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative comprises 1 ng or about 1 ng of genomic DNA.
148. The method of any one of embodiments 127-147, wherein said plurality of SNPs comprises related SNPs.
149. The method of any one of embodiments 127-148, wherein said plurality of SNPs comprises Y chromosome SNPs (Y-SNPs).
150. The method of any one of embodiments 127-149, wherein said plurality of SNPs comprises kiSNPs and Y-SNPs.
151. The method of any one of embodiments 127-150, wherein said plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs).
152. The method of any one of embodiments 127-151, wherein said plurality of SNPs comprises SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.
153. The method of any one of embodiments 127-152, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of said plurality of SNPs are related SNPs.
154. The method of any one of embodiments 1-92 and 127-153, wherein said sequencing comprises a sequencing complexity of up to 40-plex.
155. The method of any one of embodiments 1-92 and 127-153, wherein said sequencing comprises a sequencing complexity of up to 32-plex.
156. The method of any one of embodiments 1-92 and 127-153, wherein said sequencing comprises a sequencing complexity of 12-plex to 32-plex.
157. The method of any one of embodiments 1-92 and 127-153, wherein said sequencing comprises a sequencing complexity of 24-plex to 32-plex.
158. (a) The sequencing is performed using a 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, or 17-plex method. 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex or 45-plex, or about 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex (b) said sequencing comprises a sequencing complexity of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex or 45-plex; or 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex or 35-plex, or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex or 35-plex
154. The method of any one of embodiments 1 to 92 and 127 to 153.
159. The method of any one of embodiments 1-92 and 127-158, wherein said sequencing comprises a sequencing complexity of at or about 8-16 plex for post-mortem samples, and/or said sequencing comprises a sequencing complexity of at or about 24-40 plex for ante-mortem samples.
160. The method of any one of embodiments 1-92 and 127-158, wherein said sequencing comprises a sequencing complexity of at or about 12-plex for post-mortem samples, and/or said sequencing comprises a sequencing complexity of at or about 32-plex for ante-mortem samples.
161. The method of any one of embodiments 1-92 and 127-153, wherein said sequencing comprises a sequencing complexity of, or about, 30-plex, 31-plex, or 32-plex.
162. A method for identifying genetic relatives of a DNA profile, comprising:
Calculating the degree of relatedness of the DNA profile according to any one of embodiments 127 to 161 with one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of the person of interest;
generating a pedigree comprising said DNA profile in relation to said one or more reference DNA profiles.
163. The method of embodiment 162, wherein the one or more reference DNA profiles are part of a database.
164. The method of embodiment 162 or embodiment 163, wherein the reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.
165. The method of any one of embodiments 162-164, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
166. The method of any one of embodiments 162-165, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest.
167. The method of embodiment 165 or embodiment 166, wherein each relative of the person of interest is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.
168. The method of any one of embodiments 162-167, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known.
169. The method of any one of embodiments 162-168, wherein the identity of each of said one or more reference DNA profiles in said reference set of DNA profiles is known.
170. The method of any one of embodiments 162-169, wherein the reference set of DNA profiles is in a database.
171. The method of embodiment 170, wherein the database is not publicly accessible.
172. The method of embodiment 170 or embodiment 171, wherein the database is not accessible by a third-party genealogy service.
173. A method for confirming the identity of a DNA profile, comprising:
calculating a degree of relatedness between a DNA profile comprising genotypes of at least or between about 2,000 and 50,000 SNPs and one or more reference DNA profiles, wherein the DNA profile is from a person of interest and the one or more reference DNA profiles are included within a reference set of DNA profiles comprising one or more reference DNA profiles from relatives of the person of interest;
generating a pedigree comprising said DNA profile in relation to said one or more reference DNA profiles.
174. The method of embodiment 173, wherein the DNA profile is generated by the method of any one of embodiments 127 to 161.
175. The method of embodiment 173 or embodiment 174, wherein the relatedness is calculated using a kinship model.
176. The method of any one of embodiments 173-175, wherein the relatedness is calculated using a kinship model trained using a PCA method.
177. The method of embodiment 176, wherein the PCA method for training the kinship model is or comprises PCA.
178. The method of embodiment 176 or embodiment 177, wherein the PCA method is PC-AiR.
179. 179. The method of embodiment 178, wherein the PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples in a training database, optionally in a training DNA profile, wherein pairings with a relatedness coefficient >0.025 are confirmed as closely related and pairings with a relatedness coefficient <-0.025 are confirmed as ancestrally diverged; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating it as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to samples in the unrelated sample set, thereby designating it as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating starting with step (3)(i).
180. The method of embodiment 176 or embodiment 177, wherein the PCA method is modified PC-Air.
181. The method of embodiment 180, wherein the modified PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples, optionally training DNA profiles, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i).
182. The method of any one of embodiments 173-181, wherein said calculating said degree of relatedness comprises calculating a coefficient of relatedness using PC-Relate.
183. The method of embodiment 182, wherein the relatedness is calculated by providing the DNA profile of the person of interest as input to PC-Relate.
184. The method of embodiment 182 or embodiment 183, wherein the relatedness is calculated by providing the kinship model and the DNA profile of the person of interest as input to PC-Relate.
185. The method of any one of embodiments 182-184, wherein said one or more reference DNA profiles are further provided as input to PC-Relate.
186. The calculating the degree of relatedness includes calculating the coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
186. The method of any one of embodiments 173-185, wherein i and j are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively.
187. The method of any one of embodiments 173-186, wherein said calculating said relevance comprises calculating a likelihood ratio.
188. The method of embodiment 187, wherein said calculating said likelihood ratio comprises comparing said plurality of SNPs between said DNA profile and said one or more reference DNA profiles.
189. The method of embodiment 187, wherein said calculating said likelihood ratio comprises comparing a set of SNPs comprising kinship SNPs from among said plurality of SNPs between said DNA profile and said one or more reference DNA profiles.
190. The method of any one of embodiments 187-189, wherein calculating said likelihood ratio comprises dividing the probability that said DNA profile and a reference DNA profile from among said one or more reference DNA profiles are related by the probability that said DNA profile and said reference DNA profile are unrelated, based on the genotypes of said plurality of SNPs.
191. The likelihood ratio (LR) is calculated as follows:
191. The method of any one of embodiments 187-190, wherein D represents said genotype, H _r represents the hypothesis that said individuals are related, and H _u represents the hypothesis that said individuals are unrelated.
192. The LR is calculated as follows:
191. The method of any one of embodiments 187-190, wherein 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.
193. The method of any one of embodiments 173-192, wherein the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles.
194. The method of embodiment 193, wherein said calculating a likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs comprising one or more Y-SNPs between said DNA profile and said one or more reference DNA profiles.
195. The method of embodiment 194, wherein said one or more Y-SNPs are comprised within said plurality of SNPs.
196. The method of embodiment 194 or embodiment 195, wherein the one or more Y-SNPs comprise at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs.
197. The method of any one of embodiments 194-196, wherein said one or more Y-SNPs comprise 85 Y-SNPs.
198. The method of any one of embodiments 193-197, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome, based on the genotypes of the one or more Y-SNPs.
199. The method of any one of embodiments 173-198, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of disaster or conflict.
200. The method of any one of embodiments 173-199, wherein each of said one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.
201. The method of any one of embodiments 1-81, wherein the one or more reference samples comprise up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA samples.
202. The method of any one of embodiments 173-201, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of said one or more reference samples are from relatives of said person of interest.
203. The method of any one of embodiments 173-202, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are first-, second-, third-, fourth-, or fifth-degree relatives.
204. The method of any one of embodiments 173-203, wherein at least 50% of the reference DNA profiles in the one or more reference samples are from relatives of the person of interest.
205. The method of any one of embodiments 199-204, wherein each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively.
206. The method of embodiment 205, wherein each relative of the person of interest in the one or more reference samples is a first-degree, second-degree, or third-degree relative of the person of interest, respectively.
207. The method of any one of embodiments 173-206, wherein the identity of each relative of the person of interest in the one or more reference samples is known.
208. The method of any one of embodiments 173-207, wherein the identity of each of said one or more reference samples is known.
209. A kit comprising at least one container means, said at least one container means comprising a plurality of the primers of any one of embodiments 94-126.
210. The plurality of SNPs may include between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and 11,000 SNPs. SNPs between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs, or about 2,000 to 11,000 SNPs. 00 SNPs, 3,000-11,000 SNPs, 4,000-11,000 SNPs, 5,000-11,000 SNPs, 5,500-11,000 SNPs, 6,000-11,000 SNPs, 7,000-15,000 SNPs, 7,000-14,000 SNPs, 7,000-13,000 SNPs, 7,000-12,000 SNPs, 7,000-11,000 SNPs, 8,000-15,000 SNPs 209. The method of any one of embodiments 1-92 and 127-208, comprising between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs.
211. The method of any one of embodiments 1-92 and 127-208, wherein said plurality of SNPs comprises 10,230 SNPs.
212. The plurality of SNPs may include between 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and 11,000 SNPs. SNPs between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs, or about 2,000 to 11,000 SNPs. 000 SNPs, 3,000-11,000 SNPs, 4,000-11,000 SNPs, 5,000-11,000 SNPs, 5,500-11,000 SNPs, 6,000-11,000 SNPs, 7,000-15,000 SNPs, 7,000-14,000 SNPs, 7,000-13,000 SNPs, 7,000-12,000 SNPs, 7,000-11,000 SNPs, 8,000-15,000 SNPs 127. The plurality of primers of any one of embodiments 94-126, comprising between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs.
213. The plurality of primers of any one of embodiments 94-126, wherein the plurality of SNPs comprises 10,230 SNPs.
214. The method of any one of embodiments 1-5, 11-93, 127-161, and 210-213, further comprising generating a pedigree comprising said DNA profile in relation to one or more DNA profiles contained within said reference set of DNA profiles.
215. The method of embodiment 214, wherein the pedigree tree comprises the DNA profile in association with one or more DNA profiles from blood relatives of the person of interest.

The following examples are included for illustrative purposes only and are not intended to limit the scope of the invention.
Example 1: Generation of sequence libraries and determination of sensitivity

This example describes a method for determining the sensitivity of the multiplex polymerase chain reaction described herein to generate a sequenceable library. Figure 1 shows an exemplary schematic of the method for generating a sequenceable library described in this example.
A. PCR Amplification of Genomic DNA Targets

A multiplex polymerase chain reaction was performed to amplify 10,230 individual amplicons in genomic DNA samples. Each primer pair was designed to selectively hybridize to and promote the amplification of a specific single nucleotide polymorphism (SNP) in the genomic DNA sample. Input genomic DNA ranging from 50 ng to 50 pg was tested, more specifically, 5 ng, 2.5 ng, 1 ng, 500 pg, 250 pg, 100 pg, and 50 pg. Briefly, 18.5 ml of PCR master mix containing sufficient buffer, dNTPs, MgCl2, salts, and PCR additives, such as glycerol, was added to a single well of a 96-well PCR plate. Five microliters of primer pool containing 10,530 primer pairs, 2-4 units of DNA polymerase, such as Phusion hot start DNA polymerase (Thermo Fisher, catalog number F549L or any other thermostable DNA polymerase), and 50 ng-50 pg of genomic DNA, were also added.

The PCR plate was sealed and placed in a thermal cycler (Veriti 96-well thermal cycler, Thermo Fisher Scientific, 4413964) and run at the template profile described below to generate the amplicon library.
98°C for 3 minutes, 18 cycles:
96°C for 45 seconds 80°C for 10 seconds 54°C for 4 minutes, applicable lamp mode 66°C for 90 seconds, applicable lamp mode 68°C for 10 minutes Hold at 4°C

After cycling, the amplicon library was kept at 2-8°C until proceeding to the purification steps outlined below.
B. Purification of Amplicons from Input DNA and Primers

Two rounds of purification using MagBind Total Pure NGS beads (Omega Biotek, M1378-02), binding, washing, and elution at 1.6x and 0.6x volume ratios, were found to remove genomic DNA and unbound or excess primers. The amplification and purification steps outlined herein produce amplicons approximately 150-350 bp in length. The purified amplicons are then used in a second round of PCR to add adapters for sequencing.
C. Enrichment of Purified Amplicons to Generate a Sequenable Library

A second round of PCR amplification was performed in a 96-well PCR plate by combining 25 ml of purified amplicons from the previous step with 5 ml of adapters provided in the Forenseq Kintelligence kit (Verogen PN: V16000120) and 20 ml of KPCR2 Master Mix provided in the Forenseq Kintelligence kit (Verogen PN: V16000120). The PCR plate was sealed and placed in a thermal cycler (Veriti 96-well thermal cycler, Thermo Fisher Scientific, 4413964) and run using the template profile described below to generate the amplicon library.
98°C for 30 seconds, 15 cycles:
98°C for 20 seconds, 66°C for 30 seconds, 72°C for 30 seconds, 72°C for 1 minute, hold at 4°C

The library was purified using MagBind Total Pure NGS beads (Omega Biotek, M1378-02) binding, washing, and elution with 1x. The purified library was quantified, normalized, denatured, and diluted according to the instructions in the Forenseq Kintelligence Kit User Guide (Verogen PN: V16000120, the entire contents of which are incorporated herein by reference).

The denatured libraries were sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (Document No. VD2018006, the entire contents of which are incorporated herein by reference). As shown in Figure 2, the number of loci detected was similar across the range of input genomic DNA dosage adjustments.

Results are analyzed using Forenseq Universal Analysis Software 2.1 (Verogen, San Diego, CA) according to the instructions outlined therein and provided in Reference Guide Document No. VD2019002, the entire contents of which are incorporated herein by reference.
Example 2: Generation of sequence libraries using degraded DNA

This example describes the sequencing of DNA from small, highly degraded samples. Degraded DNA: A series of degraded blood DNA was obtained from Innogenomics (New Orleans, Louisiana). DNA samples were used to generate sequencing libraries as described in Example 1, except that primer pairs for 10,327 loci were used in this example. Figure 3 shows the percentage of loci detected in degraded DNA using the assay described herein compared to the microarray (GSA) call rate (call rate). The degradation index (DI) is shown on the x-axis, and the number of detected loci is shown on the y-axis. These results demonstrate that even with highly degraded DNA with a DI of 158.3, the assay detected 9,167 loci, sufficient for uploading to a genealogy database for relative search. Alternative technologies, such as microarrays, were unable to detect loci in samples with a high degradation index.
Example 3: Evaluation of inhibitor activity on library preparations

This example describes the evaluation of the effect of PCR inhibitors on the preparation of the libraries disclosed herein. DNA samples from crime scenes often contain co-purifying impurities that inhibit PCR. PCR inhibition is the most common cause of PCR failure when adequate copies of DNA are present. Humic compounds are a group of substances produced during the decay process and have been implicated as contaminants of DNA in soil, natural waters, and recent sediments. Other common inhibitors include hematin (derived from blood), indigo (derived from blue jean), and tannic acid.

To assess the impact of inhibitors commonly found in forensic samples, library preparation was performed as described in Example 1, except that 200 uM hematin, 50 ng/uL humic acid, 133 uM indigo, and 16 uM tannic acid were spiked into the "amplify and tag target" step above, and primer pairs for 10,380 loci were used. The results are shown in Figure 4, with PCR reactions without any inhibitors labeled as controls.
Example 4: Determining relevance

This example describes exemplary results from samples prepared as generally described in Example 1 above.

The Illumina Global Screening Array (GSA) 2.0 was run on 200 ng of each of 17 samples of Utah CEPH family 1463 DNA (Coriell Institute). SNP calls were uploaded to the GEDmatch database (Verogen). An exemplary pedigree is shown in Figure 5. One of the samples, NA12889 (paternal grandfather), was run through the library preparation protocol described in Example 1 and run in the ForenSeq UAS 2.1 module. The generated report was uploaded to the database and searched using the 1:multi tool for relationship searching. The relatedness coefficients from the algorithm in the database were compared to the expected relatedness coefficients. The expected and observed relatedness coefficients are shown in Figure 6.
Example 5: Determining the Coefficient of Kinship in an Illustrative Case Study

This example describes the results of an exemplary case study using sample SNP profiles to determine relatedness coefficients. Ten established pedigrees with 12-28 family members in the GEDmatch database were used to test the ability of the 1:many search algorithm to detect potential relatives. The sample SNP profile from the assay disclosed herein was considered to be Mr./Ms. X = POI (person of interest/unknown crime scene profile). Candidate hits, relatedness coefficients, and relative status are shown in Figure 7.

The results generated from the search algorithm were then used to generate a family tree for Mr. X, as shown in Figure 8. As shown in the family tree, Mr. X's third-degree relatives, a cousin (1C) and great-grandfather (G GF), were returned within the first 11 candidate hits. Mr. X's great-great-grandmother (GG GM), great-great-uncles (Uncle GG/Uncle GG), and cousin's child (1C1R), were fourth-degree relatives and were returned within the first 15 candidate hits. Mr. X's fifth-degree relative, a second cousin (2C), was the 12th hit.
Example 6: Generation of sequence libraries and determination of sensitivity, including evaluation by locus type

This example involves a method for determining the sensitivity of the multiplex polymerase chain reaction described herein to generate a sequenceable library, including evaluation by locus type.

Sequence libraries (sequenced nucleic acid libraries), also called DNA profiles, were generated in the same manner as described in Example 1, except that the results were analyzed using Forenseq Universal Analysis Software version 2.2.

Results are shown in Figure 9, a table summarizing the number of loci detected (as the average of three replicates) based on the amount of input DNA (ng) for each of the different types of loci, e.g., Y-chromosome SNPs (Y-SNPs), X-chromosome SNPs (X-SNPs), phenotypic SNPs (piSNPs), kinship SNPs (kiSNPs), identity SNPs (iiSNPs), and biogeographic SNPs (aiSNPs), out of a total of 10,230 loci analyzed. Genomic DNA input dosage adjustments tested included 5 ng, 2.5 ng, 1 ng, 0.5 ng (500 pg), 0.25 ng (250 pg), 0.10 ng (100 pg), and 0.05 ng (50 pg) of input genomic DNA. As shown in Figure 9, at least 98.9% (10,117 loci) were detected with input DNA amounts ranging from 0.05 ng to 5 ng, representing the total number of detected SNPs, and at least 99.5% (10,179 loci) were detected with input DNA amounts of 0.10 ng and greater.

The data demonstrate that over 10,000 loci can be detected with high efficiency and sensitivity using different types of SNPs and amounts of input DNA ranging from 0.05 ng (50 pg) to 5 ng.
Example 7: Evaluation of inhibitor activity on sequence library preparations, including evaluation by locus type

This example describes the evaluation of the effect of specific inhibitors on the preparation of sequence libraries (sequenced nucleic acid libraries), also called DNA profiles, as disclosed herein, including by the type of locus being detected and sequenced. DNA samples from crime scenes often contain co-purifying impurities that inhibit amplification. Common inhibitors include hematin, humic acid, and indigo.

To assess the impact of inhibitors commonly found in forensic samples, library preparation was performed as described in Example 1, except results were analyzed using Forenseq Universal Analysis Software version 2.2. Evaluation of the impact of specific inhibitors on amplification was performed as described in Example 3, except the inhibitors tested were: 200 μM hematin, 100 μM hematin, 50 ng/μL humic acid, 25 ng/μL humic acid, 16 μM tannic acid, 8 μM tannic acid, 133 μM indigo, and 66.5 μM indigo, included in the amplification step as described in Example 1, and primer pairs for the 10230 locus were used. A positive control reaction without inhibitor was also performed. 1 ng of input DNA was used.

The results are shown in Figure 10, demonstrating that a variety of SNPs, including kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs, and aiSNPs, can be amplified and detected in combination with one another according to the methods described herein with high efficiency and detection rates, as demonstrated, for example, by all or nearly all of the SNPs of each type being detected even in the presence of inhibitors. For example, the number of kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs, and aiSNPs detected is each similar to the number detected in the positive control lacking inhibitors (Figure 10). This data demonstrates that the presence of common inhibitors in a sample does not adversely affect the ability to amplify more than 10,000 SNPs in a PCR reaction using the methods described herein.
Example 8: Evaluation of sequence library preparation using DNA samples obtained after simulated sexual assault

This example describes the generation of a sequence library (sequenced nucleic acid library), also called a DNA profile, using DNA from a simulated sexual assault sample to determine whether a sequence library, e.g., a sequenced nucleic acid library, can be successfully generated using small amounts of input DNA, e.g., 500 pg, the recommended amount being less than 1 ng.

Simulated sexual assault DNA was obtained from samples collected 9 and 22 hours after the simulated sexual assault occurred. DNA was isolated from the sperm fraction using differential extraction, and sperm fractions from both time points were collected and stored for analysis. The amount of DNA from the sperm fraction available as input for the assay (to generate the sequencing library) was only 500 pg, half the recommended amount of 1 ng.

The DNA samples were used to generate sequence libraries (sequenced nucleic acid libraries) as described in Example 1, except that the results were analyzed using Forenseq Universal Analysis Software version 2.2. The percentage of loci detected in the assay (call rate) and the number of SNPs of each type present are shown in Figure 11. Results demonstrate that the majority of SNPs were detected with as little as 500 pg of input DNA, with 99.99% of all SNPs (10,229 of 10,230 SNPs) detected at 9 hours and 99.93% of all SNPs (10,223 of 10,230 SNPs) detected at 22 hours. Specifically, all aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs were detected at both 9 and 22 hours after the simulated sexual assault. At 9 hours, only 1 kiSNP was detected in 9,867 samples, and at 22 hours, only 7 kiSNPs were detected in 9,867 samples. The number of loci detected is sufficient to upload to a genealogy database for relative searching.

This data demonstrates that the methods described herein can be used to detect over 10,000 SNPs, including various kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs, and aiSNPs, and to generate sequence libraries using as little as 500 pg of DNA 9 and 22 hours after a simulated sexual assault occurred, with greater than 99.9% of all SNPs detected. Thus, the methods described herein are suitable for use in generating sequence libraries containing less than the recommended amount of DNA, e.g., 500 pg, following a criminal incident, including sexual assault, or from victims of disasters or conflicts, or from samples left behind by missing persons.
Example 9: Evaluation of PCIA carryover during generation of sequence libraries from saliva samples

This example describes sequencing a nucleic acid library (e.g., to generate a DNA profile) from DNA derived from a saliva sample extracted using organic extraction with phenol-chloroform-isoamyl alcohol (PCIA) extraction.

Saliva DNA was obtained from saliva samples in which the extraction reagent PCIA (e.g., no PCIA, light PCIA, medium PCIA, and heavy PCIA) was intentionally left over in the extracted DNA as carryover, simulating an incomplete extraction. PCIA, which contains phenol, is a known inhibitor of PCR amplification.

DNA samples with no PCIA, light PCIA, moderate PCIA, and heavy PCIA were used to generate sequence libraries (sequenced nucleic acid libraries) as described in Example 1, except that the results were analyzed using Forenseq Universal Analysis Software version 2.2. The total number of SNPs detected for each sample was determined and is shown in Figure 12. The results indicate that PCIA carryover, even at high levels with heavy PCIA carryover, does not affect the assay's ability to detect SNPs, as over 10,170 SNPs were detected in each sample.
Example 10: Generation of sequence libraries from blood samples using various substrates and evaluation of the effect of heme

This example describes the sequencing of nucleic acid libraries (e.g., to generate DNA profiles) of DNA derived from blood samples deposited on different substrates typically found at crime scenes, including rust and denim, as well as blood samples on swabs where only 420 pg of DNA was available, and blood samples extracted using CheleX™ where increasing levels of heme were carried along with the DNA. Heme is a known inhibitor of PCR amplification. Denim contains indigo dye, a known inhibitor of PCR amplification.

Each of the DNA samples was used to generate sequence libraries (sequenced nucleic acid libraries) as described in Example 1, except that the results were analyzed using Forenseq Universal Analysis Software version 2.2, including the blood and rust-containing sample, two blood samples on denim, a 420 pg blood sample on a swab, and blood samples with low or moderate heme carryover or no heme as controls, as well as a positive control blood sample. The total number of SNPs detected for each sample and reference control was determined and is shown in Figure 13. Results show that even with blood samples deposited on different substrates, detection of more than 10,114 SNPs out of 10,230 total SNPs was still possible. While 9,563 SNPs were detected in the 420 pg blood sample, over 10,000 SNPs were detected in the sample with heme, and the number of SNPs detected was not affected by the amount of heme present in the sample. This demonstrates that DNA extracted from blood samples deposited on a variety of substrates commonly found at crime scenes can be used in accordance with the methods provided herein to detect over 10,000 SNPs for forensic applications.
Example 11: Kinship analysis using related samples, related antemortem samples, unrelated postmortem samples, and related mock postmortem samples

This example describes performing kinship analysis as described herein to ascertain up to three degrees of kinship relationships in four different sample sets, including undegraded samples, highly degraded samples, and low-input samples. Specifically, the goal of this example was to determine up to three degrees of kinship relationships from degraded samples sequenced at high plexity, where potential matches existed in a local private database (rather than a publicly accessible database), while still allowing for enough SNPs to accurately predict such kinship relationships. The methodology involved is outlined in FIG. 14 and includes: (a) curating a set of forensically relevant SNP targets and selecting >10,000 SNPs, such as 10,230 SNPs; (b) preparing sequencing libraries from postmortem and antemortem DNA samples by tagging and copying targets, enriching targets, purifying targets, and normalizing target abundance; (c) performing next-generation sequencing at higher plexity, e.g., 12-plex or greater; (d) generating SNP reports (also called DNA profiles); (e) uploading the SNP reports to a local server; (f) performing pairwise comparisons; and (g) calculating relatedness coefficients and likelihood ratios and filtering the most likely familial relationships. In some embodiments, the curation in step (a) is performed in a previous workflow, and the same selected SNP targets, e.g., a specific set of 10,230 SNP targets, are utilized in this workflow.

A set of 10,230 SNP targets was selected for detection in each of four sets of samples. The four different sample sets were sequenced to generate the sequence library described in Example 1. These four different sample sets included: (1) a set of related antemortem samples from CEPH/Utah, including Coriell-verified relatives up to the second degree (referred to herein as "related antemortem CEPH/Utah samples"); (2) a set of related antemortem samples from private family members including relatives up to the fifth degree (referred to herein as "related antemortem private family samples"); (3) a set of unrelated postmortem samples including bone (cremated, embalmed, incinerated, and buried), dental remains/teeth, and deteriorated blood at various Deterioration Index (DI) levels (referred to herein as "true postmortem samples"); and (4) a set of related mock postmortem samples including the same samples from set (2), but either (a) artificially deteriorated by boiling the DNA for 24 hours (DI range of 2.1-20) or (b) sequenced with low DNA input (50 pg) (referred to herein as "related mock postmortem samples").

The true postmortem samples were run in 12-plex using the MiSeq FGx sequencing system, and the results are shown in Figure 15, which shows the number of SNPs detected for each individual sample within this set of true postmortem samples. This includes true postmortem samples labeled as "teeth," "degraded blood," "buried bone," "low-input samples," "other degraded samples," and "other true postmortem samples." As shown in Figure 15, for the true postmortem samples, three of the four buried bone samples had the fewest number of detected (or called) SNPs, ranging from 248 to 1,319 SNPs. The degraded blood samples with the highest DI (DI 158 and DI 56) had the next fewest number of detected SNPs, with 4,603 and 5,069 SNPs, respectively. The remaining true postmortem samples all had a minimum of 6,737 SNPs detected and a maximum of 9,903 SNPs detected (Figure 15). The "Total Pass" count reflects the total number of SNPs detected for each sample out of the full set of 10,230 SNPs, while the "Count Pass" count reflects the total number of SNPs detected among the subset of 2,639 SNPs that are consistently called across samples. As shown in Figure 15, the number of SNPs detected among the subset of 2,639 SNPs ("Count Pass" SNPs) varies less than the total number of SNPs called overall, so there is a core set of SNPs that are consistently called, i.e., detected, across samples.

Mock postmortem samples were also run in 12-plex using the MiSeq FGx sequencing system, and the results are shown in Figure 16. As shown in Figure 16, related mock postmortem samples artificially degraded by boiling, i.e., samples with a DI greater than 0, had a range of 1,470 to 8,999 detected SNPs, with an average of 6,462 SNPs detected for samples from related parents and daughters. Low-input DNA samples had a DI of 0 and an input of 0.05 ng of DNA (Figure 16).

Using the MiSeq FGx sequencing system, related antemortem CEPH/Utah samples were run at 12-plex, 16-plex, 24-plex, and 32-plex to determine the highest plexity that would yield a sufficiently high number of detected SNPs (i.e., SNP call rate) for kinship analysis. 24-plex sequencing runs resulted in the detection of an average of 9,691 SNPs, ranging from 8,297 detected SNPs to a maximum of 9,982 detected SNPs, depending on the sample. 32-plex sequencing runs resulted in the detection of an average of 9,048 SNPs, ranging from 6,894 detected SNPs to a maximum of 9,827 detected SNPs (data not shown). This demonstrated that 30-plex runs allow for a sufficiently high throughput of detected SNPs without significantly compromising the number of detected SNPs and the reliability of kinship analysis.

A related pre-mortem private family sample was sequenced along with three unrelated samples using a 30-plex sequencing run using a MiSeq FGx sequencing system. The results are shown in Figure 17. As shown in Figure 17, with the exception of one replicate of the "self" sample (labeled "rep1*"), over 7,000 SNPs were detected in each sample, which could be attributed to library preparation errors for that particular sample. Samples in which over 7,000 SNPs were detected included the "self" sample and several blood relatives of the "self" individual, including samples from a cousin's child, daughter, sister, nephew, cousin, and husband.

The DNA profiles generated after the sequencing run were then used to perform kinship analysis. Related pre-mortem private family samples (sequenced at 30-plex) were compared with mock postmortem samples (sequenced at 12-plex) derived from the same original related sample, but where the related mock postmortem samples were artificially degraded or used at low input. Using a minimum relatedness coefficient value of 0.031, all expected relationships up to the third degree (e.g., cousins) were matched and no false matches were obtained (e.g., no false positives), resulting in 100% specificity and 100% sensitivity (data not shown). Some, but not all, expected fourth-degree matches (e.g., cousins' children) were obtained (data not shown). These data confirm that this method can accurately confirm relationships up to the third degree while ruling out all unrelated relationships, even with highly degraded, low-input samples such as those that may be available in missing persons and disaster/conflict victim situations.

Next, to assess the most appropriate relatedness threshold, related simulated postmortem samples (sequenced at 12-plex) were compared to the GEDMatch database, assuming that related individuals represented by the related simulated postmortem samples had no close relatives in the GEDMatch database. It was determined that a relatedness coefficient of 0.062 was required to achieve 100% specificity (i.e., no false positives). Applying this relatedness threshold (0.062) to related simulated postmortem samples (sequenced at 12-plex) compared to related premortem private family samples (sequenced at 30-plex) was found to reduce sensitivity by excluding some known third-degree relationships.
Example 12: Assessing the minimum number of SNPs to accurately determine relatedness

To identify the minimum number of detected SNPs (i.e., SNP call rate) required to accurately determine relatedness, we compared known relationships in the GEDMatch database that simulated the range of detected SNPs (i.e., called SNPs). This involved testing call rates of 2,000 SNPs, 4,000 SNPs, 6,000 SNPs, 8,000 SNPs, and 10,000 SNPs for sensitivity and specificity in confirming first-, second-, and third-degree kinship. The results are shown in Figures 18A–E, which show receiver operating characteristic (ROC) curves for the results for 2,000 SNPs (Figure 18A), 4,000 SNPs (Figure 18B), 6,000 SNPs (Figure 18C), 8,000 SNPs (Figure 18D), and 10,000 SNPs (Figure 18E). The lowest SNP call rate (n = 2,000) resulted in reduced specificity for first-, second-, and third-degree relationships (Figure 18A), suggesting that the SNP call rate of the 2,000 detected SNPs represents an absolute floor in SNP call rates for accurately identifying relationships.

The ability to sequence undegraded samples in 3-plex opens the possibility of confirming fourth- and fifth-degree relationships. A similar analysis was performed using the GEDMatch database, but to confirm fourth- and fifth-degree relationships. This involved testing the call rates of 2,000 SNPs, 4,000 SNPs, 6,000 SNPs, 8,000 SNPs, and 10,000 SNPs for sensitivity and specificity in confirming fourth- and fifth-degree familial relationships. The results are shown in Figures 19A-E, which show ROC curves for results of 2,000 SNPs (Figure 19A), 4,000 SNPs (Figure 19B), 6,000 SNPs (Figure 19C), 8,000 SNPs (Figure 19D), and 10,000 SNPs (Figure 19E) for relatives. As shown in Figures 19A-E, a higher minimum number of called SNPs (approximately 6,000) is required to accurately confirm true fourth- and fifth-degree relationships.
Example 13: High-plexity SNP sequencing for kinship analysis using related, simulated antemortem, and simulated postmortem samples

This example describes performing kinship analysis as described herein to confirm relatedness in different sample sets, including undegraded samples, highly degraded samples, and low-input postmortem (PM) and antemortem (AM) samples. Specifically, the goal of this example was to determine familial relationships up to the third degree from degraded samples sequenced at high plexity, with potential matches present in a local private database (rather than a publicly accessible database), while still allowing for enough SNPs to accurately predict such familial relationships. The methodology involved is outlined in Figure 14 and includes the steps of: (a) curating a set of forensically relevant SNP targets and selecting >10,000 SNPs, such as 10,230 SNPs; (b) preparing sequencing libraries from postmortem and antemortem type DNA samples by tagging and copying targets, enriching targets, purifying targets, and normalizing target abundance; (c) performing next-generation sequencing at higher plexity, e.g., 12-plex or greater; (d) generating SNP reports (also called DNA profiles); (e) uploading the SNP reports to a local server; (f) performing pairwise comparisons; and (g) calculating relatedness coefficients and likelihood ratios and filtering the most likely familial relationships. In some embodiments, the curation in step (a) is performed in a previous workflow, and the same selected SNP targets, e.g., a specific set of 10,230 SNP targets, are utilized in this workflow. In some embodiments, a windowed kinship algorithm is used.
A. methodology

A set of 10,230 SNP targets was selected for detection in each set of samples, including mock antemortem and mock postmortem samples.

Libraries were generated as described in Example 1 in these studies, with 1 ng of NA24385 DNA as a positive control, and results were automatically analyzed in ForenSeq Universal Software version 2.3 (UAS) using expected quality control metrics.

Commercially available intact DNA used as simulated antemortem (AM) samples was purchased for these studies and included 81 DNA samples from the 1000 Genomes Project, CEPH collection, or Personal Genome Project DNA samples from the Coriell Institute for Medical Research (Camden, NJ, USA), and four DNA samples extracted from whole blood from Innogenomics Inc. (New Orleans, LA, USA). The low-input DNA samples included sample NA24385 at 0.05 ng, 0.1 ng, 0.25 ng, and 0.5 ng.

The simulated postmortem (PM) sample DNA extracts consisted of five modern tooth (CT) samples, designated CT1, CT2, CT3, CT4, and CT5, seven modern bone samples, and one DNA extract from an ancient bone of Eastern European origin. DNA from the seven modern bone (CB) samples was extracted using either a PrepFiler™ Forensic DNA Extraction Kit (Thermo Fisher, Waltham, MA, USA) for samples CB1, CB3, CB4, CB6, and CB7, or a decalcification protocol for bone samples CB2 and CB5. The degradation index and DNA concentration of CB bone DNA samples were determined using the Quantifiler™ Trio DNA Quantification Kit (Thermo Fisher, Waltham, MA, USA). The DI of the CB samples was 13.6, 4.3, 5.6, 1.1, 1.8, 2.5, and 6.5 for CB1, CB2, CB3, CB4, CB5, CB6, and CB7, respectively.

Additionally, intentionally degraded DNA was used as either mock AM or mock PM samples. Two series of degraded DNA were purchased from Innogenomics Inc. (New Orleans, LA, USA): DNA was extracted from whole blood using organic methods from two different male donors and sheared by sonication at 50°C for times ranging from 0 to 16 hours (samples 1231 and 3551). The quantity and degradation state of human DNA was also determined. The 1231 samples had DIs of 26.3, 33.6, 48.6, 160.3, and 459.8 for the 1231 samples designated 7, 8, 10, 11, and 12, respectively. The 3551 samples had DIs of 56 and 158.3 for the 3551 samples designated 56 and 158, respectively.

Buccal samples were collected from volunteers from families with known pedigrees (RF004, RF016-021), referred to herein as relatives (RF), with the pedigree shown in Figure 26. DNA was extracted from buccal swabs and purified. Two of the DNA samples from the relatives (RF004 and RF016) were artificially degraded using high-temperature treatment as follows: Five replicates of purified buccal DNA from each individual were subjected to 21 cycles of heating and cooling at 98°C for 1 hour, followed by 4°C for 10 minutes. DNA-grade water was then added to the dried DNA to bring the DNA into solution. Degradation indices and DNA concentrations were determined for all relative DNA samples. Degradation indices varied across replicates, with values of 1, 2.1, 2.6, 5.1, and 20 for sample RF004 and 1, 1.5, 2.0, 2.2, and 2.9 for sample RF016.

DNA sequencing libraries were prepared using the ForenSeq Kintelligence Kit (Verogen, San Diego, CA, USA) according to the manufacturer's instructions, and libraries were quantified using the QuantiFluor ONE dsDNA system (Promega, Madison, WI, USA). When sequencing libraries at higher plexities, unique dual-index adapters (UDIs) were utilized. Prior to library preparation, intact DNA samples were quantified for input into the library preparation. Mock PM DNA samples were quantified using qPCR. Unless otherwise noted, the DNA was diluted to 40 pg/μL, and 1 ng of total DNA was added to the library preparation reaction. The positive control DNA NA24385 was serially diluted to 20, 10, 4, and 2 pg/μL for total DNA inputs of 500, 250, 100, and 50 pg to mimic low-input PM samples. The purchased artificially degraded samples had sufficient DNA concentrations to add 1 ng of DNA to the library prep reactions. Not all degraded relative samples had sufficient DNA concentrations to add 1 ng of DNA to the library prep reactions. For sample RF004, degraded replicates with DIs of 2.1, 20, 5.1, and 2.6 were added at 600 pg, 600 pg, 700 pg, and 250 pg, respectively, to the library prep reactions. For sample RF016, degraded replicates with DIs of 2.0, 2.2, and 2.9 had sufficient DNA concentrations to add 1 ng to the library prep reactions. To mimic low-input samples, 50 pg of RF004 with DIs of 1 and 50 and 250 pg of RF016 with DIs of 1 and 1.5 were added to the library preparation reactions, respectively. Based on mtDNA quantification of approximately 1400 mtDNA copies/μL, the DNA concentration of the ancient bones was estimated to be 390 pg. Each set of library preparations included one positive amplification control of 1 ng of NA24385 DNA and one negative template control (NTC).

After target amplification and purification, libraries were normalized to 0.75 ng/μL. If the library yield was less than 0.75 ng/μL, the libraries were pooled undiluted. Mock AM libraries generated from commercially obtained intact DNA showed library yields >0.75 ng/μL, with the exception of one at 0.67 ng/μL. Several libraries generated from mock PM, low DNA input, and commercially degraded DNA samples also had yields >0.75 ng/μL. Libraries were pooled at various plexities by pipetting 8 μL of each normalized or intact library into a 1.7 ml microcentrifuge tube. Libraries generated from mock AM samples were pooled at sample plexities of 3, 12, 16, 24, 30, or 32 total libraries for denaturation and sequencing. Libraries generated from mock PM DNA samples were pooled at sample plexities of 3 or 12 total libraries for denaturation and sequencing. The pooled libraries were denatured with freshly diluted NaOH (HP3) by incubation at room temperature for 5 minutes and then diluted with HT1. The human sequencing control (HSC) (a library consisting of 33 STRs serving as a positive sequencing control) was similarly denatured and diluted with HT1. The denatured library pool, combined with the denatured HSC, was then sequenced on a MiSeq FGx instrument using the MiSeq FGx Reagent Kit according to the manufacturer's recommendations. Where possible, sequencing runs were generated with ForenSeq Universal Analysis Software v2.2. Sequencing utilized 151 cycles of paired-end reads for all libraries. The sequencing run included two 8-cycle indexing reads required to demultiplex the libraries using the indexes present in the UDI adapters.

To generate sequencing results for the mock AM library with very high reads per sample for simulation studies, 30 libraries from the mock AM sample were sequenced on a NextSeq 500 instrument using the NextSeq 500/550 High Output Kit v2.5 (300 Cycles) kit (Illumina, San Diego, CA, USA) according to the manufacturer's recommendations (see NextSeq and Denaturation/Pooling Guide).

The sequencing data were then analyzed using secondary and tertiary data analysis as follows.
Metrics are set using the UAS for the quality of a MiSeq FGx™ run. These metrics include cluster density, cluster pass filter, phasing, prephasing, and Q-score threshold. Cluster density is the number of clusters per square millimeter of the run (K), and the metric is set at 400-1650K/ ^mm² for optimal sequencing results. The cluster pass filter metric measures base call quality via the percentage of clusters passing an Illumina chastity filter (ref), and the metric was set at ≥ 80%. Failure of this metric affects the number of usable reads, but not the quality of those passing reads. The phasing metric represents the percentage of DNA strands in a cluster that lag behind the current cycle in the read and have a passing value of ≤ 0.25%. Alternatively, prephasing is performed before the current cycle in the read and represents molecules in a cluster that have a passing value of ≤ 0.15%. If phasing or prephasing is out of specification, there may be a higher percentage of sequencing errors. Before using data from a run, it is important to determine whether the HSC passes the metric.

All library samples prepared with the six UDI adapters provided in the ForenSeq Kintelligence Kit sequenced on the MiSeq FGx were analyzed for allele and genotype calling using ForenSeq Universal Analysis Software (UAS) v2.3 (Verogen, San Diego, CA) as previously described (Jager et al., Forensic Sci. Int. Genet., 2017, 28:52-70, the contents of which are incorporated herein by reference in their entirety). For library samples prepared with additional UDI adapters for higher complexity, sequencing runs were analyzed on a separate server using the same bioinformatics pipeline utilized in UAS, but via command-line tools. This pipeline (run within UAS or via a command line tool) uses the same basic algorithm for SNP genotype calling present in UAS v1.3 used for ForenSeq DNA Signature analysis (Jager ref) (Verogen, San Diego, CA, USA). First, samples are demultiplexed based on the supplied index sequences found on the UDI adapter by demultiplexing binary base call (BCL) files and generating FASTQ files. Reads 1 and 2 are aligned to primer sequences using the Smith-Waterman-Gotoh algorithm (Gotoh, O., J. Mol. Biol., 1982, 162:705-708). Reads aligned to specific primer pairs are assigned to the loci corresponding to those pairs. The alignments are then written in BAM format. At each SNP position, matches to the reference base call and matches to the alternate base call were counted and filtered with a minimum base quality of 30. The number of reads was then summed for each type of call (reference or alternate) at each locus, requiring a minimum coverage of greater than 10 reads. SNP genotypes were then determined by filtering with an analysis threshold (AT) and interpretation threshold (IT), both set at 3%. The AT and IT thresholds were determined by multiplying the sum of the read counts at that locus by 3%. When low coverage occurred, a minimum of 650 reads was used to calculate the threshold. The resulting AT and IT values were then compared to the total read counts for the reference and alternate alleles at each locus. If the call passed both the AT and IT thresholds, a genotype was determined for each locus. Genotyping results were then written in a variant call format (VCF).

In several studies, GEDMatch data simulations were performed to test the genome-wide kinship algorithm. For these studies, GEDMatch database profiles were downloaded and analyzed as described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61, 102769, doi:10.1016/j.fsigen.2022.102769 (the contents of which are incorporated herein by reference in their entirety). A set of 1,000 de-identified samples, referred to as "query samples," was randomly selected from GEDMatch. These samples were then queried for relatives in the GEDMatch database, and any hits, referred to as "target samples," were selected based on the shared centimorgan (cM) values calculated by the GEDMatch one-to-many tool. This search yielded 2,954 target samples along with matched query samples. These results included query-target pairs with zero shared cM, representing true unrelated pairs. Therefore, the number of target samples is greater than the number of query samples, as it includes both related and unrelated target samples. Degrees of relatedness were determined by comparing the resulting shared cM values generated from the one-to-many tool with the expected range of shared cM per degree of kinship provided by DNA Painter, accessible at https://dnapainter.com/tools/sharedcmv4. Loci classified for samples included in the query and target sets were first filtered for the 10,230 SNPs in the panel and then randomly filtered to 80%, 60%, 40%, and 20% call rates, resulting in 8,000, 6,000, 4,000, and 2,000 loci called for each query-target pair, respectively. Genome-wide relatedness coefficients were calculated for each query-target sample pair for each level of reduced locus call rate using the kinship algorithm. Pairs with genome-wide relatedness coefficients greater than 0.031 were considered related. Pairs with genome-wide relatedness coefficients less than or equal to 0.031 were considered unrelated. Sensitivity and specificity were calculated by comparing these results with the one-to-many tool query results. In other words, the one-to-many query results were considered the true set, and the results generated by the kinship algorithm were considered the test set.

Likelihood ratios (LRs) and relatedness values were then calculated as follows: LRs were calculated using the algorithms pedprobr (Brustad et al., Int. J. Legal Med., 2021, 135:117-129, the contents of which are incorporated herein by reference in their entirety) and dvir (Vigeland et al., Scientific Reports, 2021, 11:13661, the contents of which are incorporated herein by reference in their entirety). Population frequency averages from the Genome Aggregation Database (gnoMAD) (Karczewski et al., Nature, 2020, 581:434-443, the contents of which are incorporated herein by reference in their entirety) v3.0 were used for LR calculations. No mutation model was used and theta was set to 0 because the SNPs selected for analysis have low linkage disequilibrium (Karczewski et al., supra). LR was calculated as follows:
where D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated. The relatedness hypothesis was indicated by pedigrees in which unidentified individuals were tested as relatives. The unrelatedness hypothesis was indicated by Hardy-Weinberg equilibrium calculations. LR values were calculated for each locus and then multiplied across loci to obtain the final LR of relationship. To improve computational efficiency, each locus LR was logarithmically transformed and the locus LRs were summed. However, due to the high complexity of this platform and often the high degradation levels and/or low input of PM samples in mass fatality incident (MFI) cases, stochastic effects during PCR can cause allele dropout, resulting in situations where allele combinations between two individuals at a locus appear impossible. This typically occurs in parent-offspring relationships where each individual in the relationship is genotyped as homozygous for different alleles, but one or both are actually heterozygous (e.g., a parent has genotype AA and a child has genotype BB, when both the parent or parent and child are actually AB). Genotyping errors and de novo mutations are also possible causes in such cases. This situation can result in an LR of 0, and the entire LR being 0, given that the final LR is the product of the locus LRs. A modification was made to pedprobr to ensure that the locus LR was not 0. The likelihood ratio for the locus in these cases was calculated as follows:
where 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2 (Galvan-Femenia et al., Heredity, 2021, 126:537-547, the contents of which are incorporated herein by reference in their entirety).

Genome-wide relatedness coefficients, shared cM, and longest segment cM were calculated using PC-Relate (Conomos et al., American Journal of Human Genetics, 2016, 98:127-148, the contents of which are incorporated herein by reference in their entirety) and PC-AiR (Conomos et al., Genetic Epidemiology, 2015, 39:276-293, the contents of which are incorporated herein by reference in their entirety) as previously described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61:102769, the contents of which are incorporated herein by reference in their entirety. This method is useful when the relationship between two individuals is unknown and therefore pedigrees are not required.

The PC-AiR method first takes a set of genotyped individuals and separates them into two non-overlapping subsets: one set contains unrelated individuals representing the ancestry of all individuals (unrelated subset), and the other set contains individuals who have at least one relative in the first subset (related subset). To construct the unrelated subset, modifications were made to the original PC-AiR method to improve computational efficiency in building the model. Samples with no relatives or the fewest relatives are added to the unrelated subset, while samples with more relatives are excluded from the unrelated subset. This is similar to the method described by Conomos et al. This was done by calculating a pairwise relatedness value and classifying each individual as related or unrelated based on a strict threshold, as described in [Random], Genetic Epidemiology, 2015, 39:276-293 (the contents of which are incorporated herein by reference in their entirety): relatedness values greater than 0.01 were considered related, and relatedness values less than -0.025 were considered unrelated. Samples with less than 5% missing SNP data were excluded. Principal component analysis (PCA) was then performed on the unrelated subset, and values along components of variation were then predicted for all individuals in the related subset based on their genetic similarity to individuals in the unrelated subset. The resulting components represented a model that could be used in place of static population frequencies to confirm fit in an unknown set of individuals. The model used in this study was built using the GEDMatch database.

PC-Relate uses principal components from PC-AiR to separate genetic correlation into two components: one for sharing of identical alleles by descent from a recent common ancestor, and one for allele sharing by a more distant common ancestor. The components from PC-AiR were used to estimate allele frequencies based on an individual's ancestral background using linear regression instead of static population frequencies such as those from gnoMAD. Then, for two individuals i and j, the relatedness coefficient
is calculated from the PC-AiR model as follows:
was calculated using
where s is the SNP in S SNPs classified in both individuals;
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively. This algorithm is called "whole-genome kinship" because it considers the whole genome as one segment of relatedness, indicated by the whole-genome relatedness coefficient. This whole-genome relatedness coefficient is used to confirm relatedness when referring to the whole-genome kinship algorithm. In this study, if the number of SNPs shared between two individuals is less than 6000, a whole-genome relatedness coefficient of more than 0.031 between the two individuals was required to be considered related.

Snedecor et al. (2013) introduced an additional step called "windowed kinship" to more accurately identify distant relationships. Windowed kinship involves calculating a genome-wide window of kinship to find shared related segments. This is performed by enumerating all possible windows within each chromosome and calculating the kinship coefficients for all windows. These windows are then filtered by a minimum kinship coefficient threshold and included in the shared cMs calculation. The filtered segments are then iterated, and stretches of SNPs that share at least one allele and two alleles are separately classified. Total shared cMs are then calculated across all segments. The total shared cMs and the longest segment of cMs are used to confirm relationships when referring to the windowed kinship algorithm. If the number of shared SNPs between two individuals is between 6,000 and 8,000, the shared cM value must exceed 180, and the longest segment of cMs must exceed 30, to consider the relationship. If the number of SNPs shared between two individuals is between 8,000 and 9,000, the shared cM value must exceed 150 and the longest segment of cM must exceed 30 to consider related. If the number of SNPs shared between two individuals is 9,000 or more, the shared cM value must exceed 140 and the longest segment of cM must exceed 30 to consider related. The whole-genome relatedness coefficient can be used to filter on any number of shared SNPs. However, Snedecor et al., supra, observed higher specificity when filtering by shared cM and longest segment of cM (e.g., using windowed relatedness) when the SNP overlap was greater than 6,000, particularly for higher degrees of relatedness.

More simply, the number of SNPs classified between two individuals (SNP overlap) can be used to determine when to use a genome-wide relatedness algorithm (<6,000 SNP overlap) and when to use a windowed relatedness algorithm (>6,000 SNP overlap). Once an algorithm is determined based on the SNP overlap, a value or set of values is used to filter the data and identify relationships, depending on the selected algorithm. As demonstrated in Snedecor et al., supra, cutoffs for both genome-wide relatedness and windowed relatedness were selected to ensure high sensitivity, but more importantly, high specificity. Lowering these thresholds may capture more relationships (i.e., increasing sensitivity), but is expected to result in more false-positive hits, especially for more distant relationships (e.g., fourth- and fifth-degree relatives).
Using the above method, studies were conducted to sequence libraries at higher plexity, thereby reducing total sample reads, in order to generate a stable but smaller typed SNP set for a cost-effective method for kinship analysis.
B. result

To demonstrate the feasibility of high plexity using sequence libraries, we simulated high plexity (>3 samples per sequencing run) with a set of 30 libraries generated from high-quality DNA samples from among the simulated AM samples. The 30 simulated AM samples were prepared and sequenced together as described above to generate sample data with a large number of reads. The BCL files were demultiplexed using a custom, local build of the ForenSeq UAS secondary analysis pipeline, which differed only in the generation of FASTQ files due to differences in the raw data generated by NextSeq. To determine the number of reads to simulate 16 sequence libraries, we divided 25,000,000 (the total estimated number of reads) by 16 (the number of samples) and multiplied by the average percent aligned (96%), yielding a total of 1,500,000 reads per sample at this plexity. The same calculations were then performed to simulate 30 sequence libraries, but instead of dividing by 16, we divided by 30 to obtain 800,000 reads. Simulating a sequencing run with fewer reads is called "downsampling." Bioinformatics tools such as seqtk (https://github.com/lh3/seqtk) can be used to simulate runs with fewer reads or downsample sequencing runs. Using Seqtk, each sample was downsampled to 1.5 million reads to simulate a plexity of 16 samples/run or 800,000 reads to simulate a plexity of 30 samples/run. seqtk randomly selects reads from the FASTQ file and outputs a new FASTQ with the desired number of reads. The resulting downsampled FASTQ was then processed through a locally built ForenSeq UAS pipeline that analyzed the FASTQs as described above.

The total reads generated per sample ranged from 8,086,090 to 32,707,490, with an average of 23,186,251 reads. To simulate high plexity, reads were randomly selected from the FASTQ file for each sample until the desired number of reads was met, referred to as downsampling. Downsampling is defined as the random selection of reads from the FASTQ file until the desired number of reads was achieved. The randomly selected reads were then output to a new FASTQ file. The resulting FASTQ file was analyzed with the bioinformatics algorithm described above. Sequencing plexities of 16 and 30 were simulated by downsampling the data to 1.5M and 800,000 reads for each sample, respectively. Reducing the number of reads per sample resulted in an expected decrease in the number of classified SNPs.

To determine the SNP call rate across samples, the number of classified SNPs was determined for each sample at each simulated sequencing complexity (16 and 30). The 16-sample run (16-plex) data was generated by downsampling the raw reads in the FASTQ file to 1.5 million reads per sample, and the 30-sample run (30-plex) data was generated by downsampling the raw reads to 800,000 reads per sample. Genotyping was then performed, and the number of classified SNPs per sample was summed. For the 16-sample run, the minimum was 7781, the first quartile was 8472, the median was 8630, the third quartile was 8708, and the maximum was 8848. For the 30-plex run, the minimum was 6375, the first quartile was 7179, the median was 7299, the third quartile was 7382, and the maximum was 7516. The distribution of sorted SNPs for two simulated plexities of 30 libraries is shown in Figure 20A. The mean recovery for a plexity of 16 was 8586 SNPs (ranging from 7781 to 8848 SNPs), with a median of 8630 SNPs, a first quartile of 8472 SNPs, and a third quartile of 8708 SNPs. The mean recovery for a plexity of 30 was 7234 SNPs (ranging from 6375 to 7516 SNPs), with a median of 7299 SNPs, a first quartile of 7179 SNPs, and a third quartile of 7382 SNPs (Figure 20A).

Determining relatedness using the above-described kinship algorithm and likelihood ratios relies significantly on the number of classified SNPs for each sample in a one-to-one comparison, also called SNP overlap.

The greater the number of SNPs classified in both samples, the more certain the relatedness and likelihood ratio values. Therefore, we calculated the average SNP overlap between samples in both the simulated 16- and 30-plexity sequencing runs to determine whether sufficient classified SNP loci were shared between samples to confirm true relatedness and the likelihood ratio values. The average common classified SNP loci across all sample combinations in both the simulated 16- and 30-plexity sequencing runs was 6,998 loci, with a minimum overlap of 6,058 and a maximum overlap of 7,322. The simulation demonstrates that sequencing libraries to obtain fewer total reads for each sample allows for the classification of a smaller, more stable set of SNPs for each sample with sufficient overlap for relatedness determination. The number of common classified SNPs was less than 8,000, which may not be sufficient to confirm higher-order relatedness (e.g., fourth- and fifth-degree relatedness), but may be sufficient to confirm relatedness up to the third degree.

It was expected that sequencing libraries at increased plexity would result in fewer classified SNPs and may result in increased allele dropout and subsequent decreased heterozygosity, especially for postmortem (PM) samples with degraded and/or low DNA abundance. To assess locus and allele dropout and heterozygosity levels at higher plexities, libraries generated from simulated antemortem (AM) and PM samples as described above were sequenced at the recommended plexity of three samples per sequencing run, followed by sequencing at four higher plexities of 12, 16, 24, and 32 samples per sequencing run. The resulting number of classified SNPs is shown in Figure 20B. As shown in Figure 20B, the minimum, 1st quartile, median, 3rd quartile, and maximum were 9853, 9976, 10009, 10059, and 10135 for 3-plex; 9332, 9394, 9419, 9520, and 9945 for 12-plex; 8881, 9091, 9303, 9419, and 9901 for 16-plex; and 7653, 8348, 8515, 8706, and 9753 for 32-plex. As shown in Figure 20C, the minimum, first quartile, median, third quartile, and maximum were 7215, 8677, 9724, 9923, and 9991 for 3-plex; and 4603, 8261, 9360, 9664, and 9903 for 12-plex. As shown in Figure 20B, the number of loci with reads below the accepted threshold (AT) increased as the plexity for the reference sample increased. Furthermore, the distribution of loci below the AT broadened as the sequencing plexity increased, with an average of 10,111 (minimum 9,853, maximum 10,135) classified SNPs for samples sequenced with three samples per run and an average of 8,528 (minimum 7,653, maximum 9,753) classified SNPs for samples sequenced with 32 samples per run. The minimum number of typed SNPs remained above 7,000 at the highest plexity of 32 samples per sequencing run tested, indicating that sequencing these libraries at high plexity resulted in many more typed SNP loci compared to the simulated results discussed above and shown in Figure 20A.

Next, the impact of higher-plexity sequencing on sister allele loss for heterozygous loci was assessed by comparing SNP genotypes determined for sample libraries sequenced at the recommended plexity of three samples per run with SNP genotypes determined for the same sample libraries sequenced with 32 samples per run. For each sample and each locus, genotypes were considered concordant if both runs classified the same allele; otherwise, they were discordant. The average overlap between 3-plex and 32-plex sequencing was 8,610 SNPs, with a minimum of 7,808 SNPs and a maximum of 9,667 SNPs. For autosomal loci, both alleles must match to be considered concordant. Allelic concordance for each sample was calculated by dividing the number of concordant alleles by the total number of alleles at the locus classified in both sequencing runs. For simulated antemortem samples, allelic discordance (alleles dropping below AT) increased by an average of 1.9% between libraries sequenced at a plexity of 3 compared to 32, with a minimum of 0.50% and a maximum of 2.8% (Figure 21A, left y-axis). Heterozygosity was determined by summing the number of heterozygous loci per sample and dividing that value by the total number of loci called. Samples sequenced at 32 samples per run (32-plex) showed the greatest difference in heterozygosity compared to the standard plexity of 3 samples per run (3-plex), with a mean difference of 6.8%, a minimum difference of 2.0%, and a maximum difference of 10.2%, indicating that 3-plex sequencing exhibits greater heterozygosity by these values (Figure 21A, right y-axis).

A characteristic of samples from MFI victims is that they are often degraded and may contain low levels of genomic DNA. However, while increasing the number of samples per sequencing run is also advantageous in a cost-effective and overall context, this may result in a lower number of classified SNPs and reduced heterozygosity, thereby exacerbating these issues. To evaluate how increased sequencing plexity affects mock postmortem samples compared to the standard protocol using three samples per sequencing run for degraded and low-input DNA, we generated libraries from 30 mock postmortem (PM) samples with various levels of degradation, low-input DNA samples, dental remains, and cremated, embalmed, incinerated, and buried bones. These libraries were sequenced at standard 3-plex and 12-plex. The number of classified SNPs per sample and above AT per run did not differ significantly between the two sequencing runs, but some samples sequenced at higher plexity had significantly fewer classified SNPs. For example, as shown in Figure 20C, 3-plex sequencing yielded an average of 9,313 SNPs, a minimum of 7,215 SNPs, and a maximum of 9,991 SNPs for the mock PM samples, while 12-plex sequencing yielded an average of 8,796 SNPs, a minimum of 4,603 SNPs, and a maximum of 9,903 SNPs for the mock PM samples. These results indicate that for degraded and/or low-DNA input samples, such as postmortem samples, increasing the number of samples per run increases the number of sub-AT loci, but the greatest impact on the number of classified loci is the DNA degradation state and/or quantity.

Genotype call concordance for mock PM samples from 3-plex or 12-plex sequenced libraries was performed as described for mock AM samples above. This included a modern bone sample and a modern tooth sample, plus two sets of degraded DNA (samples 1231 and 3551) purchased from Innogenomics Inc. (New Orleans, LA, USA). Percent concordance was calculated by comparing the 32-plex or 12-plex run with each 3-plex run and classifying identical allele calls at each locus as concordant and distinct allele calls as discordant (less than AT). For autosomal loci to be considered concordant, both alleles must match. The percentage was calculated by dividing the number of matching loci by the total number of loci called by both runs for each sample. The range of loci called for the simulated AM samples in Figure 21A was 7808 to 9667, with an average of 8610. For the simulated PM samples in Figure 21B, the range was 4580 to 9795, with an average of 8627. Percent heterozygosity was determined by summing the number of heterozygous loci and dividing that value by the total number of called loci. The simulated PM samples included DNA extracted from tooth remains (Tooth), blood with various levels of degradation (sample names beginning with 1231 and 3551), buried bone (BB1), cremated bone (CB1), embalmed bone (CB2 and CB3), incinerated bone (CB5, CB6, CB7), and a series of low DNA inputs (50 pg, 100 pg, and 250 pg, respectively) of Coriell sample NA24385 at 0.05, 0.1, and 0.25 ng.

The number of loci classified in both runs ranged from 4,580 to 9,795, with an average of 8,627 SNP loci. Allelic discordance (alleles falling below the AT) increased by an average of 1.2%, with a minimum of 0.1% and a maximum of 2.6% (Figure 21B, right axis). Dental remains and cremated and incinerated bones showed the lowest allelic discordance, with an average discordance of 0.6%, a minimum discordance of 0.1%, and a maximum discordance of 1.3%.

Heterozygosity was calculated as described above for the mock AM samples. Heterozygosity levels varied depending on the degradation level and amount of input DNA (Figure 21B, right axis). Two degraded DNA samples with degradation indices of 158 and 56 (3551_158 and 3551_56, respectively), as well as the burial bone sample (BB1) and the 0.05 ng (50 pg) NA24385 sample, demonstrated the highest differences in heterozygosity across sequencing plexities (9.0%, 13.5%, 26.9%, and 10.8%, respectively). 1231 libraries 7-8 and 10-12 (Figure 21B) were generated from the same DNA samples with increasing amounts of degradation (DIs of 26.3, 33.6, 48.6, 160.3, and 459.8 for the 1231 samples designated 7, 8, 10, 11, and 12, respectively). Heterozygosity decreased with increasing DNA degradation, with sample 1231_7 having a DI of 26.3 and sample 1231_12 having a DI of 459.8 for these libraries sequenced on the MiSeq FGx at a plexity of 12. BB1 had the highest level of heterozygosity when sequenced at 3-plex. Examination of the intralocus balance (ILB) of this sample (BB1) showed 53% and 28% ILB for samples sequenced at a plexity of 3 and 12 samples per run, respectively. These results indicate that the increased heterozygosity observed for the BB1 sample sequenced at a plexity of 3 samples per run is not due to sequencing errors, as the ILB was expected to be very low.

Bone samples with a CB designation (incinerated, embalmed, or cremated bone DNA extracts) showed the smallest differences in heterozygosity (among seven samples: 1.7% mean difference; 0.1% minimum; 3.9% maximum). Furthermore, high-throughput using this approach performed well with dental remains, showing a 2.5% mean difference in heterozygosity, with a 1.8% minimum difference and a 4.5% maximum difference.

To assess the kinship of MFI victims, AM samples were compared with PM samples to test for relatedness, resulting in a one-to-one comparison. The number of SNPs classified in both individuals (SNP overlap) significantly influences the determination of relatedness, especially distant relationships, such as those between the fourth and fifth degrees of kinship. Therefore, we calculated the SNP overlap (shared SNP loci) among all sample combinations between simulated PM samples sequenced at 12-plex and simulated AM samples sequenced at 32-plex. This was done by pairing each simulated PM sample with each simulated AM sample, determining the number of SNP loci genotyped in both samples, and summing these values to obtain the SNP overlap for each pair. The average overlap between the simulated PM and simulated AM sample combinations was 8,020, with a minimum of 4,071 and a maximum of 9,727. These data indicate that sequencing these libraries at higher plexity (12 for PM samples and 32 for AM samples per run) using this approach, with an average of approximately 8,000 SNPs, allows for confirmation of first-, second-, third-, up to fourth-, and some fifth-degree relationships.

Next, we examined the ability of whole-genome kinship algorithms to confirm relationships when the SNP call rate was <9,000 loci in common (2,000-8,000 classified SNPs). The data presented by Snedecor et al., supra, demonstrated high accuracy in confirming all relatedness using a windowed kinship algorithm when the SNP call rate was approximately 9,000 loci. Windowed kinship works by calculating a genome-wide window of kinship to find shared segments between two individuals. See Snedecor et al., supra. The whole-genome kinship algorithm considers the entire genome as a single segment of kinship, rather than a window between two individuals. Snedecor et al. demonstrated that for confirming higher-order relationships (e.g., fourth- and fifth-degree kinship), windowed kinship is more accurate when the number of SNPs classified in two corresponding samples (SNP overlap) is at least 9,000 SNPs. As shown in Figures 20B and 20C, simulated AM and PM samples sequenced at high plexity were less likely to exhibit the SNP call rate of >/= 9000 required for the windowed kinship algorithm. As described in Snedecor et al., supra, windowed kinship performed well for first-, second-, third-, and most fourth-degree relationships when SNP overlaps were commonly in the range of 6000-8000 loci. However, the performance of windowed kinship decreased when SNP overlaps were commonly in the range of 2000-4000 loci.

Using the methods described above to simulate relatedness profiles, we next examined the ability of whole-genome kinship algorithms to confirm relationships when the SNP call rate was less than 9,000 loci in common (2,000-8,000 classified SNPs).

The windowed kinship algorithm reports relatedness in terms of shared centimorgans (cM) and longest segment cM metrics, while the whole-genome kinship approach reports relatedness in terms of whole-genome kinship coefficients, with a kinship coefficient threshold of >0.031 being considered related.

One thousand anonymous query profiles were randomly selected from the GEDMatch database and searched across the database to select 2,954 "related" target profiles for relationships ranging from the first to fifth degree of kinship, as well as unrelated samples with zero shared cM. Briefly, a set of 1,000 anonymized samples, referred to as "query samples," was randomly selected from GEDMatch. These samples were then queried against relatives in the GEDMatch database, and any hits, referred to as "target samples," were selected based on shared centimorgan (cM) values calculated by the GEDMatch one-to-many tool. This search yielded 2,954 target samples along with matched query samples. These results included query-target pairs with zero shared cM, representing truly unrelated pairs. Therefore, the number of target samples is greater than the number of query samples because it includes both related and unrelated target samples. Relationship degree was determined by comparing the resulting shared cM values generated from the one-to-many tool with the expected range of shared cM per degree provided by DNA Painter. Profiles were first filtered for the 10,230 SNPs in the Kintelligence multiplex. Profiles were then randomly filtered to call rates of 80%, 60%, 40%, and 20%, resulting in 8,000, 6,000, 4,000, and 2,000 loci in each profile for each query-target pair, respectively. A whole-genome kinship algorithm was then calculated to test for one-to-one relationships across all query-target pairs at all SNP counts, for a total of 446,182 comparisons.

First- and second-degree relationships showed high specificity and sensitivity (average sensitivity 70.6%, average specificity 100%), even for profiles with 2,000 classified SNPs, as shown in Figure 22A (Figures 22A-D). Although sensitivity began to decrease for third-degree relationships, the whole-genome kinship algorithm performed better for relationship detection than windowed kinship for profiles with 4,000 to 8,000 classified SNPs (average sensitivity 25.1%, average specificity 99.2%) (Figures 22B, 22C, and 22D). With fewer classified SNPs, fourth- and fifth-degree matches showed lower sensitivity and specificity (Figures 22A-D). The windowed kinship algorithm confirmed these higher-order relationships with higher sensitivity and specificity using these library profiles with a large number of classified SNPs (Snedecor et al., Forensic Sci Int Genet 2022, 61, 102769, doi: 10.1016/j.fsigen.2022.102769, the contents of which are incorporated herein by reference in their entirety). Importantly, specificity for all degrees of kinship remained above 99.98%, and for the 6,000 and 8,000 SNP ranges remained above 99.9975% (Figures 22C and 22D). These results indicate that genome-wide kinship has a very low false positive rate. Overall, these data suggest that the sequencing libraries prepared at higher plexities herein yield sufficient typed SNP data to reliably confirm first-, second-, and third-degree relationships. These simulated results also suggest that combining this approach to generating sequence libraries with the whole-genome algorithm described herein should work for disaster victim identification (DVI) and could be implemented on a local server (as opposed to a publicly and/or law enforcement-accessible server) to enable kinship determination across small databases of antemortem samples.

Next, the ability of the whole-genome algorithm described herein to confirm true relationships on libraries prepared by the above method was tested using the related antemortem CEPH/Utah samples described above and shown in Figure 23A. These samples were selected because they contained two sets of grandparents, one set of parents, and 11 siblings, thereby representing first- and second-degree relationships (Figure 23A). These libraries were sequenced at a plexity of 12, 16, 24, or 32 samples per sequencing run to determine the highest plexity possible to maintain accuracy in confirming true relationships while excluding known unrelated pairs.

The number of SNPs classified in each sample per run was determined to ensure that enough SNPs were classified to perform relatedness and likelihood ratio calculations (Figure 24). The SNP call rate decreased as the number of samples per run increased (Figure 24), a result comparable to the initial high-throughput studies in Figures 20B and 20C, demonstrating the reproducibility of high-plexity sequencing of these libraries. As shown in Figure 24, the minimum, first quartile, median, third quartile, and maximum were 9831, 9859, 9907, 9987, and 10079 for 12-plex; 9718, 9816, 9862, 9957, and 10024 for 16-plex; 9428, 9570, 9618, 9798, and 9907 for 24-plex; and 8979, 9294, 9387, 9588, and 9743 for 32-plex.

Next, kinship metrics were calculated to validate the sensitivity and specificity of the algorithms described herein using these high-complexity profiles. The calculated kinship metrics included the genome-wide kinship coefficient, shared cM, longest peak cM, and likelihood ratios for all combinations of members within the pedigree. Most members of this family were related, with only five pairs of individuals being unrelated. To increase the number of unrelated controls and best simulate databases used in missing persons cases (which consist mostly of defined, simulated antemortem samples from victims' family members), we included 100 randomly selected samples from the 1000 Genomes Project. Genotypes were downloaded from the International Genome Sample Resource database (Fairley et al., Nucleic Acids Res 2020, 48, D941-D947, doi:10.1093/nar/gkz836, the contents of which are incorporated herein by reference in their entirety) and filtered to include only loci within the 10,230 SNP panel of the ForenSeq Kintelligence Kit (Verogen, Inc.).

As demonstrated above, a plexity of 12 samples per run was determined to be sufficient to call sufficient SNPs from simulated postmortem (PM) samples with degraded and/or low DNA content. To simulate PM vs. antemortem (AM) comparisons as performed for DVI, the 12-sample run samples were considered PM samples. Each PM sample profile was then paired with other sample profiles from all sequencing runs (12, 16, 24, and 32 samples per run) and each of 100 unrelated profiles from the 1000 Genomes Project. Genome-wide relatedness coefficients, shared cM, longest segment cM, and likelihood ratios were calculated for each pair to identify related pairs. The Utah/CEPH 1463 family included grandparents, parents, and siblings. Each unique graph represents a comparison of a 12-sample run versus itself (12_vs_12), 16-sample (12_vs_16), 24-sample (12_vs_24), and 32-sample (12_vs_32) runs to simulate postmortem versus antemortem comparisons and determine the highest plexity possible to maintain accurate identification of close relatedness of antemortem samples. Samples were paired, and the coefficient of kinship and logLR were calculated for each pair. For the kinship analysis of the Utah/CEPH 1463 family, 100 randomly selected samples beginning with the prefix HG were included from the 1000 Genomes Project (Fairley et al., Nucleic Acids Res 2020, 48, D941-D947, doi:10.1093/nar/gkz836, the contents of which are incorporated herein by reference in their entirety) to serve as unrelated controls. Genotypes were downloaded from the International Genome Sample Resource database and filtered to include only loci within the 10,230 Kintelligence SNP panel. Pairs were considered related if the kinship coefficient was greater than 0.031; otherwise, they were unrelated, as represented by the vertical black line in Figure 25A. If the logLR is greater than 0, the pair is considered related; otherwise, it is unrelated, represented by the black vertical line in Figure 25B. Samples include samples from grandparents (G), parents (P), siblings (S), unrelated controls (U), unrelated grandparents (GU), unrelated parents (PU), and unrelated siblings (SU). As shown in Figures 25A and 25B, the relatedness coefficient and logLR threshold were sufficient to distinguish related pairs from unrelated pairs.

Because the minimum SNP overlap was >8,900 among samples sequenced at all plexities, the shared cM and longest segment cM were determined using the windowed kinship algorithm as described above. Using the whole-genome algorithm described above to determine relationships, the coefficients of kinship were well above 0.031 for all predicted related pairs and below 0.031 for all predicted unrelated pairs. Furthermore, because this family has relationships up to the second degree, both windowed kinship and whole-genome kinship performed equally well.

Genome-wide kinship coefficients clearly distinguished each predicted relationship, with no false-positive relationships detected (Figure 25A). As shown in Figure 25A, all unrelated pairs fell below the 0.031 threshold for kinship coefficients, while all related pairs were well above the 0.031 threshold for kinship coefficients, thereby clearly distinguishing between related and unrelated pairs. Considering that all comparisons were performed within small databases, such as those used in the DVI case, containing a small number of unrelated individuals, all measures of kinship were able to accurately confirm all relationships from the Utah/CEPH 1463 family, even when comparing profiles generated at run times of 12 samples with profiles generated at run times of 32 samples. Likelihood ratios showed a similar trend, with all predicted related pairs correctly identified as related by having positive values above the 0 threshold, and all unrelated pairs classified as unrelated by having negative values below the 0 threshold (Figure 25B). These data demonstrate that at a maximum plexity of 32 samples per run for the simulated AM library, profiles contain sufficient SNP data to accurately ascertain relationships up to the second degree and exclude unrelated pairs, including when analyses are performed on small databases with few unrelated individuals, such as those used in missing persons and DVI cases.

Next, as described above and shown in Figure 26, related pre-mortem private family ("relative" or RF) samples were used to test higher-order relationships, including first-, second-, third-, fourth-, and fifth-degree relationships, as shown in Figure 26. The related private family included the self-sample and individuals with specific relationships to the self-sample, including parents (father and mother), aunts/aunts, cousins, children of cousins (1C1R), and second cousins ( ^2nd cousins) (Figure 26). These individuals labeled as related in Figure 26 were sequenced and included in the following kinship analysis.

Three replicate libraries were generated for each of the eight family members, along with several positive and negative amplification controls, for a total of 30 samples sequenced in the run.

Replicates from the self (RF016) and maternal (RF004) DNA were subjected to artificial thermal degradation to mimic PM samples. Specifically, samples were subjected to 21 cycles of heating and cooling at 98°C for 1 hour, followed by 4°C for 10 minutes. Degradation indices were measured, yielding DIs of 2.1, 2.6, 5.1, and 20 for the four maternal (RF004) replicates and 1.5, 2.0, 2.2, and 2.9 for the four self (RF016) replicates. Libraries were generated from these degraded samples as well as intact self and maternal DNA samples (50 pg). The degraded and low-input samples were sequenced at 12 samples per run (12-plex), whereas the intact samples were sequenced at 30 plex. The number of classified SNPs was significantly lower and more widely distributed than the mock PM samples sequenced at 12 plex, showing a higher number of classified SNPs with a tighter distribution (Figure 27A). The minimum, first quartile, median, third quartile, and maximum for these samples were 3256, 8106, 8391, 8580, and 8896 for the degraded/low-input 12-plex run; and 1470, 5057, 7807, 8782, and 9898 for the intact 30-plex run (Figure 27A).

Furthermore, mock AM sample profiles, libraries generated from intact DNA, had fewer classified SNPs compared to mock AM sample profiles generated from commercially available DNA samples sequenced at similar plexities (Figure 27A compared to Figure 20B and Figure 24). The average percent difference in heterozygosity between the intact mock AM maternal (RF004) sample sequenced at a plexity of 30 and the corresponding degraded/low DNA input mock PM maternal (RF004) sample sequenced at a plexity of 12 was 14.6%, with a minimum difference of 1.6% and a maximum difference of 25.5%.

Head-to-head comparisons were performed by pairing each degraded/low-input simulated PM sample with each intact simulated AM sample and calculating the whole-genome relatedness coefficient, shared cM, longest segment cM, and likelihood ratio. All relationships up to and including the third degree of kinship were determined with 100% sensitivity and 100% specificity using both kinship algorithms described herein. Eight of the 30 possible fourth-degree relationships were confirmed using the windowed kinship algorithm described herein, whereas only four of the 30 possible fourth-degree relationships were confirmed using the whole-genome algorithm described herein. However, no fifth-degree relationships passed the relatedness threshold using either algorithm. Thirty-nine of all possible pairs of samples were unrelated within this dataset, and no false-positive relationships were detected. Nineteen paired samples overlapped with fewer than 2,000 SNPs, 18 of which were mock PM sample mothers (RF004) with a DI of 2.6 and 7.2% heterozygosity, and one of which was a mock PM sample self (RF0016) with 50 pg of input DNA and 15.4% heterozygosity. Of these 19 pairs, seven were first-degree, three were second-degree, and three were third-degree related, all with a coefficient of kinship greater than 0.031. Three pairs were self-to-self with a high coefficient of kinship. These results confirm that the methods described herein can be used to determine first- to third-degree relatedness for mock PM and AM sample libraries sequenced with high plexity and high specificity. An example of the whole-genome relatedness coefficients and corresponding log-likelihood ratios from one degraded simulated PM sample (self, RF016) paired with an intact self sample, with a DI of 2.9, and simulated AM samples including a known mother (first degree), a known aunt/aunt (second degree), a known cousin (third degree), a child of a known cousin (fourth degree), and a known second cousin (fifth degree), is shown in Figure 27B. The relatedness of the self (RF016) (degradation index 2.9) simulated postmortem sample to the intact simulated antemortem (AM) sample was determined using a whole-genome algorithm. The horizontal dotted line depicted in Figure 27B represents a relatedness coefficient threshold of >0.031. The SNP overlap was averaged at 7400 across the six pairs, with a minimum of 7061 and a maximum of 7755 (Figure 27B). Additionally, the log-likelihood ratios for self, maternal, aunt/aunt, first cousin, child of first cousin, and second cousin were 167, 94.5, 26.1, 8.9, 2.2, and 0.4, respectively. All relationships except for child of first cousin and second cousin (fourth and fifth degrees of kinship) passed the 0.031 threshold required for the windowed kinship and whole genome algorithms using the methodology described herein, thereby ensuring the validity of using these algorithms to ascertain first-, second-, and third-degree relatives using the methods described herein.
C. Consideration

Disaster victim identification (DVI) using DNA typically consists of examining highly polymorphic regions of the genome, specifically autosomal short tandem repeats (STRs), STRs on the Y chromosome, and mitochondrial DNA (mtDNA) (Alonso et al., Croat Med J 2005, 46, 540-548; Ambers et al., Int J Legal Med 2018, 132, 1545-1553; Alvarez-Cubero et al., Pathobiology 2012, 79, 228-238; Waterston et al., Forensic Sci Int Genet 2018, 37, 270-282; Prinz et al., Forensic Sci Int Genet 2007, 1, 3-12, the contents of which are incorporated herein by reference in their entirety). STR markers have been used to identify remains in several mass fatality incidents (MFIs), but can only provide enough information to confirm first- and second-degree relationships and can result in a high false-positive rate (Alonso et al., Croat Med J 2005, 46, 540-548; Alvarez-Cubero et al., Pathobiology 2012, 79, 228-238; Birus et al., Croat Med J 2003, 44, 322-326; Brenner et al., Theor Popul Biol 2003, 63, 173-178; Graham et al., Forensic Sci Med Pathol 2006, 2, 203-207 (the entire contents of which are incorporated herein by reference). The development of next-generation sequencing (NGS) assays for STR analysis has overcome some of the obstacles to utilizing STRs for DVI (Alvarez-Cubero et al., Ann Hum Biol 2017, 44, 581-592; Zavala et al., Impact of DNA degradation on massively parallel sequencing-based autosomal STR, iiSNP, and mitochondrial DNA typing systems, 2019; Senst et al., Forensic Science International: Genetics 2023, 62; Senst et al., J Forensic Sci 2022, 67, 1382-1398; Ambers et al., BMC Genomics 2016, 17, 750 (the contents of which are incorporated herein by reference in their entireties). The mtDNA control region, or hypervariable region, or the entire mitochondrial genome (mtGenome) has also been used to identify remains (Holland et al., Mitochondrial DNA Sequence Analysis - Validation and Use for Forensic Casework. Forensic science review 1999; Holland et al., Croat Med J 2003, 44, 264-272, the contents of which are incorporated herein by reference in their entirety). The mtGenome has a high copy number and is a circular genome; therefore, there is a higher chance of recovery of mtDNA compared to fragmented nuclear DNA in aged remains (Amorim et al., PeerJ 2019, 7, e7314, doi:10.7717/peerj.7314, the contents of which are incorporated herein by reference in their entirety). However, mtDNA classifies maternal lineages, which can be problematic when multiple family members are affected by MFI or when only paternal relatives are available for kinship analysis (Zavala et al., Impact of DNA degradation on massively parallel sequencing-based autosomal STRs, SNPs, and mitochondrial DNA typing systems, 2019; Holland et al., Mitochondrial DNA Sequence Analysis - Validation and Use for Forensic Casework. Forensic science). review 1999; Holland et al., Croat Med J 2003, 44, 264-272; Amorim et al., PeerJ 2019, 7, e7314, doi:10.7717/peerj.7314. Conversely, single nucleotide polymorphisms (SNPs) can be used to determine either paternal or maternal lineages or both (Waterston et al., Forensic Sci Int Genet 2018, 37, 270-282; Amorim et al., Forensic Sci Int 2005, 150, 17-21; Gorden et al., Forensic Sci Int Genet 2022, 57, 102636). Furthermore, in PCR-based assays, SNP amplicons tend to be shorter than STR amplicons and are more likely to be amplified in compromised samples (Waterston et al., Forensic Sci Int Genet 2018, 37, 270-282; Zavala et al., Impact of DNA degradation on massively parallel sequencing-based autosomal STR, SNP, and mitochondrial DNA typing systems. 2019; Senst et al., J Forensic Sci 2022, 67, 1382-1398; Ambers et al., BMC Genomics 2016, 17, 750 (the contents of which are incorporated herein by reference in their entirety). Also, when a large number of SNPs are investigated, kinship and especially likelihood ratio values are more discriminatory than STRs (Turner et al., Front Genet 2022, 13, 882-268; Cho et al., Transfus Med Hemother 2016, 43, 429-432; Tillmar et al., Genes 2021, 12, 1968). Although STRs are more polymorphic than SNPs (Devesse et al., Forensic Sci Int Genet 2018, 34, 57-61; Phillips et al. , ELECTROPHORESIS 2018, 39, 2708-2724; Gettings et al. , Forensic Science International: Genetics 2016, 21, 15-21; Novroski et al. , Forensic Sci Int Genet 2016, 25, 214-226; Wendt et al. , Forensic Sci Int Genet 2017, 28, 146-154; Delest et al. , Forensic Sci Int Genet 2020, 47, 102304 (the contents of which are incorporated herein by reference in their entireties), SNP assays examine hundreds to thousands of data points compared to tens of data points using STR. High density SNP data can reduce the false positive rate (Snedecor et al., Forensic Sci Int Genet 2022, 61, 102769 (the contents of which are incorporated herein by reference in their entireties)). Finally, SNPs have previously been used in the context of degraded DNA and have proven to provide sufficient discriminatory power to identify remains (Snedecor et al., Forensic Sci Int Genet 2022, 61, 102769; Gorden et al., Forensic Sci Int Genet 2022, 57, 102636; Marshall et al. al., Genes (Basel) 2020, 11, doi:10.3390/genes11080938 (the contents of which are incorporated herein by reference in their entirety).

The ForenSeq Kintelligence Library Prep Kit® is a library prep kit for forensic genetic genealogy (Kling et al., Forensic Sci Int Genet 2021, 52, 102474; Snecedor et al., Forensic Science International: Genetics 2022, 61, 102769; Peck et al., Internal Validation of the ForenSeq Kintelligence Kit for Application to Forensic Genetic Geneology.bioRxiv 2022, 2022.2010.2028.514056, doi:10.1101/2022.10.28.514056; Verogen. ForenSeq Intelligence Kit Datasheet: Document # VD2020054. Rev. A. 2021. Available online: https://verogen. com/wp-content/uploads/2021/03/forenseq-kintelligence-datasheet-vd2020054-a. pdf; Verogen. High-Quality Outputs from Low-Quality Samples with ForenSeq Kinterlingence Application Note: Document # VD2021002 Rev. B. 2021.2021 (the entire contents of which are incorporated herein by reference) contains a set of 10,230 forensically relevant SNPs that can be used to solve violent crime and missing persons cases. ForenSeq Kintelligence has been used to resolve cases of unidentified remains in Oregon (International, D.L. DNA Labs International is the first certified laboratory to use the ForenSeq Kintelligence System to assist in the identification of unidentified remains. 2022). Kintelligence results were analyzed using GEDMatch PRO or FamilyTreeDNA (Verogen and Gene by Gene Form Groundbreaking Partnership to Accelerate Adoption for Forensic Investigational Genetic Genetics) to search for unknown relatives available in the database. Geneology. Available online: https://www.businesswire.com/news/home/20220815005116/en/Verogen-and-Gene-by-Gene-Form-Groundbreaking-Partnership-to-Accelerate-Adoption-of-Forensic-Investigative-Genetic-Genetics. The algorithm utilized in GEDMatch PRO was specifically designed to work with the 10,230 SNPs in the Kintelligence kit, but uploading to a public database for relative searching is required, which requires a minimum of 6,000 SNPs typed in the sample. Additionally, the current configuration of the Kintelligence kit allows for up to three samples to be sequenced at once on the Miseq FGx, ensuring that enough SNPs are typed for GEDMatch PRO upload, but also reducing cost-effectiveness for MFI cases where distant relationships are undesirable. Furthermore, if relatives are known but not present in one of the two databases, matching postmortem (PM) samples require known relatives to upload their profiles to the database.

To support the use of library preparation kits and similar methodologies for nucleic acid library generation, such as the ForenSeq Kintelligence Library Prep Kit®, for DVI in a higher-throughput and cost-effective manner, libraries were sequenced at a plexity exceeding the recommended plexity of three libraries per sequencing run. The goal was to maximize plexity for both antemortem (AM) and postmortem (PM) samples while maintaining accuracy in confirming relationships up to the third degree.

The optimal sequencing plexity for determining relationships up to three degrees was 12 simulated PM libraries or 32 simulated AM libraries per sequencing run, based on simulations (Figure 20A) and sequencing simulated AM and PM samples on the MiSeq FGx (Figures 20B, 20C, 24, and 27A). Although SNP alleles and loci fell below AT for library sequencing at higher plexities compared to the recommended plexity of three samples per sequencing run (Figures 21A and 21B), sufficient loci were commonly classified between simulated AM and PM samples to allow determination of relationships up to three degrees, even for highly degraded and low-DNA-input samples (Figures 22, 25A, 25B, and 27B).

Analyses using relative frequency (RF) confirmed the confirmation of relationships up to the third degree, in addition to confirming some fourth-degree relationships (Figure 27B). SNP overlap between samples with fourth-degree relationships ranged from 1,356 to 7,872 SNPs. Of the 30 possible pairs, eight pairs with overlaps of more than 7,000 SNPs did not pass the threshold for determining relatedness. If the quality of the PM samples is good enough to maintain a high SNP call rate, fourth-degree relationships can be determined but may not pass the threshold (Figures 22A-22D).

As noted in the studies in Figures 21A and 21B, the decreased SNP call rate and increased allele dropout observed in simulated PM analyses, and in the study in Figure 27A using pedigrees like those shown in Figure 26, the overlap of loci called between PM and AM samples decreases as the quality of PM DNA decreases in analyses of degraded kinship samples. This overlap significantly impacts the performance of kinship algorithms, reducing sensitivity at higher degrees of relatedness (Figures 22A-22D). Therefore, to prevent further loss of loci, the number of SNPs classified in AM samples must be maximized. If more distant relationships are expected in MFI searches and PM samples show low SNP call rates, these libraries can be resequenced at a lower plexity to ensure sufficient SNPs are called.

High-throughput simulations performed in the context of Figure 20A and actual high-throughput analyses performed in the context of Figures 20B and 20C demonstrated that increasing plexity reduces the number of classified SNPs. Higher levels of degradation and lower amounts of DNA for PM samples further impact the number of classified SNPs. As demonstrated in a study that sampled de-identified relatives from the GEDMatch database and determined relatedness by randomly reducing the number of SNPs to 8,000, 6,000, 4,000, and 2,000, whole-genome relatedness (using relatedness coefficients to confirm relatedness instead of shared cM) could confirm up to the third degree of kinship with high levels of specificity and sensitivity (Figures 22A-22D). Lowering the relatedness coefficient threshold improves sensitivity (number of possible matches); however, specificity is compromised, resulting in more false positives (number of false matches referred to as true matches). The threshold used in this study yielded higher specificity (nearly 100%) and prioritized the loss of true positives to avoid an increase in false positives. Furthermore, these lower SNP call rates make it difficult to confirm more distant relationships, such as those between the fourth and fifth degrees. Both the whole-genome kinship algorithm and the windowed kinship algorithm are sensitive to the number of SNPs classified between the two individuals tested. In kinship analyses involving both degraded and low-input samples, we were able to confirm all relationships, including those up to the third degree. However, because the average number of classified SNPs was too low to reference windowed kinship (less than 6,000) for fourth- and fifth-degree relationships, we were unable to utilize the more specific windowed kinship algorithm for these more distant relationships. The whole-genome kinship algorithm is not as specific or sensitive as the windowed kinship algorithm in confirming fourth- and fifth-degree relationships.

Heterozygosity is another measure of data loss, and if it is too low, it can affect the accuracy of likelihood ratios and kinship values. Loss of heterozygosity occurs due to high degradation or low amounts of DNA in the sample. Even with degraded samples, which exhibited the 7.2% heterozygosity observed in the kinship samples, all first-, second-, and third-degree relationships were captured. Fourth-degree relationships were determined using mock PM samples, which exhibited 22.6% heterozygosity. Given the level of locus loss observed in degraded and low-input samples, increasing the number of samples that can be sequenced together to 12 for degraded and/or low-DNA input samples and to 32 for AM samples is expected to capture all relationships, including those up to and including third-degree relationships.

If PM samples are expected to represent fourth- or fifth-degree relatives, maximizing SNP overlap by reducing the number of samples sequenced together is recommended, especially if the input is low and/or the samples are highly degraded. Alternatively, if PM samples are expected to represent first-, second-, or third-degree relatives, co-sequencing a larger number of samples (up to 12), even degraded and/or low-input samples, should not affect the ability of the co-sequencing algorithm to confirm first- to third-degree relationships. Alternatively, for AM samples, as described above, the number of classified SNPs should be maximized. However, it has been observed that up to 32 co-sequenced samples can confirm all relationships up to and including third-degree relatives using both the described kinship algorithm and likelihood ratio.

In conclusion, sequencing libraries at high multiplexing using the methodology described herein improves the cost-effectiveness of relationship confirmation by increasing the multiplexing from 3 to 12 for PM samples and up to 32 for AM samples. The highly tuned thresholds set in the algorithm and the large panel of SNPs reduce the false positive rate in relationship confirmation to zero, while confirming all relationships up to the third degree (e.g., cousins or great-grandparents) with perfect sensitivity and specificity. Installed on a private server with additional methods for likelihood ratio calculation, these relationship algorithms function as a private database with the ability to find relatives and accurately determine MFI relationships combined with the cost-effectiveness of higher multiplexed sequencing, and are a solution to DVI.

The present invention is not intended to be limited in scope to the particular disclosed embodiments, which are provided, for example, to illustrate various embodiments of the invention. Various modifications to the compositions and methods described will become apparent from the descriptions and teachings herein. Such variations can be made without departing from the true scope and spirit of the present disclosure and are intended to be included within the scope of the present disclosure.

Claims (215)

1. A method for performing DNA-based kinship analysis, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
generating a nucleic acid library from the amplification products;
sequencing the nucleic acid library generated from the amplification products;
analyzing the sequence of the amplification product;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
calculating a degree of relatedness between said DNA profile and one or more reference DNA profiles, said one or more reference DNA profiles being included in a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of said person of interest;
A method comprising: 1. A method for performing DNA-based kinship analysis, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
generating a nucleic acid library from the amplification products;
sequencing the nucleic acid library generated from the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
calculating a degree of relatedness between said DNA profile and one or more reference DNA profiles, said one or more reference DNA profiles being included in a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of said person of interest;
A method comprising: The method of claim 1 or claim 2, wherein the sequencing is performed using massively parallel sequencing (MPS). The method of any one of claims 1 to 3, wherein the sequencing does not include whole genome sequencing (WGS). The method of any one of claims 1 to 4, further comprising generating a family tree comprising the DNA profile associated with one or more DNA profiles. 1. A method for constructing a nucleic acid library for a person of interest, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions;
A method comprising: The method of claim 6, further comprising sequencing the amplification products to produce a DNA profile of the person of interest. 1. A method for constructing a nucleic acid library for a reference DNA sample, comprising:
providing nucleic acid samples from relatives of the person of interest;
amplifying the nucleic acid sample with a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions;
A method comprising: The method of claim 8, wherein the relative is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest. The method of claim 8 or claim 9, wherein the relative is a first-, second-, or third-degree relative of the person of interest. The method of any one of claims 1 to 10, wherein the nucleic acid sample contains genomic DNA. The method of any one of claims 1 to 11, wherein the nucleic acid sample contains one or more enzyme inhibitors. The method of claim 12, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. The method of any one of claims 1 to 13, wherein the nucleic acid sample contains low-quality nucleic acid molecules and/or low-abundance nucleic acid molecules. The method of claim 14, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA. The low-quality nucleic acid molecule has a degradation index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least 1, 16. The method of claim 14 or claim 15, wherein the composition has a Deterioration Index (DI) of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. The method of claim 14 or claim 15, wherein the low-quality nucleic acid molecules have a DI of at least 1 and at most 158.3 or less. The method of any one of claims 1 to 13, wherein the nucleic acid sample contains high-quality nucleic acid molecules. The method of claim 18, wherein the high-quality nucleic acid molecules have a DI of less than 1. The method of any one of claims 1 to 19, wherein the person of interest is a missing person. The method of any one of claims 1 to 19, wherein the person of interest is a victim of a disaster or conflict. The method of any one of claims 1 to 21, wherein the nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bone, or skin. The method of claim 22, wherein the nucleic acid sample is derived from saliva, blood, or semen. The method of claim 22, wherein the nucleic acid sample is derived from bone or hair. The method of any one of claims 1 to 21, wherein the nucleic acid sample is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, semen, or other bodily fluid, or containing hair or skin cells. The method of any one of claims 1 to 25, wherein the nucleic acid sample contains 3 pg to 100 ng or approximately 3 pg to 100 ng of genomic DNA. The method of any one of claims 1 to 26, wherein the nucleic acid sample contains between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA. The method of claim 26 or claim 27, wherein the nucleic acid sample contains 1 ng or about 1 ng of genomic DNA. The method of any one of claims 1 to 28, wherein the plurality of SNPs includes kinship SNPs (kiSNPs). The method of any one of claims 1 to 29, wherein the plurality of SNPs includes Y chromosome SNPs (Y-SNPs). The method of any one of claims 1 to 30, wherein the plurality of SNPs includes kiSNPs and Y-SNPs. The method of any one of claims 1 to 31, wherein the plurality of SNPs includes kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs). The method of any one of claims 1 to 28, wherein the plurality of SNPs includes SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. The method of any one of claims 1 to 33, wherein at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are related SNPs. The method of any one of claims 1 to 34, wherein the reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. The method of any one of claims 1 to 35, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles within the reference set of DNA profiles are from relatives of the person of interest. The method of any one of claims 1 to 36, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from first-, second-, third-, fourth-, or fifth-degree relatives. The method of any one of claims 1 to 37, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. The method of any one of claims 36 to 38, wherein each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. The method of claim 39, wherein each relative of the person of interest in the reference set of DNA profiles is a first-degree, second-degree, or third-degree relative of the person of interest, respectively. The method of any one of claims 1 to 40, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known. The method of any one of claims 1 to 41, wherein the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known. The method of any one of claims 1 to 42, wherein the reference set of DNA profiles is in a database. The method of claim 43, wherein the database is not publicly accessible. The method of any one of claims 1 to 44, wherein the sequencing comprises a sequencing complexity of up to 40-plex. The method of any one of claims 1 to 44, wherein the sequencing comprises a sequencing complexity of up to 32-plex. The method of any one of claims 1 to 44, wherein the sequencing comprises a sequencing complexity of 12-plex to 32-plex. The method of any one of claims 1 to 44, wherein the sequencing comprises a sequencing complexity of 24-plex to 32-plex. The sequencing may be 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex, or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex The method of any one of claims 1 to 44, comprising a sequencing complexity of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. The method of any one of claims 1 to 49, wherein the sequencing comprises a sequencing complexity of 8-16 plex or about 8-16 plex for postmortem samples, and/or the sequencing comprises a sequencing complexity of 24-40 plex or about 24-40 plex for antemortem samples. The method of any one of claims 1 to 50, wherein the sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and/or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples. The method of any one of claims 1 to 51, wherein the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex, or about 30-plex, 31-plex, or 32-plex. The method of any one of claims 1 to 52, further comprising verifying the identity of the person of interest. 1. A method for calculating relatedness, comprising:
obtaining a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest;
and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from blood relatives of the person of interest. 1. A method for calculating relatedness, comprising:
generating a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest;
and calculating a degree of relatedness between the DNA profile and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference set of DNA profiles that includes one or more reference DNA profiles from blood relatives of the person of interest. The method of any one of claims 1 to 55, wherein the degree of relatedness is calculated using a kinship model. The method of any one of claims 1 to 56, wherein the degree of relatedness is calculated using a kinship model trained using a PCA method. The method of claim 57, wherein the PCA method for training the kinship model is or includes PCA. The method of claim 57 or 58, wherein the PCA method is PC-AiR. 59. The method of claim 59, wherein the PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally in a training DNA profile, where pairings with a relatedness coefficient >0.025 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating it as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to samples in the unrelated sample set, thereby designating it as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i). The method of claim 57 or claim 58, wherein the PCA method is modified PC-Air. 62. The method of claim 61, wherein the modified PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples of training DNA profiles, as appropriate, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i). The method of any one of claims 1 to 62, wherein calculating the degree of relatedness includes calculating the coefficient of relatedness using PC-Relate. The method of claim 63, wherein the degree of relatedness is calculated by providing the DNA profile of the person of interest as input to PC-Relate. The method of any one of claims 57 to 64, wherein the relatedness is calculated by providing the kinship model and the DNA profile of the person of interest as inputs to PC-Relate. The method of any one of claims 63 to 65, wherein the one or more reference DNA profiles are further provided as input to PC-Relate. Calculating the degree of relatedness includes calculating a coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
67. The method of any one of claims 1 to 66, wherein i and j are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively. The method of any one of claims 1 to 67, wherein calculating the relevance includes calculating a likelihood ratio. The method of claim 68, wherein calculating the likelihood ratio comprises comparing the plurality of SNPs between the DNA profile and the one or more reference DNA profiles. The method of claim 68, wherein calculating the likelihood ratio comprises comparing a set of SNPs including kinship SNPs from among the plurality of SNPs between the DNA profile and the one or more reference DNA profiles. The method of any one of claims 68 to 70, wherein calculating the likelihood ratio comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles are related based on the genotypes of the plurality of SNPs by the probability that the DNA profile and the reference DNA profile are unrelated. The likelihood ratio (LR) is calculated as follows:
72. The method of any one of claims 68 to 71, wherein D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated. The LR is calculated as follows:
72. The method of any one of claims 68 to 71, wherein 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2. The method of any one of claims 1 to 73, wherein the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles. The method of claim 74, wherein calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs, including one or more Y-SNPs, between the DNA profile and the one or more reference DNA profiles. The method of claim 75, wherein the one or more Y-SNPs are included within the plurality of SNPs. The method of claim 75, wherein the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. The method of any one of claims 75 to 77, wherein the one or more Y-SNPs include 85 Y-SNPs. The method of any one of claims 75 to 78, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome, based on the genotypes of the one or more Y-SNPs, by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome. The method of any one of claims 1 to 79, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of disaster or conflict. The method of any one of claims 1 to 80, wherein each of the DNA profiles in the reference set of DNA profiles is from a relative of a missing person or a victim of a disaster or conflict. The method of any one of claims 1 to 81, wherein the reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. The method of any one of claims 1 to 82, wherein the reference set of DNA profiles includes up to 100 reference DNA profiles. The method of any one of claims 1 to 83, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles within the reference set of DNA profiles are from relatives of the person of interest. The method of any one of claims 1 to 84, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. The method of claim 84 or claim 85, wherein each relative of the person of interest in the reference set of DNA profiles is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. The method of any one of claims 1 to 86, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known. The method of any one of claims 1 to 87, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known. The method of any one of claims 1 to 88, wherein the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known. The method of any one of claims 1 to 89, wherein the reference set of DNA profiles is in a database. The method of claim 90, wherein the database is not publicly accessible. The method of claim 90 or claim 91, wherein the database is not accessible by a third-party genealogy service. A nucleic acid library constructed using the method of any one of claims 6 to 92. A plurality of primers that specifically hybridize to a plurality of target sequences comprising at least 2,000 to 50,000 or approximately 2,000 to 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from a person of interest, wherein amplification products are obtained when the nucleic acid sample is amplified using the plurality of primers in one or more multiplex PCR reactions. a plurality of primers that specifically hybridize to a plurality of target sequences comprising between at least or between about 2,000 and 50,000 single nucleotide polymorphisms (SNPs) in a nucleic acid sample from the person of interest and one or more reference samples;
the one or more reference samples comprise samples from relatives of the person of interest;
A plurality of primers, wherein amplifying said nucleic acid sample from said person of interest and said nucleic acid sample from one or more reference samples using said plurality of primers in one or more multiplex PCR reactions results in amplification products. The plurality of primers described in claim 94 or claim 95, wherein the nucleic acid sample from the person of interest comprises genomic DNA. The plurality of primers described in any one of claims 94 to 96, wherein the nucleic acid sample from the person of interest contains one or more enzyme inhibitors. The plurality of primers described in claim 97, wherein the one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. The plurality of primers described in any one of claims 94 to 98, wherein the nucleic acid sample from the person of interest contains low-quality nucleic acid molecules and/or low-abundance nucleic acid molecules. The plurality of primers described in claim 99, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA. The low-quality nucleic acid molecules have a Degradation Index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least 1, 2, 3, The plurality of primers of claim 99 or 100, having a Degradation Index (DI) of 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. The plurality of primers described in any one of claims 99 to 101, wherein the low-quality nucleic acid molecules have a DI of at least 1 and a maximum of 158.3 or less. The plurality of primers described in any one of claims 94 to 102, wherein the nucleic acid sample from the person of interest and/or the nucleic acid sample from one or more reference samples contains high-quality nucleic acid molecules. The plurality of primers described in claim 103, wherein the high-quality nucleic acid molecules have a DI of less than 1. The plurality of primers described in any one of claims 94 to 104, wherein the person of interest is a missing person. The plurality of primers described in any one of claims 94 to 104, wherein the person of interest is a victim of a disaster or conflict. The plurality of primers described in any one of claims 94 to 106, wherein the nucleic acid sample from the person of interest is derived from a buccal swab, paper, fabric, or other substrate or object impregnated with saliva, blood, or other bodily fluid, or containing hair or skin cells. The plurality of primers described in any one of claims 94 to 107, wherein the nucleic acid sample from the person of interest contains 3 pg to 100 ng or about 3 pg to 100 ng of genomic DNA. The plurality of primers described in any one of claims 94 to 108, wherein the nucleic acid sample from the person of interest comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA. The plurality of primers described in claim 108 or claim 109, wherein the nucleic acid sample from the person of interest comprises 1 ng or about 1 ng of genomic DNA. The plurality of primers described in any one of claims 94 to 110, wherein the plurality of SNPs includes kinship SNPs (kiSNPs). The plurality of primers described in any one of claims 94 to 111, wherein the plurality of SNPs includes Y chromosome SNPs (Y-SNPs). The plurality of primers described in any one of claims 94 to 112, wherein the plurality of SNPs includes kiSNPs and Y-SNPs. The plurality of primers described in any one of claims 94 to 113, wherein the plurality of SNPs includes kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs). The plurality of primers described in any one of claims 94 to 111, wherein the plurality of SNPs includes SNPs selected from one or more of the group consisting of kiSNP, aiSNP, iiSNP, piSNP, X-SNP, and Y-SNP. The plurality of primers described in any one of claims 94 to 115, wherein at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are, or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are related SNPs. The plurality of primers described in any one of claims 94 to 116, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of disaster or conflict. The plurality of primers described in any one of claims 94 to 116, wherein each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict. The plurality of primers described in any one of claims 94 to 118, wherein the one or more reference samples comprise up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples. The plurality of primers of any one of claims 94 to 119, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest. The plurality of primers of any one of claims 94 to 120, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from first-, second-, third-, fourth-, or fifth-degree relatives. The plurality of primers described in any one of claims 94 to 121, wherein at least 50% of the one or more reference samples are from blood relatives of the person of interest. The plurality of primers described in any one of claims 120 to 122, wherein each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. The plurality of primers described in claim 123, wherein each relative of the person of interest in the one or more reference samples is a first-degree, second-degree, or third-degree relative of the person of interest, respectively. The plurality of primers described in any one of claims 94 to 124, wherein the identity of each relative of the person of interest in the one or more reference samples is known. The plurality of primers described in any one of claims 94 to 125, wherein the identity of each of the one or more reference samples is known. 1. A method for constructing a DNA profile, comprising:
providing a nucleic acid sample from the person of interest;
amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of between at least or about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
sequencing the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile; and
A method comprising: 1. A method for constructing a DNA profile, comprising:
providing a nucleic acid sample from the person of interest;
providing a nucleic acid sample from a relative of the person of interest;
amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relatives using a plurality of primers that specifically hybridize to a plurality of target sequences that collectively comprise a plurality of at least or between about 2,000 and 50,000 single nucleotide polymorphisms (SNPs), thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions;
sequencing the amplification products;
genotyping the plurality of SNPs, thereby generating a DNA profile for the person of interest and the relatives of the person of interest;
A method comprising: The method of claim 127 or claim 128, wherein the sequencing does not include whole genome sequencing (WGS). The method of claim 127 or claim 129, wherein the nucleic acid sample comprises genomic DNA. The method of claim 128 or claim 129, wherein the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative of the person of interest comprises genomic DNA. The method of any one of claims 127 to 131, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative contain one or more enzyme inhibitors. 133. The method of claim 132, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of hematin, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. The method of any one of claims 127 to 133, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative contains low-quality nucleic acid molecules and/or low-abundance nucleic acid molecules. The method of claim 134, wherein the low-quality nucleic acid molecules are degraded and/or fragmented genomic DNA. The low-quality nucleic acid molecule has a Degradation Index (DI) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or at least 1, 2, , 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. The method of any one of claims 134 to 136, wherein the low-quality nucleic acid molecules have a DI of at least 1 and at most 158.3 or less. The method of any one of claims 127 to 133, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative contain high-quality nucleic acid molecules. The method of claim 138, wherein the high-quality nucleic acid molecule has a DI of less than 1. The method of any one of claims 127 to 139, wherein the person of interest is a missing person. The method of any one of claims 127 to 139, wherein the person of interest is a victim of a disaster or conflict. The method of any one of claims 128, 129, and 131-141, wherein the relative of the person of interest is a first-degree, second-degree, third-degree, fourth-degree, or fifth-degree relative. The method of any one of claims 128, 129, and 131-141, wherein the relative of the person of interest is a first-degree, second-degree, or third-degree relative. The method of any one of claims 127 to 143, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the blood relative are derived from a buccal swab, paper, fabric or other substrate or object impregnated with saliva, blood or other bodily fluid, or containing hair or skin cells. The method of any one of claims 127 to 144, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises 3 pg to 100 ng or approximately 3 pg to 100 ng of genomic DNA. The method of any one of claims 127 to 145, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and/or the nucleic acid sample of the relative comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA. The method of claim 145 or claim 146, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and/or the nucleic acid sample of the relative comprises 1 ng or about 1 ng of genomic DNA. The method of any one of claims 127 to 147, wherein the plurality of SNPs includes consanguineous SNPs. The method of any one of claims 127 to 148, wherein the plurality of SNPs includes Y chromosome SNPs (Y-SNPs). The method of any one of claims 127 to 149, wherein the plurality of SNPs includes kiSNPs and Y-SNPs. The method of any one of claims 127 to 150, wherein the plurality of SNPs includes kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotype SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs). The method of any one of claims 127 to 151, wherein the plurality of SNPs includes SNPs selected from one or more of the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. The method of any one of claims 127 to 152, wherein at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are related SNPs. The method of any one of claims 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of up to 40-plex. The method of any one of claims 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of up to 32-plex. A method according to any one of claims 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of 12-plex to 32-plex. A method according to any one of claims 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of 24-plex to 32-plex. The sequencing may be performed in a 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, or 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex or 45-plex, or about 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex wherein the sequencing comprises a sequencing complexity of 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, or 45-plex; or wherein the sequencing comprises a sequencing complexity of 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex or 35-plex, or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex or 35-plex
154. The method of any one of claims 1 to 92 and 127 to 153. The method of any one of claims 1 to 92 and 127 to 158, wherein the sequencing comprises a sequencing complexity of 8-16 plex or about 8-16 plex for postmortem samples, and/or the sequencing comprises a sequencing complexity of 24-40 plex or about 24-40 plex for antemortem samples. The method of any one of claims 1 to 92 and 127 to 158, wherein the sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and/or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples. A method according to any one of claims 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex, or about 30-plex, 31-plex, or 32-plex. 1. A method for identifying genetic relatives of a DNA profile, comprising:
Calculating a degree of relatedness between the DNA profile of any one of claims 127 to 161 and one or more reference DNA profiles, wherein the one or more reference DNA profiles are comprised within a reference set of DNA profiles comprising one or more reference DNA profiles from blood relatives of the person of interest;
generating a pedigree comprising said DNA profile in relation to said one or more reference DNA profiles. The method of claim 162, wherein the one or more reference DNA profiles are part of a database. The method of claim 162 or claim 163, wherein the reference set of DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. The method of any one of claims 162 to 164, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles within the reference set of DNA profiles are from relatives of the person of interest. The method of any one of claims 162 to 165, wherein at least 50% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest. The method of claim 165 or claim 166, wherein each relative of the person of interest is a first-degree, second-degree, third-degree, fourth-degree, or fifth-degree relative of the person of interest, respectively. The method of any one of claims 162 to 167, wherein the identity of each relative of the person of interest in the reference set of DNA profiles is known. The method of any one of claims 162 to 168, wherein the identity of each of the one or more reference DNA profiles in the reference set of DNA profiles is known. The method of any one of claims 162 to 169, wherein the reference set of DNA profiles is in a database. The method of claim 170, wherein the database is not publicly accessible. The method of claim 170 or claim 171, wherein the database is not accessible by a third-party genealogy service. 1. A method for confirming the identity of a DNA profile, comprising:
calculating a degree of relatedness between a DNA profile comprising genotypes of at least or between about 2,000 and 50,000 SNPs and one or more reference DNA profiles, wherein the DNA profile is from a person of interest and the one or more reference DNA profiles are included within a reference set of DNA profiles comprising one or more reference DNA profiles from relatives of the person of interest;
generating a pedigree comprising said DNA profile in relation to said one or more reference DNA profiles. The method of claim 173, wherein the DNA profile is generated by the method of any one of claims 127 to 161. The method of claim 173 or claim 174, wherein the relatedness is calculated using a kinship model. The method of any one of claims 173 to 175, wherein the degree of relatedness is calculated using a kinship model trained using a PCA method. 177. The method of claim 176, wherein the PCA method for training the kinship model is or includes PCA. The method of claim 176 or claim 177, wherein the PCA method is PC-AiR. 178. The method of claim 178, wherein the PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples in a training database, and optionally in a training DNA profile, where pairings with a relatedness coefficient >0.025 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) initializing an unrelated sample set containing all samples; and (3) iteratively: (i) identifying a set in the unrelated sample set that has the most related samples in the unrelated sample set, thereby designating it as X; (ii) identifying a set of samples in X that has the fewest ancestrally diverged pairings compared to samples in the unrelated sample set, thereby designating it as Y; and (iii) terminating the process if Y has 0 samples, or randomly selecting one sample from Y and removing it from U if Y has at least one sample, and repeating beginning with step (3)(i). The method of claim 176 or claim 177, wherein the PCA method is modified PC-Air. 180. The method of claim 180, wherein the modified PC-AiR comprises: (1) estimating relatedness coefficients between all pairs of samples, optionally training DNA profiles, in a training database, where pairings with a relatedness coefficient >0.01 are identified as closely related and pairings with a relatedness coefficient <-0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) ranking all DNA profiles by assigning each DNA profile a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles in the complete database ranked from smallest to largest, broken down by the number of ancestrally diverged DNA profiles in the complete database ranked from largest to smallest. In some embodiments, step (3) includes iterating through the ranked DNA profiles, and for each DNA profile, (i) if the DNA profile is not already in the relevant sample set, adding it to the unrelated sample set and adding all relevant DNA profiles to the relevant sample set, and (ii) if the DNA profile is already in the relevant sample set, skipping to the next DNA profile and repeating starting with step (3)(i). The method of any one of claims 173 to 181, wherein calculating the degree of relatedness includes calculating the coefficient of relatedness using PC-Relate. The method of claim 182, wherein the degree of relatedness is calculated by providing the DNA profile of the person of interest as input to PC-Relate. The method of claim 182 or claim 183, wherein the degree of relatedness is calculated by providing the kinship model and the DNA profile of the person of interest as input to PC-Relate. The method of any one of claims 182 to 184, wherein the one or more reference DNA profiles are further provided as input to PC-Relate. Calculating the degree of relatedness includes calculating a coefficient of relatedness using a whole-genome kinship algorithm as follows:
the reference DNA profiles of the person of interest and the one or more reference DNA profiles are i and j;
is the coefficient of kinship,
is the estimated allele frequency, s is the SNP in S SNPs classified in both individuals,
and
are the numbers of reference alleles at i and j at SNP s, respectively;
and
and j are the expected allele frequencies calculated by PC-AiR for i and j at SNP s, respectively. The method of any one of claims 173 to 186, wherein calculating the relevance includes calculating a likelihood ratio. 188. The method of claim 187, wherein calculating the likelihood ratio comprises comparing the plurality of SNPs between the DNA profile and the one or more reference DNA profiles. 188. The method of claim 187, wherein calculating the likelihood ratio comprises comparing a set of SNPs including related SNPs from among the plurality of SNPs between the DNA profile and the one or more reference DNA profiles. The method of any one of claims 187 to 189, wherein calculating the likelihood ratio comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles are related based on the genotypes of the plurality of SNPs by the probability that the DNA profile and the reference DNA profile are unrelated. The likelihood ratio (LR) is calculated as follows:
191. The method of any one of claims 187 to 190, wherein D represents the genotype, _Hr represents the hypothesis that the individuals are related, and _Hu represents the hypothesis that the individuals are unrelated. The LR is calculated as follows:
191. The method of any one of claims 187 to 190, wherein 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2. The method of any one of claims 173 to 192, wherein the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles. The method of claim 193, wherein the calculating the likelihood ratio of sharing a Y chromosome comprises comparing a set of SNPs, including one or more Y-SNPs, between the DNA profile and the one or more reference DNA profiles. The method of claim 194, wherein the one or more Y-SNPs are included within the plurality of SNPs. The method of claim 194 or claim 195, wherein the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. The method of any one of claims 194 to 196, wherein the one or more Y-SNPs include 85 Y-SNPs. The method of any one of claims 193 to 197, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile and a reference DNA profile from among the one or more reference DNA profiles share a Y chromosome, based on the genotypes of the one or more Y-SNPs, by the probability that the DNA profile and the reference DNA profile do not share a Y chromosome. The method of any one of claims 173 to 198, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference set of DNA profiles are from relatives of missing persons or victims of a disaster or conflict. The method of any one of claims 173 to 199, wherein each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict. The method of any one of claims 173 to 200, wherein the one or more reference samples comprise up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples. The method of any one of claims 173 to 201, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest. The method of any one of claims 173 to 202, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from relatives of the person of interest, and wherein each of the at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference set of DNA profiles are from first-, second-, third-, fourth-, or fifth-degree relatives. The method of any one of claims 173 to 203, wherein at least 50% of the one or more reference samples are from blood relatives of the person of interest. The method of any one of claims 199 to 204, wherein each relative of the person of interest in the one or more reference samples is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest, respectively. The method of claim 205, wherein each relative of the person of interest in the one or more reference samples is a first-degree, second-degree, or third-degree relative of the person of interest, respectively. The method of any one of claims 173 to 206, wherein the identity of each relative of the person of interest in the one or more reference samples is known. The method of any one of claims 173 to 207, wherein the identity of each of the one or more reference samples is known. 127. A kit comprising at least one container means comprising a plurality of the primers of any one of claims 94-126. The plurality of SNPs may be selected from the group consisting of 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, and 7,000 to 11,000 SNPs. SNPs between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs, or about 2,000 to 11,000 SNPs. 00 SNPs, 3,000-11,000 SNPs, 4,000-11,000 SNPs, 5,000-11,000 SNPs, 5,500-11,000 SNPs, 6,000-11,000 SNPs, 7,000-15,000 SNPs, 7,000-14,000 SNPs, 7,000-13,000 SNPs, 7,000-12,000 SNPs, 7,000-11,000 SNPs, 8,000-15,000 SNPs The method of any one of claims 1 to 92 and 127 to 208, comprising SNPs between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs. The method of any one of claims 1 to 92 and 127 to 208, wherein the plurality of SNPs comprises 10,230 SNPs. The plurality of SNPs may be selected from the group consisting of 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, and 7,000 to 11,000 SNPs. SNPs between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs or between 9,000 and 11,000 SNPs, or about 2,000 to 11,000 SNPs. 000 SNPs, 3,000-11,000 SNPs, 4,000-11,000 SNPs, 5,000-11,000 SNPs, 5,500-11,000 SNPs, 6,000-11,000 SNPs, 7,000-15,000 SNPs, 7,000-14,000 SNPs, 7,000-13,000 SNPs, 7,000-12,000 SNPs, 7,000-11,000 SNPs, 8,000-15,000 SNPs The plurality of primers according to any one of claims 94 to 126, comprising between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs. The plurality of primers described in any one of claims 94 to 126, wherein the plurality of SNPs includes 10,230 SNPs. The method of any one of claims 1-5, 11-93, 127-161, and 210-213, further comprising generating a pedigree comprising the DNA profile in relation to one or more DNA profiles contained within the reference set of DNA profiles. The method of claim 214, wherein the pedigree tree includes the DNA profile in relation to one or more DNA profiles from blood relatives of the person of interest.