WO2008039257A2 - Single nucleotide polymorphisms and use of same in selection of dairy cattle - Google Patents

Abstract

Embodiments of present invention provides methods and kits to aid in the selection of dairy parent animals so as to more rapidly introduce and improve desirable milk-related traits. Preferred aspects of the invention are drawn to using one or more animals' allelic composition for one or more loci within the beta2-adrenergic receptor to help determine how to use animals in a breeding program so as most efficiently improve the progeny's economic value; particularly with respect to milk production.

Description

SINGLE NUCLEOTIDE POLYMORPHISMS AND USE OF SAME IN SELECTION OF

DAIRY CATTLE

PRIORITY

[0001 J This application claims the benefit of United States Provisional Application Serial Number 60/813,006, filed June 13, 2006.

INCORPORATION OF SEQUENCE LISTING

[0002] A sequence listing is contained in the file named "ADRB2 Seq Listing.ST25.txt" which is 4096 bytes (4.00 kilobytes) (measured in MS-Windows XP) and was created on June 28, 2007 and is located in computer readable form on a compact disc (in accordance with 37 C.F.R. § 1. 52(e) and 37 C.F.R. § 1. 1.821) is enclosed herewith and incorporated herein by reference.

FIELD OF THE INVENTION

[0003] The invention relates to the field of Animal genomics. More particularly it relates to the field of dairy cattle selection and the improvement of milk production.

BACKGROUND OF THE INVENTION

[0004] Domestic animals are bred to improve their useful attributes. For dairy cattle the primary attribute of interest is milk production, which includes milk volume, milk composition and milking rate. Many, and perhaps most, dairy cows are artificially inseminated using semen from high quality sires in order to improve herd genetics. However, while the overall statistical average of the milking characteristics of the daughters of a particular sire can be measured, it has not been possible to accurately predict the milking characteristics of the daughter offspring produced by breeding a cow with a particular sire. Accordingly, there is a need to better understand the genetic traits that produce desirable milking characteristics in a particular daughter.

[0005] It is known that some milking characteristics are linked with other traits. For example, an association between adrenoceptor concentrations and milkability in primiparous cows was described by Roets, et al. (1986, J. Dairy Sci.). Subsequently Roets, et al. (1989, J. Dairy Sci.) reported on the numbers of alpha2- and beta2-adrenoceptors in teat tissue and blood cells and milkability in primiparous cows. Subsequently, Roets, et al. analyzed the number of alpha2- and beta2-adrenoceptors on the blood cells of bulls and the milkability of their daughters (1995, J. Dairy Res.). Further, Blum et al. reported that treatment with phentolamine (an alpha-adrenergic blocker) and isoproterenol (a beta-adrenoceptor agonist) each facilitated milk removal. Brown et al. raised the possibility that milking speed was correlated with both somatic cell score (SCS) and mastitis. Other studies have also reported correlations between mastitis and SCS (e.g. MacMillan et al., Reneau, and McClelland. A positive correlation between milking speed and SCS was also reported by Boettcher et al.

[0006] Some DNA sequence in and around the bovine beta2-adrenergic receptor (ADRB2) is available in public genome databases (Genbank Acession numbers Z86037, Einspanier et al., AF331034, Schimpf et al.). These sequences include the ADRB2 coding region (1257 bases inclusive from the ATG start codon to the TAA stop codon), as well as 223 bases upstream from the ATG start codon and the 550 bases downstream from the TAA stop codon.

[0007] Nevertheless, methods are needed for efficiently determining which bulls will produce cows with improved milkability and for breeding that more effectively uses genetic information to improve milkability.

SUMMARY OF THE INVENTION

[0008] Various embodiments of the present invention provide methods and products for improving milking characteristics in dairy animals. Certain embodiments of the invention provide for methods of selecting animals for breeding so as to improve milking characteristics. According to some of these embodiments at least one animal being considered as a potential sire or dam is evaluated for its allelic composition at one or more loci. According to preferred aspects of this embodiment genetic samples from one or more animals are analyzed for the allelic composition at one or more loci or single nucleotide polymorphism (SNP) within the beta2-adrenergic receptor (ADRB2) gene. Following analysis of the animal(s) allelic composition at the selected loci, the sire or dam is either selected as more favorable for use to enhance the milking characteristics of its progeny or it is determined to have genetics considered less favorable. Accordingly, the animal is either selected, or not, to act as a sire or dam to serve as the parent of an animal with genetics providing improved milking characteristics.

[0009] Other embodiments of the invention provide methods for breeding an animal to improve milking characteristics by selecting at least one potential parent animal based on its having a favorable allelic composition at a particular locus and then breeding the animal to produce offspring. [0010] The instant invention also provides methods and kits for the detection of allelic variations, including SNPs, that are associated with improved milking characteristics and/or increased net merit.

[001 1 ] Certain embodiments of the instant invention further provide methods for detecting and/or identifying allelic variations including SNPs that are in linkage disequilibrium and/or allelic association with the SNPs specifically recited herein.

Definitions

[0012] The following definitions are provided to facilitate those skilled in the art to fully understand and appreciate the scope of the present invention. As suggested in the definitions provided below, the definitions provided are not intended to be exclusive, unless so indicated. Rather, they are preferred definitions, provided to focus the skilled artisan on various illustrative embodiments of the invention.

[0013] As used herein the term "allelic association" preferably means: nonrandom deviation of f(AiB_j) from the product of f(Aj) and f(B_j), which is specifically defined by r²>0.2, where r² is measured from a reasonably large animal sample (e.g., >100) and defined as

₂ If(A₁B₁) - T(A₁ )F(B₁ )]² r = — ⁱ — (Equation 1 ) f^Xl -ftA^Xf^Xl -fCB, )) where Ai represents an allele at one locus, Bi represents an allele at another locus; f(A|B|) denotes frequency of having both Ai and Bi, f(A|) is the frequency of Ai, f(B|) is the frequency of Bi in a population.

[0014] As used herein the term "allelic composition" is preferably used to describe the complement of alleles or genotype of an animal at a particular locus. Using the Al 1C SNP of the ADRB2 gene as an example, an animal may be either homozygous for the "A" or the "C" allele or it may be heterozygous for the Al 1C allele (this is the animal's allelic composition at that locus).

[0015] As used herein the term "linkage disequilibrium" preferably means allelic association wherein Ai and Bi are present on the same chromosome.

[0016] As used herein the term "milking characteristics" includes phenotypic traits associated with milk production. These include but are not limited to milk composition (including fat yield, fat percentage, protein yield, and protein percent), milk yield, milking rate, somatic cell score, mastitis resistance, productive longevity, daughter pregnancy rate, and indices derived from at least one trait from this list (such as net merit, fluid merit and cheese merit).

[0017] As used herein the term "milk production" preferably refers to: the volume of milk produced, the composition of the milk produced (which includes but is not limited to the fat content, the protein content, and the somatic cell count) and milking rate.

[0018] As used herein the term "milking rate" preferably refers to the speed at which milk is delivered through the teat.

[0019] As used herein the term "net merit" preferably means an index derived from component measurements of cow phenotypes, including milk production and composition, productive life, somatic cell score, size, udder conformation, and feet and legs score.

ILLUSTRATIVE EMBODIMENTS OF THE INVENTION

[0020] The various publications and/or patents referred to throughout this application are each incorporated herein by reference. Complete citations for the scientific publications are set forth in the "References" section of the specification.

[0021 ] Various embodiments of the present invention provide for kits and methods that detect polymorphisms in the bovine genome that are associated with desirable traits. Certain aspects of this embodiment of the invention provide for polymorphisms that are associated with improved milking characteristics and increase net merit of the animals possessing them. Particular aspects of this embodiment of the invention provide kits and methods for detecting variant allelic composition, especially single nucleotide polymorphisms at positions in and around the bovine beta-2-adrenergic receptor (ADRB2) gene. More particularly, the kits and methods provide various means to detect allelic variation at Single Nucleotide Polymorphic loci (SNPs). The methods include, but not limited to direct sequencing, primer extension, PCR-RFLP, and allele-specific hybridization.

[0022] DNA sequence for ADRB2 is available from the Genbank public DNA sequence database, accession numbers Z86037 (Einspanier et al., 1997) and AF331034 (Schimpf et al., 2001). Schimpf et al., further describe 2 sequence polymorphisms within the bovine ADRB2 gene, including the polymorphism defined as +1 143 (G/T) below.

[0023] This invention also provides for any kit(s) and kit components to be used to identify allelic composition at the loci described herein. [0024] More specific aspects of these embodiments of the invention provide for the detection of polymorphic nucleotides (and component alleles) identified as part of the instant invention. These include kits and methods useful to identify SNPs at one or more of the following base pair locations (base-pair location is recited relative to the adenine nucleotide within the start methionine codon of the ADRB2 protein): -732 (AfT), -558 (A/C), -529 (CTT), -408 (AJC), -390 (AfT), -184 (A/C), -85 (A/G), +1 1 (A/C), +41 (C/T), +129 (CfT), +468 (AJG), +957 (CVT), +1027 (CfT), +1 143 (GfT), + 121 1 (C/T), +1251 (AJG), +2266 (AJC), +2345 (AJG), +2408 (AfT), and +2417 (GfT); or any subset thereof.

[0025 ] Preferred embodiments of the invention provide methods for selecting animals as part of a breeding program that comprise evaluating the allelic composition of at least one parent animal at one or more loci. Preferably, the allelic composition for multiple animals is evaluated. Even more preferably multiple animals are evaluated with respect to multiple loci. In a particularly preferred aspect of this embodiment, one or more potential parent animals are evaluated for their allelic composition at one or more locus that comprises a SNP. Once their allelic composition is evaluated, one or more animals having the desired allelic composition may be selected as a potential parent animal. In addition to evaluating and selecting one or more potential parent animal(s) various aspects of this embodiment of the invention provide for either breeding at least one animal, based on its allelic composition or, alternatively, selecting the animal as not suitable for breeding based on its allelic composition. In preferred aspects of these embodiments the loci evaluated are selected from the group of SNPs within or near the ADRB2 gene that consists of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, Cl 21 IT, A 125 IG, A2266C, A2345G, A2408T, and G2417T; or any subset thereof. In especially preferred aspects of these embodiments of the invention one or more SNPs are selected from the group consisting of A-408C, C41T, A468G, and C1027T.

[0026] Other embodiments of the invention provide methods for breeding animals, where the method comprises selecting whether or not at least one potential parent animal is used as a parent based on its allelic composition at one or more loci. Preferably, this selection is made for multiple animals and most preferably the selection is made for the majority of animals within a particular animal population/herd. Even more preferably the selection(s) of the animal(s) is/are based on their allelic compositions at multiple loci. In a particularly preferred aspect of this embodiment, one or more potential parent animals are selected for their allelic composition at one or more locus that comprises a SNP. Once a suitable animal is selected (based on its allelic composition or, alternatively, selecting the animal as not suitable for breeding based on its allelic composition), it is then used in accordance with the breeding method. That is, animals having one or more desirable loci are used for breeding and those animals lacking desirable loci are not used for the breeding method. In preferred aspects of this embodiment of the invention the loci evaluated are selected from the group of SNPs within or near the ADRB2 gene that consists of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, Gl 143T, C 12 HT, A 125 IG, A2266C, A2345G, A2408T, and G2417T; or any subset thereof. In especially preferred aspects of these embodiments of the invention one or more SNPs are selected from the group consisting of A-408C, C41T, A468G, and C1027T.

[0027] Other embodiments of the invention provide kits comprising reagents for detecting one or more allelic variations. Preferred aspects of this embodiment of the invention provide kits for detecting one or more single nucleotide polymorphisms within or near the beta2-adrenergic receptor (ADRB2) gene. In more preferred aspects of this embodiment of the invention the kit contains the reagents necessary to detect one or more of SNPs from the group consisting of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C121 1T, A 125 IG, A2266C, A2345G, A2408T, and G2417T; or any subset thereof. In especially preferred aspects of these embodiments of the invention one or more SNPs are selected from the group consisting of A-408C, C41T, A468G, and C1027T.

[0028] Additional embodiments of the invention provide methods for detecting one or more allelic variations, including single nucleotide polymorphisms. Various aspects of this embodiment of the invention provide for determining the allelic composition of one or more animal(s), each at one or more loci. Preferred aspects of this embodiment of the invention provide for determining each of one or more animal's allelic composition for at least one locus in the ADRB2 gene. More preferably the animal(s) allelic composition is determined for one or more SNPs selected from the group consisting of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C 121 IT, Al 25 IG, A2266C, A2345G, A2408T, and G2417T; or any subset thereof. In especially preferred aspects of these embodiments of the invention one or more SNPs are selected from the group consisting of A-408C, C41T, A468G, and C1027T.

[0029] Other embodiments of the instant invention provide methods for identifying loci having allelic variations that are in allelic association with one or more of the SNPs selected from the group consisting of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C121 IT, A 125 IG, A2266C, A2345G, A2408T, and G2417T. In preferred aspects of these embodiments of the invention the loci having allelic variations comprise SNPs. In preferred aspects of these embodiments of the invention the identified allelic variations are in linkage disequilibrium with one or more of the group of SNPs listed above. [0030] Various aspects of these embodiments of the invention include (but are not limited to) identifying sequence in proximity to the bovine ADRB2 gene either through de novo sequencing or by accessing public sequence databases (hereafter referred to as "target regions"). In this context, "close proximity" refers to any chromosomal distance over which linkage disequilibrium may exist, preferably up to 5 cM or (5 million base pairs). Factors influencing linkage disequilibrium vary between populations and include effective population size, mating structure, generation interval, ancestry, and other factors. Once one or more target regions are identified a panel of animals is screened to determine the sequence of their genomes in the areas corresponding to the target regions. The data generated from this screening is then analyzed to identify any single nucleotide polymorphisms (SNPs) present therein. The nature of any/all of the above listed ADRB2 allelic variants is also determined for each of these animals. Finally, the SNP data is analyzed with respect to each newly identified SNP to determine which of the newly identified SNPs is in linkage disequilibrium/allelic association with any above listed ADRB2 polymorphisms.

[0031 ] In various preferred aspects of the invention the ADRB2 gene is from a species used to produce milk commercially. In particularly preferred aspects of these embodiments the milk producing species is bovine.

[0032] Moreover, the methods, and kits of the present invention may be used as part of a breeding program to increase, within an animal population, the frequency of genetic markers for one or more trait(s) associated with increased economic value in a dairy animal. These traits include, but are not limited to improved milk composition (including increased fat yield and/or fat percentage, and/or increased protein yield or protein percent), increased milk yield, increased milking rate, lowered somatic cell score, increased mastitis resistance, greater productive longevity, increased daughter pregnancy rate, and indices derived from at least one trait from this list (such as net merit, fluid merit and cheese merit).

[0033] Other embodiments of the current invention provide markers and methods that may be used to identify subsets of animals, or individual animals for forensic purposes and for identifying DNA fingerprints for animals. Various embodiments of this application of the invention provide methods for proving that two samples were not derived from the same animal, or concluding that the probability of two samples containing the same allelic content at multiple loci is sufficiently low as to warrant a conclusion that the two samples must be derived from the same source. In this context, subset refers to any population of animals that have a common distinguishable characteristic, such as genotype for at least one of the ADRB2 SNPs listed above. [0034] Additional embodiments of the invention provide methods for the testing and selection of animals for breeding and for alternative management practices based on assay results. Various aspects of these embodiments of the invention provide methods for managing milking speed, somatic cell score (SCS), and mastitis susceptibility.

[0035] Other embodiments of the present invention provide methods and tests improving milking characteristics for individual animals and animal populations as a group by selecting animals with favorable phenotypes for milking speed, SCS, and mastitis resistance. One benefit of this technology would be to reduce power consumption and labor costs per cow pound or kilogram of milk produced by reducing the amount of time required to milk the animal.

[0036] Other embodiments of the invention provide methods for identifying allelic variation, including SNPs that are in linkage disequilibrium and/or allelic association with the specific loci described herein. Aspects of these methods may include (but are not limited to) identifying sequence in proximity to the bovine ADRB2 gene either through de novo sequencing or by accessing public sequence databases ("target regions"). Once one or more target regions are identified a panel of animals is screened to determine the sequence of their genomes in the areas corresponding to the target regions. The data generated from this screening is then analyzed to identify any single nucleotide polymorphisms (SNPs) present therein. The nature of any/all of the above listed ADRB2 allelic variants is also determined for each of these animals. Finally, the SNP data is analyzed with respect to each newly identified SNP to determine which of the newly identified SNPs is in linkage disequilibrium/allelic association with any above listed ADRB2 polymorphisms.

EXAMPLES

[0037] The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventors to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the invention.

Example 1 : Analysis of ADBR2 SNPs in Holstein bulls

[0038] DNA samples were obtained and assayed, and genotypic data were analyzed. The genotypic results indicate: (a) a statistically significant association (p<0.05) between C41T and Milking Speed and between A468G and Milking Speed; (b) a statistically suggestive association (p<0.10) between C41T and Somatic Cell Score, and milk, fat, and protein yield; and (c) a suggestive association of A-408C genotypes and protein yield. These results are summarized shown in tables 1-4.

[0039] Briefly, 724 Holstein bulls were genotyped for the following loci: AI l C, A-408C, C41T, C1027T, and A468G. The bulls genotyped were chosen from the "high" and "low" tail of the distribution of Somatic Cell Scores. Current Somatic Cell Score (SCS) and production proofs were obtained from the AIPL Web site (www.aipl.asrusda.gov/) on November 17, 2004. Current Milking Speed (MS) proofs for the bulls were obtained from the Canadian Holstein Association's Web site (www.holstein.ca/Genetics/ob041 lho.txt) on the same day. SCS, milk, fat, and protein yield, and productive life (PL) proofs and reliabilities were available for 720 bulls; fat and protein percentage proofs were available for 690 and 656 bulls, respectively. MS proofs and reliabilities were available for 340 bulls. Pedigree information (sire, dam, and maternal grandsire) was available for all animals.

[0040] Analyses were performed to assess significance of genotype effect on sire proofs. Genotypes at 4 loci (AI lC, A-408C, C41T, and A486G) were used in analysis. Genotypes on C1027T were not included because all animals in the data had the same homozygous genotype. Data were analyzed using the following models:

Equation 2a y_lk = μ + ∑G_/m + e_lk m=\

Equation 2b: y_lk - μ + G₁ + e_lk

4

Equation 3a: y_≠ = μ + ∑G_m + S, + β_≠

»1 = 1

Equation 3b: y_≠ = μ + G₁ + S, + e_≠

4

Equation 4a: y_≠ = μ + ∑G_m, + _Sj + e_≠

M=I

Equation 4b: y_≠ = μ + G₁ + S_j + e_≠

4

Equation 5a: y_≠ = μ + ∑G,,,, + α, + e_≠ m=l

Equation 5b: y_≠ = μ + G₁ + a_s + e_≠

where y_tJ (y_uk) is a proof (PTA) of bull k with genotype /, G, (G₁₁₁₁) is the fixed effect of genotype i (at locus m), S_j (S_J) is the fixed (random) effect of sire/ α₇ is the random animal effect with mean 0 and variance Ac?_a, and e,_k (e_tjι) is the random residual term. In all models, part a) included fitting all 4 genotypes simultaneously. In part b), genotypes were fitted one at a time. Models 2-4 were run using SAS software (SAS, 1999-2001 ). Model 5 was run using ASReml software (Gilmour et al., 2002), considering complete available pedigree information (bulls, sires, dams, and MGS - 1496 animals in total). Analyses were run separately for each trait.

Table 1: Genotype frequencies for 5 analyzed loci, for all bulls, bulls with SCS and yield proofs, and bulls with MS proofs.*

Genotype Frequency Percent Frequency Percent Frequency Percent

AIlC

AA 14 2.1 1 13 1.97 13 4.00

AC 143 21.57 143 21.67 87 26.77

CC 506 76.32 504 76.36 225 69.23

Missing 61 60 15

A-408C

AA 19 2.84 19 2.85 4 1.23

AC 232 34.68 232 34.78 94 28.83

CC 418 62.48 416 62.37 228 69.94

Missing 55 53 14

C41T

CC 610 91.87 608 91.84 296 91.36

CT 52 7.83 52 7.85 26 8.02

TT 2 0.30 2 0.30 2 0.62

Missing 60 58 16

C1027T

CC 656 100.00 654 100.00 326 100.00

CT 0 0.00 0 0.00 0 0.00

TT 0 0.00 0 0.00 0 0.00

Missing 68 66 14

A486G

AA 2 0.30 2 0.30 0 0.00

AG 42 6.32 42 6.33 24 7.34

GG 621 93.38 619 93.36 303 92.66

Missing 59 57 13

*Genotype frequencies for bulls having fat and protein percentage proofs were very similar to those of bulls having yield proofs and therefore are not shown. Table 2: Results from the analysis of SCS proofs: F values (F), p-values (P-val.), and significance (Sign.), where n.s. indicates non-significant result (p>0.10), * indicates significant result (p<0.05), ** indicates highly significant result (p<0.01), and o indicates marginally significant results (p<0.10).

Model Joint analysis Individual analysis

Locus F P-val. Sign. F P-val. Sign.

1: no sire

AI lC 4.67 0.010 ** 4.61 0.010 *

A-408C 0.55 0.580 n.s. 0.95 0.387 n.s.

C41T 1.82 0.164 n.s. 2.24 0.108 n.s.

A486G 0.65 0.521 n.s. 1.1 0.332 n.s.

2: sire fixed

AI lC 1.2 0.302 n.s. 1.2 0.303 n.s.

A^08C 0.24 0.783 n.s. 0.38 0.682 n.s.

C41T 2.25 0.107 n.s. 1.54 0.216 n.s.

A486G 1.14 0.321 n.s. 0.7 0.498 n.s.

3: sire random

AI lC 1.64 0.195 n.s. 1.61 0.201 n.s.

A-408C 0.27 0.761 n.s. 0.41 0.662 n.s.

C41T 2.33 0.099 O 1.56 0.212 n.s.

A486G 1.13 0.323 n.s. 0.64 0.529 n.s.

4: animal random

AI lC 1.22 0.543 n.s. 1.69 0.430 n.s.

A-408C 0.1 1 0.946 n.s. 0.08 0.961 n.s.

C4IT 1.92 0.383 n.s. 1.48 0.477 n.s.

A486G 1.4 0.497 n.s. 1.07 0.586 n.s.

Table 3: Results from the analysis of MS proofs: F values (F), p-values (P-val.), and significance (Sign.), where n.s. indicates non-significant result (p>0.10), * indicates significant result (p<0.05), ** indicates highly significant result (p<0.01), and o indicates marginally significant results (p<0.10).

Model Joint analysis Individual analysis

Locus F P-val. Sign. F P-val. Sign.

1: no sire

AI lC 0.37 0.691 n.s. 0.35 0.703 n.s.

A-408C 0.07 0.935 n.s. 0.04 0.962 n.s.

C41T 1.62 0.200 n.s. 2.08 0.127 n.s.

A486G 0.01 0.940 n.s. 0.92 0.339 n.s.

2: sire fixed

AI lC 0.93 0.397 n.s. 1.84 0.160 n.s.

A-408C 0.03 0.970 n.s. 0.04 0.961 n.s.

C41T 2.42 0.091 O 5.13 0.006 **

A486G 0.12 0.725 n.s. 5.5 0.020 *

3: sire random

AI lC 0.7 0.497 n.s. 1.1 0.335 n.s.

A-408C 0 0.998 n.s. 0 0.999 n.s.

C41T 2.32 0.100 O 4.46 0.012 *

A486G 0.06 0.802 n.s. 4.21 0.041 *

4: animal random

AI lC 2.54 0.281 n.s. 2.52 0.284 n.s.

A-408C 0.09 0.956 n.s. 0.22 0.896 n.s.

C41T 4.99 0.082 O 6.02 0.049 *

A486G 0.05 0.823 n.s. 6.52 0.01 1 *

[0041 ] The results of Model 1 (without sire effect) indicated significant association between genotype at locus AI l C and SCS, milk and protein yield, and fat%. However, this genotype effect was no longer significant when sire or animal effect was included in the model, indicating possible confounding between sire and genotype effect. Conversely, when random sire effect was included in the model, genotype at locus C41T showed marginal evidence of association with SCS in the joint analysis (in models that included the genotypes of the other 3 loci). This indicates possible interactions between the C41T locus and other loci in the model (interactions among loci were not explicitly tested in this analysis).

[0042] Results from the MS analyses revealed significant effect of both C41T and A486G genotypes when sire or animal effect was included in the model and the genotype effect is fitted separately for each locus. When all loci were analyzed simultaneously, only C41T locus was marginally significant; indicating tight linkage between C41T and A486G loci - i.e., when one of these loci was included in the model, including the other one did not significantly improve the model.

[0043] There was also suggestive evidence of association of C41T genotype with milk, fat, and protein yield. For SCS and yield traits, allele 'C was associated with a decreased value of the trait and appeared to be completely dominant over the alternative allele 'T'. For MS, the homozygous genotype 'CC was associated with significantly lower values of MS PTA than the 'TT' genotype, but the heterozygous genotype 'CT' was associated with significantly lower PTAs than both homozygous genotypes. In addition, locus A-408C showed marginally significant effect on protein yield.

Example 2: Associations between ADBR2 SNPs and milk-related traits

[0044] A large population (approximately 3400 animals) was used to identify marker associations between four ADRB2 SNPs and milk related traits. Semen samples were purchased from various sources, including Select Sires (www.selectsires.com) and ABS (www.absglobal.com). DNA from the semen samples was extracted. Samples were then genotyped for the four ADRB2 SNPs, namely A-408C, Al 1C, C41T, and A468G.

[0045] A total of 3, 145 Holstein bulls were genotyped all four SNPs (Al 1C, C41T, A468G, and A-408C). Traits under study include milk yield ("MILK") (pounds), fat yield ("FAT")

(pounds), fat percentage ("FATPCT") (percent), productive life ("PL") (months), somatic cell score ("SCS") (Log), daughter pregnancy rate ("DPR") (percent), protein yield ("PROT") (pounds), protein percentage ("PROTPCT") (percent), and net merit ("NM") (dollar). These traits are sex-limited, as no individual phenotypes can be measured on male animals. Instead, genetic merits of these traits defined as PTA (predicted transmitting ability) were estimated using phenotypes of all relatives. Most dairy bulls were progeny tested with a reasonably larger number of daughters (e.g., >50), and their PTA estimation is generally more or considerably more accurate than individual cow phenotype data. The genetic evaluation of traditional dairy traits of US Holstein population was performed quarterly by USDA. Detailed description of traits, genetic evaluation procedures, and genetic parameters used in the evaluation can be found at the USDA AIPL web site (aipl.arsusda.gov). The following linear models were analyzed for each of the traits:

y,_j = s, + ^'∑_d I_vkβ_2k + e_u [Equation 6] k=l y,_j = ^s, + Pi^x,_j + ^e,_j [Equation 7] y, = μ + β_l (SPTA), + ∑ I_lkβ_2k + e, [Equation 8]

Jt=I y, = μ + β, (SPTA)₁ + β_2ιχ_t + e, [Equation 9]

where y, (y,_j) is the PTA of the i^lh bull of the whole sample (PTA of the j^th son of the i^th sire); s, is the effect of the i^th sire; x, (x,_j) is the number of a specific SNP allele that of the i^lh bull of the whole sample (the j^th son of the i^th sire) has, and β₂ is the regression coefficient for x, (x,_j); (SPTA), is the sire's PTA of the i^th bull of the whole sample; μ is the mean; e, (e,_j) is the residual effect; and I,_k (I,_jk) is the indicator variable defined as

Jl if genotype equal to k [O if genotype not equal to k

Where genotype 1 and 3 are the two homozygous genotypes of two SNP alleles, and 2 is the heterozygous genotype. The analyses were performed using SAS PROC GLM procedure, and the p value was estimated using the SAS Type I sum squares. For SNP AI lC, C41T, A468G, and A-408C, x, (x_y) is equal to the number of allele "G", "A", "C", and "A", respectively.

[0046] Equations [6] and [7] were referred as "Model 1"; and equations [8] and [9] were referred as "Model 2" in Table 5 . Equations [6] and [81 are genotype models where genotypes were fitted as fixed effects; whereas, equations [71 and [9] are additive models where no dominance was assumed.

[0047] The results of these analyses using four different statistical models suggest that all four SNPs (AI lC, C41T, A468G, and A-408C) have significant associations with several milking traits (see Table 5).

Table 5. p-values for statistical association between ADRB2 SNPs and milk-related traits. Suggestive p-values (<0.1) are in bold and significant p-values (<0.05) are in bold italics. For suggestive or significant associations, an estimated allele substitution effect is provided in the last column ("Effect").

O13

(A468G)

Model 1 Model 2

Genotype Additive Genotype Additive Effect

MILK Pounds 0.3398 0.3289 0.5548 0.8183

FAT Pounds 0.2628 0.1025 0.2299 | 0.0911 [ -2.6568

FATPCT % 0.4459 0.5523 0.7039 0.7521

PL Months I 0.0417 \ 0.0287 \ 0.0038 \ 0.0013~\ -0.2654

SCS Log 0.4556 0.2099 0.6759 0.4287

DPR Rate 0.9288 0.7135 0.9269 0.7167

PROT Pounds 0.7296 0.6854 0.6589 0.8412

PROTPCT 0.3763 0.3052 0.7003 0.7680

NM 0.1015 I 0.0325 I 0.0855 \ 0.0408 ] -20.6915

Q14 (C41T)

Model 1 Model 2

Genotype Additive Genotype Additive Effect

MILK Pounds 0.6256 0.6259 0.5183 0.3669

FAT Pounds 0.1 190 0.2833 0.3065 0.3637

FATPCT % 0.5948 0.6007 0.8053 0.8715

PL Months J 0.0052 I 0.0126 I 0.0033 I 0.0062 I 0.2172

SCS Log 0.3515 0.1535 00..66884455 00..44880077

DPR Rate 0.6976 0.4425 00..88995577 00..99112211

PROT Pounds 0.5484 0.9191 00..55004499 00..55778822

PROTPCT % 0.7451 0.5206 00..66779944 00..77003377

NfM $ I 0.0387 I 0.0482 \ 0.1934 0.1 178 15.1887 015 (AIlC)

Model 1 Model 2

Genotype Additive Genot e Additive Effect

MILK Pounds 0.1776 70.1039

FAT Pounds 0.1957

1.8324

FATPCT % 0.6919 0.9833 0.9771 0.9889

PL Months 0.5044 0.7354 0.3730 0.3877

SCS Log 0.4016 0.6245 0.2520 0.4127

DPR Rate 0.181 1 0.6010 0.1624 0.4063

PROT Pounds 0.1209 I 0.0398 I 0.0514 | 0.0150 \ 1.7594

PROTPCT % 0.5899 0.9075 0.7600 0.9550

NM $ 0.0734 I 0.0225 I 0.2246 0.0847 1 1.1 176

O16

(A408C)

Model 1 Model 2

Genot e Additive Genot e Additive Effect

MILK Pounds 67.8841

FAT Pounds

3.0517

FATPCT 0.2218 0.5436 0.7620 0.7698

PL Months 0.0616 1 0.0239 1 0.0661 0.0629 0.0843

SCS Log 0.6158 0.3639 0.5941 0.4071

DPR Rate [ 0.0053 I 0.0039 0.0001 0.0001 -0.1740

PROT Pounds 0.7524 0.4547 0.0199 0.0059 1.7445

PROTPCT % 0.3710 0.2044 0.2801 0.1344

NM 0.6615 0.7007 I 0.0068 I 0.0026 \ 16.9967

[0048] Three of these SNPs (Al 1C, C41T, and A468G) exist within the coding region of the ADRB2 gene, and change the amino acid sequence of the protein. These potentially change the structure, and subsequently affect the ability of the receptor protein to discharge its proper cellular function (thus leading to a phenotypic effect). However, in this analysis the SNP with the broadest range of marker-trait associations (A-408C) resides 408 bases upstream to the mRNA transcription start site, in a region highly conserved with the human DNA sequence (which implies functionality) and that is predicted to be a transcriptional promoter (potentially regulating gene expression).

[0049] Further, the A-408C SNP exists within an important regulatory element known as an SV40 EARLY-SEQl element (Fromm and Berg, 1982; Byrne et al., 1983). The consensus sequence of this element is YYCCGCCC (SEQ ID NO: 1) (where Y = C or T). The predominant (and negative) allele of this SNP is "C", which creates an element of TCCCGCCC (SEQ ID NO:2) (consistent); whereas the more rare (and advantageous) allele of this SNP is A, which creates an element of TACCGCC (SEQ ID NO:3), which is inconsistent with the published promoter element. This suggests that alternative alleles of this SNP may differentially affect the expression levels of the ADRB2 gene, which is consistent with the referenced literature claiming associations between milk-related traits and concentrations of ADRB2 mRNA and protein.

Example 3: Analysis of possible effects of the C41T and A468G SNPs on the ADRB2 protein

[0050] Possible impacts for the C41T and A468G polymorphisms were analyzed using software available at the "PolyPhen" website (www.bork.embl-hedelberg.de/PolyPhen). For the C41T locus, the published sequence indicates the presence of a cytosine ("C") at nucleotide position 41 , whereas the variant identified as part of the instant invention has a thymidine ("T") nucleotide. Substitution of T for C at position 41 alters the amino acid sequence of the ADRB2 protein by replacing a proline with a leucine amino acid residue at position 14 of the protein. Analysis using PolyPhen suggests that this mutation is potentially damaging to the function of the protein product.

[0051] For the A468G locus, the published sequence indicates the presence of a guanidine ("G") at position 468, whereas the variant identified as part of instant invention has an adenine ("A") at that position. Substitution of A for G at position 468 alters the amino acid sequence of the ADRB2 protein by replacing methionine residue with an isoleucine residue at amino acid position 156 of the protein.

[0052] Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR- RFLP) assays to enable genotyping of DNA samples at the C41T and A468G loci were designed and validated. For locus C41T, the assay is based on the presence or absence of the Sfol restriction enzyme recognition site GGCGCC (SEQ ID NO:4) in DNA fragments amplified by primer pair repAl lCF and repAl lCR (defined below). The T allele at the C41T locus changes this sequence to GGCGCT (SEQ ID NO:5), which is not recognized, and therefore not digested, by Sfol. [0053] For locus A468G, the assay is based on the presence or absence of the Fokl restriction enzyme recognition site GGATG (SEQ ID NO:6) in DNA fragments amplified by primer pair A468GF and A468GR (defined below). Use of these exact primers will introduce the restriction site through site-specific mutagenesis. The A allele at the A468G locus changes this sequence to GGATA (SEQ ID NO:7), which is not recognized, and therefore not digested, by Fokl.

Example 4: Oligonucleotide Primers

[0054] The following oligonucleotide primers (DNA sequences) were used to amplify and sequence the ADRB2 gene and adjacent DNA from bovine genomic sequence.

ADRB2-A1F AGGGCACCAAAAGTACTGGA (SEQ ID NO:8)

ADRB2-A1R GCGAGCTTACCAGCCAACTA (SEQ ID NO:9)

ADRB2-B1F AGCATGTATGTCGGGGTACAG (SEQ ID NO: 10)

ADRB2-B1R GTTCCAGCGTGACGTTTTG (SEQ ID NO: 11)

ADRB2-C1F TGGAACTGGCTGAACTGACA (SEQIDNO:12)

ADRB2-C1R AGTTGATGGCTTCCTTGTGG (SEQIDNO:13)

ADRB2-D1F TGGAACTGGCTGAACTGACA (SEQ ID NO: 14)

ADRB2-D1R AGTTGATGGCTTCCTTGTGG (SEQIDNO:15)

ADRB2-E1F ATTGTGCACGTGATCAAGGA (SEQIDNO:16)

ADRB2-E1R CGAAAGGTCCGAGAGACTCA (SEQIDNO:17)

ADRB2-F1F TCCTCTTTGCCTGGAACTTG (SEQ ID NO: 18)

ADRB2-F1R CAAGCCCTGATGCACTATGA (SEQIDNO:19)

ADRB2-G1F AACCCCTTGACTGGAGTGTG (SEQ ID NO:20)

ADRB2-G1R TTCTCTGGCTGAAAATGTTA (SEQIDNO:21) repAllCF AGGTCCGCTCGCTGAGG (SEQ ID NO:22) repAllCR GTTCCAGCGTGACGTTTTG (SEQ ID NO:23)

A468GF GAATAAGGCCCGGGTGGTCATTTGGAT (SEQ ID NO:24)

A468GR GCAGTTGATGGCTTCCTTGTG (SEQ ID NO:25) Example 5: Detection of SNPs

[0055] To detect new SNPs, one skilled in the art could use any number of sequence detection methodologies, including (but not limited to) identifying sequence either through random de novo sequencing or by accessing public sequence databases. Once one or more target regions are identified a panel of animals is screened to determine the sequence of their genomes in the areas corresponding to the target regions. The sequence data generated from this screening is then aligned and analyzed to identify any single nucleotide polymorphisms (SNPs) present therein. Such polymorphisms may be expressed as one or more individuals being heterozygous (showing roughly equal peak intensity for each of two base pairs) and/or as two or more animals showing alternate homozygote genotypes (for example, one sample containing only the A allele while another samples only shows the G allele).

[0056] All of the compositions and methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied without departing from the concept and scope of the invention.

REFERENCES

[0057] The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.

Blum JW, Schams D, Bruckmaier R. 1989. Catecholamines, oxytocin and milk removal in dairy cows. J Dairy Res 56(2): 167- 177.

Boettcher PJ, Dekkers JC, Kolstad BW. 1998. Development of an udder health index for sire selection based on somatic cell score, udder conformation, and milking speed. J Dairy Sci 81(4): 1 157-1 168.

Brown CA, Rischette SJ, Schultz LH. 1986. Relationship of milking rate to somatic cell count. J Dairy Sci 69(3):850-854.

Byrne BJ, Davis MS, Yamaguchi J, Bergsma DJ, and Subramanian KN. 1983. Proc. Nat. Acad. Sci. USA. 80:721 -725.

Einspanier R, Gabler C, Kloas W. Expression of the beta2 adrenergic receptor in the cattle. Direct submission to Genbank, accession number Z86037.

Fromm M and Berg P. 1982. Deletion Mapping of DNA Regions Required for SV40 Early Region Promoter Function In Vivo. J. MoI Appl. Gen. 1 :457-481.

Gilmour, AR, Gogel, BJ, Cullis, BR, Welham, SJ, Thompson, R. 2002 ASRem l User's Guide Release 1.0, VNS International Ltd., Hemel Hempstead, UK.

MacMillan KL, Duirs GF, Duganzich DM. 1983. Associations between dry cow therapy, clinical mastitis, and somatic cell count score with milk and fat production in ten New Zealand dairy herds. J Dairy Sci 66(2): 259-265.

McClelland, L. A. 1983 A comparison of objective and subjective measures of Milking Speed on Canadian Holstein-Friesians. University of Guelph.

Reneau JK. 1986. Effective use of dairy herd improvement somatic cell counts in mastitis control. J Dairy Sci 69(6): 1708- 1720.

Roets E, Vandeputte-Van Messom G, Peeters G. 1986. Relationship between milkability and adrenoceptor concentrations in teat tissue in primiparous cows. J Dairy Sci 69(12):3121-3131.

Roets E, Vandeputte-Van Messom G, Burvenich C, Peeters G. 1989. Relationship between numbers of alpha 2- and beta 2-adrenoceptors in teat tissue and blood cells and milkability of primiparous cows. J Dairy Sci 72(12):3304-3313.

Roets E, Burvenich C, Hamann J. 1986. Relationship between numbers of alpha 2- and beta 2-adrenoceptors on blood cells of bulls and milkability of their daughters. J Dairy Res 62(4):567-575. Roets, et al. 1995. Relationship between numbers of alpha2- and beta2-adrenoceptors on blood cells of bulls and milkability of their daughters. J. Dairy Res 62(4) 567-575.

SAS Release 8.02, 199-2002. SAS Institute Inc., Cary, NC.

Schimpf RJ, Schmutz SM, Buchanan FC. 2001. Genetic mapping of the ADRB2 gene on cattle chromosome 7. Anim. Genet. 32(6):390.

Valero FY, Kobayashi Y, Hoeffer Jr. CA, Collier RJ. 2002. Relationship of Adrenergic receptor mRNA to Milking Rate in Dairy Cows. Proceedings of Plant and Animal Genome X, San Diego, CA, USA.

Claims

What is claimed is:

1. A method of selecting animals for breeding comprising: a. evaluating the allelic composition of at least one potential parent animal for at least one locus in the beta2-adrenergic receptor (ADRB2) gene; wherein at least one evaluated locus comprises a single nucleotide polymorphism (SNP) selected from the group consisting of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C121 1T, A 125 IG, A2266C, A2345G, A2408T, and G2417T; and b. selecting at least one potential parent animal for breeding based on its allelic composition for at least one SNP in the ADRB2 gene selected from the group consisting of: A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, Cl 2 HT, A 125 IG, A2266C, A2345G, A2408T, and G2417T.

2. The method of claim 1 wherein the single nucleotide polymorphism is selected from the group consisting of A-408C, C41T, A468G, and C1027T.

3. A method of breeding an animal comprising: a. selecting at least one potential parent animal for breeding based on its allelic composition for at least one locus at the ADRB2 gene wherein at least one locus is selected from the group consisting of the following single nucleotide polymorphisms: A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G C957T, C1027T, G1 143T, C121 1T, A 125 IG, A2266C, A2345G, A2408T, and G2417T, or one or more SNPs in allelic association or linkage disequilibrium with one or more of these SNPs; and b. breeding the animal.

4. The method of claim 3 wherein the single nucleotide polymorphism is selected from the group consisting of A-408C, C41T, A468G, and C1027T.

5. A kit comprising reagents for the detection of at least one single-nucleotide polymorphism (SNP) in the beta2-adrenergic receptor (ADRB2) gene wherein at least one SNP is selected from the group consisting of: A-732T, A-558C, C-529T, A-408C, A- 390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C 121 IT, A 125 IG, A2266C, A2345G, A2408T, and G2417T.

6. The kit of claim 5 wherein at least one SNP is selected from the group consisting of: A-408C, C41T, A468G, and C1027T.

7. A method of evaluating allelic composition of an animal for at least one locus, the method comprising: determining the allelic composition of at least one animal for at least one locus in the beta2-adrenergic receptor (ADRB2) gene; wherein at least one locus for which the allelic composition is determined comprises a single nucleotide polymorphism (SNP) selected from the group consisting of A-732T, A-558C, C-529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C 121 IT, A 125 IG, A2266C, A2345G, A2408T, and G2417T.

8 The method of claim 7 wherein the SNP is selected from the group consisting of: A-408C, C41T, A468G, and C1027T.

9. A method of identifying allelic variation in allelic association or linkage disequilibrium with one or more SNPs selected from the group consisting of: A-732T, A-558C, C- 529T, A-408C, A-390T, A-184C, A-85G, C41T, C129T, A468G, C957T, C1027T, G1 143T, C 121 IT, A 125 IG, A2266C, A2345G, A2408T, and G2417T, the method comprising targeted resequencing of genomic regions in proximity to ADRB2 from multiple individuals to identify one or more SNPs displaying linkage disequilibrium/allelic association with the above list of ADRB2

10. The method of claim 9 wherein the single nucleotide polymorphism is selected from the group consisting of A-408C, C41T, A468G, and C1027T.