WO2012001613A1 - COMBINATION OF SIX SNPs FOR DETECTING THE PREDISPOSITION TO NEUROVASCULAR DISEASES - Google Patents

Abstract

The present invention relates to the combination of six single nucleotide polymorphisms (SNPs), namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of neurovascular diseases. The present invention also relates to a method for determining a genetic predisposition to neurovascular diseases in an individual, comprising detecting the alleles of the six SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803 in an isolated nucleic acid sample of an individual. The present invention further relates to a kit for determining an individual's risk for a neurovascular disease. The present invention also relates to the use of the combination mentioned above.

Description

COMBINATION OF SIX SNPs FOR DETECTING THE PREDISPOSITION TO

NEUROVASCULAR DISEASES

Field of the invention

The present invention relates to the detection of a predisposition to neurovascular diseases in an individual, in particular stroke. The present invention relates to a method and a kit for detecting a predisposition to stroke in an individual, comprising the detection of a combination of six single nucleotide polymorphisms (SNPs), namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, as well as the combination itself, the polynucleotides comprising them and their uses.

Background of the invention

Neurovascular diseases are a huge public health burden worldwide. In particular, neurovascular accidents, or strokes, are the 3^rd cause of death and 1^st cause of disability, showing a negative impact not only on the quality of life of the affected patient but also on the family, with a large number of patients suffering functional dependence even more than one year after the event [1]. Moreover, the cost for health care systems is severe because there are direct costs (hospital stay, rehabilitation, transportation, medical monitoring, pharmaceutical consumption, etc.), and indirect costs (lost of productivity of patients and caregivers, loss of activity, premature mortality, etc.). Overall, the estimated cost of stroke in Spain is 13,826€ the first year, 8,945€ the second year and 7,739€ the third year per patient [2]. Therefore, a need exists for detecting and preventing stroke.

The present inventors have selected to explore neurovascular accidents prevention from a genetic point of view. Indeed, there is an important genetic implication in neurovascular diseases, discovered in the early nineties by twin and familiar aggregation studies, but the genes responsible for the inheritance remain largely undetermined [3]. For instance, the classical linkage analysis and candidate gene association approaches have reported an association of the PDE4D, ALOX5AP, ApoE, IL6, MTHFR or TNFa genes with ischemic stroke, but replication of these findings has been really inconsistent [4]. Moreover, genome-wide association studies (GWAS) have revealed the association of two SNPs with stroke within chromosome 12pl3 in Caucasians, suggesting a role for the NINJ2 gene [5], an association that has been later contradicted [6]. Also, a meta-analysis of GWAS in ischemic stroke in Caucasians identified the ZFHX3 gene on chromosome 16q22 [7] and the PRKCH and CELSR1 genes have been associated with ischemic stroke in the Japanese population [8- 9], but these markers remain to be checked in other studies. In any case, all studies revealed associations with a small effect on the overall prediction of the disease. Similarly, the genetic risk factors for hemorrhagic stroke are still unclear [3]. However, the genetic basis for all neurovascular diseases are probably identical since all SNPs identified to date are located in genes involved in pathophysiological processes common to all neurovascular diseases, such as inflammation, angiogenesis, fibrinolysis, coagulation, hypertension, coronary heart disease, angiogenesis, lipid metabolism or diabetes, namely rs7193343, rs6007897, rs4044210, rsl 1064005, rs22381 12, rsl2319392, rs2228576, rs9634156, rsl3376333, rs6725887, rs2306374, rsl2526453, rs3798220, rsl 122608, rs9982601, rsl 801282, rs5219, rs3754777, rsl3333226, rs4977574, rsl0012946 [3]. Additionally, neurovascular diseases share common demographical and clinical risk factors such as the presence of transient ischemic attacks, hypertension, dyslipidemia, diabetes, atrial fibrillation, cardiac disease, amyloid angiopathy, obesity, drug use or abuse, sedentary life style, smoking, medication, alcohol consumption and/or familiar antecedents [3].

Interestingly and based on the fact that neurovascular diseases are complex diseases caused by multiple genetic and environmental factors, the inventors thought that the combination of various genetic markers and clinical variables could perhaps unveil stronger associations. The inventors published the article "KCNK17 genetic variants in ischemic stroke", in Atherosclerosis (2009) [10], where they disclosed that the A allele of SNP rsl 0947803 in the KCNK17 gene could be a risk marker for ischemic stroke with an OR of 1.48, but no disclosure or suggestion was made about the possible use of this SNP in association with other SNPs to predict the risk of stroke. The rs5742912 SNP of the SCNN1A gene was described in the article "Impact of eNaC Polymorphisms on the Risk of Ischemic Cerebrovascular Events: A Multicenter Case-Control Study", in Clinical Chemistry (2005) [1 1], as a risk marker for ischemic stroke, with an OR of 3.26 in homozygous young women for this mutation. However, no mention of using this SNP in association with other markers to diagnose ischemic stroke, and no mention of using this SNP as a predictive tool in association with other markers to improve stroke detection was made. In view of the teachings of the prior art, it is necessary to provide a new tool for determining the risk of suffering neurovascular diseases. A first object of the present invention is to provide new genetic markers for neurovascular diseases detection. A second object of the present invention is to provide a new method and a new kit for determining the probability of suffering a neurovascular accident, or stroke. In response to these objects, the present inventors have surprisingly found that the combination of six SNPs disclosed in the present invention is useful for determining the risk of suffering a neurovascular accident, or stroke, and consequently a method and a kit using said combination can be developed.

Summary of the invention The present invention relates to the combination of six single nucleotide polymorphisms (SNPs), namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of neurovascular diseases. The present invention also relates to a method for determining a genetic predisposition to neurovascular diseases in an individual, comprising detecting the alleles of the six SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803 in an isolated nucleic acid sample of an individual. The present invention further relates to a kit for determining an individual's risk for a neurovascular disease. The present invention also relates to the use of the combination mentioned above.

Brief description of the drawings

Figure 1. ROC curves of the model with classical stroke risk factors (age, male gender, hypertension, smoking and diabetes) with and without addition of the 6 genetic variants.

Figure 2. Percent of controls and stroke cases depending on the number of genetic variants carried.

Figure 3. Percent of controls and stroke cases depending on the total number of risk factors carried.

Figure 4. Distribution of controls and stroke cases for each stroke score.

Figure 5. Percent of controls and stroke cases in each category proposed by the stroke risk model.

Detailed description of the invention

1.- Definitions and terminology

The following abbreviations and acronyms are used in the present invention:

CI: confidence interval

GWAS: genome-wide association studies

eNaC: epithelial sodium channel

MAF: minor allele frequency

MMP: matrix metalloproteinase

OR: odd ratio

ROC: Receiver operating characteristic

PCR: polymerase chain reaction

SNP: single nucleotide polymorphism

TOAST: Trial of ORG 10172 in AcuteStroke Treatment

ALOX5AP: Arachidonate 5-lipoxygenase-activating protein

APOE: apolipoprotein E

CELSR1: cadherin EGF lag seven-pass G-type receptor 1 IL6: interleukin 6

KCNK17: potassium channel, subfamily K, member 17

LRP1: low density lipoprotein receptor-related protein 1

MMP12: matrix metalloproteinase 12

MTHFR: 5, 10-methylenetetrahydrofolate reductase

NINJ2: nerve injury-induced protein 2

NOS3: nitric oxide synthase 3

PDE4D: phosphodiesterase 4D

PITX2: paired-like homeodomain transcription factor 2

PRKCH: protein kinase C, eta

SCNN1A: sodium channel, nonvoltage-gated 1, alpha subunit

TNFa: tumor necrosis factor alpha

ZFHX3: zinc finger homeobox 3

T: thymine

C: cytosine

A: adenine

G: guanine

The term "allele", as used herein, refers to a sequence variant of the gene.

The terms "polymorphic" and "polymorphism", as used herein, refer to the condition in which two or more variants of a specific genomic sequence, or the encoded amino acid sequence, can be found in a population. The terms refer either to the nucleic acid sequence or the encoded amino acid sequence; the use will be clear from the context. The polymorphic region or polymorphic site refers to a region of the nucleic acid where the nucleotide difference that distinguishes the variants occurs, or, for amino acid sequences, a region of the amino acid where the amino acid difference that distinguishes the protein variants occurs. As used herein, a "single nucleotide polymorphism" or "SNP", refers to a polymorphic site consisting of a single nucleotide position.

The term "genotype" refers to a description of the alleles of a gene or genes contained in an individual or a sample. As used herein, no distinction is made between the genotype of an individual and the genotype of a sample originating from the individual. Although typically a genotype is determined from samples of diploid cells, a genotype can be determined from a sample of haploid cells, such as a sperm cell. The term "odd ratio" or "OR" refers to the ratio of the odds of the disease for individuals with the marker (polymorphism) relative to the odds of the disease in individuals without the marker (polymorphism).

The term "isolated polynucleotide acid" or "isolated nucleic acid", as used herein, is referred to a polynucleotide that is separated and/or recovered from a nucleic acid from nucleotide sequences which normally flank the nucleic acid molecule and/or has been completely or partially purified from other biological material (e.g. protein) normally associated with the nucleic acid. Likewise, said "isolated polynucleotide acid" comprises polynucleotide variants wherein one or more insertions, deletions and/or substitutions have been carried out.

By "oligonucleotide" is meant a single- stranded nucleotide polymer made of more than 2 nucleotide subunits covalently joined together.

The term "gene", as used herein, refers not only to the strictly coding part but also the non-coding parts.

The term "combination", unless otherwise stated, is referred herein to the combination of six SNPS, namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of ischemic stroke, wherein said SNPs are included in an isolated polynucleotide sequence as defined herein.

When referred to the SNPs disclosed herein and the polynucleotides comprising them, the term "a combination of six" SNPs is not intended to mean a closed group of a maximum of 6 SNPs or the corresponding polynucleotides, but it can also include combinations with more SNPs or the corresponding polynucleotides. In these combinations, the SNPs or the corresponding polynucleotides can be included or not in stroke-associated genes, providing that said six SNPs correspond to the SNPs and the corresponding polynucleotides disclosed in the present invention.

By "clinical variable" is meant the use of demographic data indicative of stroke such as race or ethnic origin, age, gender and familial history of stroke, as well as the use of clinical information such as the presence of transient ischemic attacks, hypertension, dyslipidemia, diabetes, atrial fibrillation, cardiac disease, amyloid angiopathy, obesity, drug use or abuse, sedentary life style, smoking, medication, alcohol consumption and antecedents of stroke.

By "stroke-associated genes" is meant genes that have been reported to be involved in the processes of stroke and in processes functionally related to stroke such as inflammation, fibrinolysis, coagulation, hypertension, heart disease, angiogenesis, lipid metabolism or diabetes. The term "neurovascular diseases" refers to complex disorders of the brain, spinal cord and/or blood vessels and risk factors associated with complex disorders of the brain, spinal cord and/or blood vessels such as: stroke, arteriovenous malformations, thrombosis, CADASIL, cerebral amyloid angiopathy, aneurysms, dissections, hyper/hypotension, encephalopathies, lacunar syndromes, Fabry, homocystinuria/homocysteinemia, hyperglycemia/hypoglycemia, hyper/hypolipidemia, diabetes, MELAS, methylmalonic acidemia, fibromuscular dysplasia, foix-alajouanine syndrome, reperfusion injury, hemorrhagic stroke, ischemic stroke, cardioembolism, atherothrombotism, vascular malformations, transient global amnesia, transient ischemic attacks, traumatisms, neurological infections, seizures, degenerative disorders, epilepsy, cardiopathy, headache and migraines, tumors, inflammation, demyelination, movement impairments, neurodegeneration, dementia, Alzheimer, Parkinson, Down syndrome, aphasia, apraxia, psychiatric disorders, drug and substances abuse disorders, cognitive impairments, schizophrenia, sleep disorders.

By "Mediterranean population" is meant individuals originating or located in any of the following territories: Spain, France, Monaco, Italy, Malta, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, Greece, Turkey, Cyprus, Syria, Lebanon, Israel, Egypt, Libya, Tunisia, Algeria, Morocco, Crete, Euboea, Pelagie Islands, Gibraltar, Rhodes, Lesbos, Ceuta, Melilla, Chios, Kefalonia, Corfu, Naxos, Andros, Sardinia, Corsica, Sicily, Cres, Krk, Brae, Hvar, Pag, Korcula, Malta, Ibiza, Majorca, Minorca, Akrotiri and Dhekelia, Andorra, Jordan, Portugal, San Marino, the Vatican City, Macedonia and Serbia.

2.- Description

The present invention relates to a combination of six SNPs, namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of neurovascular diseases.

In a particular embodiment, said six SNPs are combined with one or more of the SNPs rs7193343, rs6007897, rs4044210, rsl 1064005, rs22381 12, rsl2319392, rs2228576, rs9634156, rsl3376333, rs6725887, rs2306374, rsl2526453, rs3798220, rsl 122608, rs9982601, rsl 801282, rs5219, rs3754777, rsl3333226, rs4977574, rsl0012946.

In another embodiment, said single nucleotide polymorphisms according to the previous embodiments are combined with one or more clinical variables associated with a neurovascular disease. Preferably, said clinical variable is selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

Preferably, said neurovascular disease in the previous embodiments is stroke.

Since said SNPs are by nature comprised in an isolated polynucleotide or nucleic acid molecule, the present invention also relates to the combination of six isolated polynucleotides included in neurovascular disease-associated genes comprising each of said polynucleotides a SNP selected from SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicatives of an individual's risk of neurovascular disease, said isolated polynucleotides corresponding to SEC ID No. 1 to 6. Preferably, said neurovascular disease is stroke.

SEC ID No. 1 indicates the position of polymorphism rs2276109 in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a A or G nucleotide, being G the allele associated with neurovascular disease. rs2276109 is located on human contig NT 033899.7 at chromosomal position 102251001 on chromosome 1 1.

SEC ID No. 2 indicates the position of polymorphism rs 10275136 in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a T or C nucleotide, being C the allele associated with neurovascular disease, rs 10275136 is located on human contig NT 007914.14 at chromosomal position 150514825 on chromosome 7.

SEC ID No. 3 indicates the position of polymorphism rs310585 in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a G or A nucleotide, being A the allele associated with neurovascular disease. rs310585 is located on human contig NT 007914.14 at chromosomal position 150526188 on chromosome 7.

SEC ID No. 4 indicates the position of polymorphism rs7956957 in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a C or G nucleotide, being G the allele associated with neurovascular disease. rs7956957 is located on human contig NT 029419.1 1 at chromosomal position 55889082 on chromosome 12.

SEC ID No. 5 indicates the position of polymorphism rs 10947803, also called rs9471058, in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a C or A nucleotide, being A the allele associated with neurovascular disease, rs 10947803 is located on human contig NT 007592.14 at chromosomal position 39378588 on chromosome

6.

SEC ID No. 6 indicates the position of polymorphism rs5742912 in its surrounding sequence, wherein the nucleotide N is the SNP at position 21 and can be a C or T nucleotide, being T the allele associated with. rs5742912 is located on human contig NT 009759.15 at chromosomal position 632861 1 on chromosome 12.

Preferably, said neurovascular disease in SEC ID No. 1-6 is stroke,

to SEC ID No. 1 to 6. Preferably, said neurovascular disease is stroke.

In a particular embodiment, the alleles of said SNPs are inferred by genetic testing of other markers or by measuring the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNN1A, KCNK17, MMP12 and/or LRP. In another particular embodiment, said combination of SNPs or the corresponding isolated polynucleotides is found in an individual belonging to the Mediterranean population.

In a more particular embodiment, said individual belongs to the Spanish population.

As will be proved further below in the methods and results section, said combination of SNPs or the corresponding isolated polynucleotides can then be applied to a new model or method for determining the genetic risk that an individual may present for suffering a neurovascular disease, preferably stroke, and a kit for implementing said method.

2.1.- Methods

The aim of this study was to create a genetic predictive model for stroke, although extendable to a neurovascular disease as defined herein, by discovering a new combination of genetic markers through one of the largest candidate gene association studies ever performed in stroke, including a replication in new individuals from Spain and Portugal and a functional analysis of the results.

2.1.1.- Study population

A case-control study design was used to analyze genetic variants in Spanish and Portuguese populations. Sample size was calculated with the Ene 2.0 software to obtain a power of 0.80 with a significance level of 0.05 for a 6% difference of alleles' frequency between cases and controls.

Spanish stroke cases (n=531) were unrelated consecutive patients who were admitted to the Emergency Department of University Hospitals through Spain, recruited within the first 3 hours after symptoms onset. Only patients with a nonlacunar ischemic stroke involving the vascular territory of the basilar or middle cerebral arteries were included. Occlusion was assessed by transcranial Doppler ultrasonography [15].

Portuguese stroke cases (n=377) were unrelated patients, under the age of 65 at stroke onset and recruited throughout Portugal. Stroke was defined by the presence of a new focal neurological deficit, with an acute onset and symptoms and signs persisting for more than 24 hours, and confirmed by computed tomography scan and/or magnetic resonance imaging [16].

Control participants were healthy volunteers from Spain (n=547) and Portugal (n=448), free of stroke as ascertained by direct interview before recruitment.

In both subsets, patients with a clinically known inflammatory or malignant disease were excluded from the study. Details on socioeconomic and demographic characteristics were obtained from all subjects by questionnaires, together with information on smoking, dyslipidemia, hypertension, diabetes mellitus and current medication use. Smoking was defined as ever smoking by interview. Hypertension was defined as systolic blood pressure > 140 mmHg and diastolic blood pressure > 85 mmHg, self-reported history and/or treatment for hypertension. Diabetes mellitus was defined by self-reported history and/or any treatment for diabetes type 2. Dyslipidemia was defined as increased lipids concentrations (cholesterol >200mg/dL or triglycemia >200mg/dL), self-reported history and/or any treatment for dyslipidemia.

Written informed consent was obtained from all subjects who were all of European ancestry. The local ethics committee approved the study.

2.1.2.- Genotyping and SNP associations

221 SNPs in 135 different candidate genes were chosen in the literature for their putative functions in neurovascular diseases and in processes functionally related to these diseases such as inflammation, fibrinolysis, coagulation, hypertension, coronary heart disease, angiogenesis, lipid metabolism or diabetes and were genotyped and analyzed as described below.

Genomic DNA was extracted for each subject from lmL of peripheral blood anti- coagulated with EDTA by standard methods. The SNPs were genotyped by SNPlex™ (Applied Biosystems, Inc., Foster City, USA) at the Spanish national genotyping centre (CeGen), Sequenom^® iPLEX or TaqMan^® (Applied Biosystems) with call rates >90%. Five genes were analyzed entirely by Tag-SNPs, defined by the HapMap data using pairwise tagger r²>0.8 and a minor allele frequencyX).1 : the MMP9, NOS3, VEGF, LRP and IL6 genes. Deviation from the Hardy- Weinberg equilibrium (HWE) was assessed using a χ2 test with 1 degree of freedom. A χ² or Fisher's exact test, as appropriate, was used to compare categorical variables between groups. Bonferroni's correction was used to account for multiple testing. Student's t-test or Krustal-Wallis was used to compare continuous variables, as appropriate. The odd ratios (ORs) and 95% confidence intervals (Cls) for the effect on ischemic stroke risk were estimated by logistic regression. All statistical analysis were done with the SPSS^® 15.0 software.

Replication was performed in Spain in a new set of individuals for all 221 SNPs and in Portugal for 6 SNPs: rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, although genotyping of rs310585 failed in these samples.

Cumulative association of the positive individual SNP associations was calculated, depending on the number of risk alleles carried (from 0 to 6) and the combination of alleles of the 6 SNPs carried (out of the 2⁶=64 possible combinations). The analysis was performed under an additive model, considering stage 1, stage 2 and the overall database. A logistic regression was then performed for each variable and the predicted probabilities were recorded. We graphed 2 ROC curves, one for risk factors only and one for the resulting model of risk factors and genetic variants, to explore the relationship between the sensitivity and specificity of a clinical test for stroke risk. We then calculated the difference between the areas under the curves using the MedCalc software. Finally, the combination of genetic and clinical variants carried by each individual was used to develop a predictive model of stroke risk and to determine stroke risk categories.

2.1.3.- Functional analysis

RNA was extracted from control participants and expression was measured by quantitative Real Time PCR for the KCNK17, LRP, SCNN1A and MMP12 genes. The white blood cell fraction obtained after centrifugation of whole blood conserved in EDTA for 15 min at 3500 rpm, was preserved in RNAlater® at -80°C and total RNA was isolated by RiboPure- Blood™ Kit (Ambion®, Foster City, USA). cDNA synthesis was performed using a High- Capacity cDNA Archive Kit (Applied Biosystems Inc., Foster City, USA). mRNA levels were determined by quantitative Real Time PCR, using a standard TaqMan® PCR kit protocol and TaqMan fluorogenic probes (LRP: Hs00233856_ml, SCNN1A: Hs00168906_ml, MMP12: Hs00159178_ml) with a 7500 Real Time PCR System (Applied Biosystems Inc., Foster City, USA). The Cyclophilin A (PPIA) was run as housekeeping gene to normalize the results (Hs99999904_ml). All reactions were run in triplicates on 96-well plates, and analyzed using the Applied Biosystems SDS 7500 system software (Applied Biosystems Inc., Foster City, USA). The results are expressed in percentage compared to a unique calibrator sample used in all experiments.

MMP12 protein level was measured from serum of controls by Fuorokine® MMP12 Analyte Profiling in 96-well microtiter plates. Each sample was concentrated from 500μί to lOOuL with Microcon® (MilliporeTM, Billerica, USA) and tested twice following manufacturer's users' guide on a Luminex analyzer (Human MMP Base Kit, Fluorokine® MAP, R&D Systems, Minneapolis, USA). NOS3 protein activity was assayed from plasma of controls by Nitrate/Nitrite Colorimetric Assay Kit in 96-well microtiter plates. Each sample was concentrated from 500μί to lOOuL with Microcon® (MilliporeTM, Billerica, USA) and tested twice following manufacturer's users' guide (Catalog n°780001, Cayman™, Ann Arbor, USA).

2.1 A- Microarrays and Ingenuity Pathway Analysis

RNA was extracted from 12 subjects. EDTA tubes were centrifuged at 3500rpm for 15 min to obtain the white blood cell fraction. RiboPure™ -Blood kit from Ambion (Ambion, Woodward st. Austin, USA) was used to extract total RNA following manufacturer's instructions. Globin RNA from erythrocytes that causes interference in microarrays studies from blood samples was extracted using the Globin-Clear kit (Ambion, Woodward st. Austin, USA). The images were processed with Microarray Analysis Suite 5.0 (Affymetrix). All samples demonstrated characteristics of high-quality cRNA (375' ratio of probe sets for glyceraldehyde-3-phosphate dehydrogenase and beta-actin of <1.5) and were subjected to subsequent analysis.

Raw expression values obtained directly from .CEL files were pre-processed using the RMA method, a three-step process which integrates background correction, normalization and summarization of probe values. These normalized values were the basis for all the analysis. Previous to any analysis, data were submitted to non-specific filtering to remove low signal genes and low variability genes. The selection of differentially expressed genes between conditions was based on a linear model analysis with empirical Bayes moderation of the variance estimates following the methodology developed by Smyth. This method combines information from the whole array and every individual gene in order to obtain improved error estimates which are very useful in microarray data analysis where sample sizes are often small what can lead to erratic error estimates and, in consequence, to untruthful p-values. Fold changes, (moderated)-t or p-values were used to rank the genes from most to least differentially expressed. In order to deal with the multiple testing issues, p-values were adjusted using the false discovery rate (FDR) method by Benjamini and Hochberg.

Significant genes were graphically highlighted using volcano plots with a first (horizontal) dimension as the fold change between the two groups and a second (vertical) axis representing the p-value. Genes selected as being differentially expressed were also clustered to look for common patterns of expression. Hierarchical clustering with Euclidean distance was used to form the groups and heatmaps were used to visualize them. The biological significance was estimated by checking if the genes that were found to be differentially expressed appeared to be concentrated or particularly absent of some Gene Ontology categories that could then be related with the biological processes involved in the analysis. The libraries used for the statistical analysis with R were developed for microarray data analysis by the Bioconductor Project 6.

We finally used the Ingenuity Pathways software (IP A) to evaluate the relationship between genes significantly associated with LRP genotypes. The microarrays results were entered into the IPA analysis tool, which measured associations of the significant molecules with other molecules and disease functions stored in the knowledgebase, scientist-curated, updated, and integrated from the published literature and other databases such as OMIM, Gene Ontology, and KEGG. The IPA software was also used to identify new molecular networks involving the genes identified. These networks were illustrated and ranked by the software depending on the significance of focus gene enrichment. The protocol used for the study is depicted in the following scheme:

Scheme 1

Data (questionnaires) and samples collection (DNA, RNA, serum, plasma, etc)

DNA extraction

Genotyping (PCR & Sequencing, Real-Time Genotyping, etc) Statistical analy^s of the data

Functional Studies:

- protein levels by ELISA

- protein activity by colorimetric assay

- mRNA levels by real-time PCR

- microarrays

2.2.- Results

2.2.1.- Genetic analysis

Genotyping and statistical analysis were performed in 2 distinct stages considering Spanish samples. Overall, stroke cases were divided in cardioembolic (50%), atherothrombotic (23%) and undetermined (27%) ischemic stroke etiologies. No difference was observed between cases and controls of the two stages. Established risk factors, including male gender, age, diabetes mellitus, hypertension and cigarette smoking were observed at a higher frequency in the stroke group (Table 1).

22 SNPs were excluded from the analysis because the minor allele frequency (MAF) was lower than 1% and 33 SNPs were excluded because genotyping failed in any stage of the analysis, so the final statistical analysis included 166 SNPs. Considering an additive model, a total of 25 SNPs were associated with stroke in at least one stage of the analysis. Six SNPs located near or in the KCNK17 (rsl0947803), LRP1 (rs7956957), MMP12 (rs2276109), NOS3 (rsl0275136 and rs310585) and SCNNIA (rs5742912) genes were associated with ischemic stroke in both stages of the analysis (Table 2). Table 1. Characteristics of the Spanish study population

Overall (n=1062)

Controls IS Cases p-value

(n=535) (n=527)

Age, years ± SD 71.6 ± 7.1 70.7 ± 12.0 <0.05

Men, n (%) 230 (42.5) 287 (54.5) <0.001

Smokers, n (%) 68 (12.6) 130 (25.9) <0.001

Hypertension, n (%) 239 (44.2) 308 (59.2) <0.001

Diabetes mellitus, n (%) 44 (8.2) 120 (22.9) <0.001

Dyslipidemia, n (%) 155 (28.7) 173 (33.1) <0.05

Table 2. SNPs significantly associated with stroke in both stages of the analysis in Spain

Stage 1

SNP Risk controls cases p-value OR (95% CI) rsl0947803 A 17.8 25.3 0.003 1.57 (1.16-2.1 1) rs7956957 G 63.0 70.0 0.0261 1.37 (1.04-1.80) rs2276109 G 9.7 17.7 0.0003 2.00 (1.37-2.91) rsl0275136 C 93.5 97.2 0.0137 2.35 (1.17-4.73) rs310585 A 46.4 58.1 0.0005 1.60 (1.23-2.09) rs5742912 T 86.8 96.5 3.1E-07 4.19 (2.33-7.54)

Stage 2

SNP Risk controls cases p-value OR (95% CI) rsl0947803 A 17.8 23.5 0.012 1.42 (1.08-2.12) rs7956957 G 64.4 70.7 0.0415 1.33 (1.01-1.76) rs2276109 G 9.7 14.0 0.0405 1.51 (1.02-2.25) rsl0275136 C 93.5 97.6 0.0046 2.89 (1.34-6.20) rs310585 A 44.9 55.4 0.0038 1.52 (1.14-2.03) rs5742912 T 93.8 97.2 0.0127 2.31 (1.18-4.55)

Overall

SNP Risk controls cases p-value OR (95% CI) rsl0947803 A 17.8 24.2 0.003 1.48 (1.14-1.91) rs7956957 G 63.7 70.3 0.0027 1.35 (1.1 1-1.64) rs2276109 G 9.7 15.9 4.3E-05 1.76 (1.34-2.31)

rsl0275136 C 93.5 97.4 0.0002 2.59 (1.55-4.34) rs310585 A 45.7 56.7 7.9E-06 1.56 (1.28-1.89) rs5742912 T 91.0 96.8 2.5E-07 3.05 (1.96-4.75)

Replication in Portugal was performed for 6 SNPs: rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803. Established risk factors, including male gender, age, diabetes mellitus, hypertension and cigarette smoking were observed at a higher frequency in the stroke group (Table 3).

Table 3. Characteristics of the Portuguese study population

Overall (n=825)

Controls IS Cases p-value

(n=448) (n=377)

Age, years ± SD 63.1 ± 6.9 50.7 ± 9.8 <0.001

Men, n (%) 213 (47.5) 214 (56.8) <0.001

Smokers, n (%) 125 (28.6) 163 (44.1) <0.001

Hypertension, n (%) 168 (38.4) 183 (54.0) <0.001

Diabetes mellitus, n (%) 46 (10.8) 57 (15.8) 0.003

Dyslipidemia, n (%) 274 (61.7) 220 (62.5) 0.748

One SNP, rs310585 of the NOS3 gene, was excluded from the analysis because genotyping failed. Considering an additive model, one SNP was associated with stroke: rs2276109 of the MMP12 gene. Moreover, one SNP showed a strong trend: rs7956957 in the LRP1 gene (Table 4).

Table 4. Results for the 6 SNPs genotyped in the Portuguese samples

SNP Risk controls cases p-value OR (95% CI) rsl0947803 A 21.0 21.1 0.958 NA

rs7956957 G 66.2 70.2 0.080 NA

rs2276109 G 9.2 12.2 0.045 1.38 (1.01-1.89) rsl0275136 C 93.5 94.0 0.673 NA

rs310585 A NA NA NA NA

rs5742912 T 86.8 96.5 3.1E-07 NA

Interestingly, the combination of 6 SNPs showed clear contribution to stroke risk in comparison with the cumulative risk obtained by conventional clinical risk factors only, including age, male gender, smoking, hypertension and diabetes, with areas under the ROC curves of 0.687 (95%CI: 0.589-0.669) vs. 0.629 (95%CI: 0.649-0.725) (Figure 1). Moreover the risk increased with the number of genetic variants carried (Figure 2), and the risk increased with the number of genetic and/or clinical risk variable carried, although nobody in our population carried more than 9 risk factors (Figure 3). Finally, we designed a predictive risk model to draw categories of low to high risk subjects. The six clinical variables and six replicated genetic variants were introduced in the model. All variables were independent one from another. Hosmer and Lemeshow test showed that our model adjusted well to the data. A risk score was assigned to each variable and a scale of risk from 1 to 45 (Figure 4) was then used to classify subjects into three categories. Stroke risk was defined as very low (score < 18), moderate (19 < score < 26) and high (35 < score) (Figure 5).

2.2.2.- Functional analysis

SCN 1A gene expression, measured in 26 healthy controls revealed that TT carriers had significantly higher mRNA levels than CT and CC carriers (TT: 109±40%, CT: 60±28%, CC: 58±25 %, p=0.005). However, LRP1 gene expression was determined in 18 healthy controls but no association between the rs7956957 SNP and mRNA levels could be observed (GG: 1 14±73%, CG: 105±60%, CC: 96±47%, p=0.578). Also, mRNA levels of the KCNK17 gene depended on the alleles of the rs 10947803 SNP (p=0.021), with A carriers presenting higher levels than C carriers (1 14 ± 35 %, n=5 vs. 77 ± 38 %, n=8).

Moreover, NOS3 protein activity determined in 80 healthy controls revealed that both risk alleles for IS of the rs310585 and rsl0275136 SNPs showed lower NOS3 activity (A: 6.8±2.0 μΜ, G: 8.0±3.4 μΜ, p=0.005 and C: 7.2±2.7 μΜ, T: 8.8±3.6 μΜ, p=0.027). However, MMP12 gene expression could not be detected in 12 healthy controls. MMP12 protein level was determined in 14 healthy controls by Luminex and we could detect a slight presence of MMP12 in 6 samples but no association with rs2276109 genotypes was observed (AA: 22.4 ±

13.3, AG: 18.1 ± 12.8, GG: 1 1.6 pg/mg of total protein, p=0.704).

Finally, microarrays analysis in 12 subjects revealed that rs7956957 genotypes of the LRP gene were associated with the expression of several genes after correction by false discovery rate (FDR) and the Ingenuity Pathway Analysis (IP A) software identified several networks associated with the LRP genotypes. Noticeably, the risk allele of the rs7956957 SNP

(G allele) was associated with lower expression of genes involved in inflammatory processes, such as CLEC4C, complements 4A and 4B (C4A and C4B), while it was associated with higher expression of genes involved in apoptosis such as ITGB3, ALOX12, NEK1 or G0S2. Moreover, the expression of eight genes was in concordance in both the CG vs. CC and GG vs. CC comparisons. 2.3.- Discussion

In this case-control study, the inventors analyzed the association of 221 SNPs functionally related to neurovascular diseases in two Mediterranean populations. After a multivariate analysis, adjusted for conventional risk factors, the inventors could replicate a statistically significant association between six SNPs (rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803) in the Spanish population and one SNP (rs2276109) in the Portuguese population, although another SNP (rs7956957) showed a strong trend to association and the risk of neurovascular accident, or stroke.

The rs5742912 of SCNNIA is an important structural change of amino-acid from tryptophan to arginine (Trp493-^Arg), so from cyclic non-polar to positively charged. The gene codes for a subunit of the eNaC protein, a nonvoltage-gated sodium channel, which is present in epithelial cells, together with two other subunits, SCNN1B and SCNNIG [17]. The role of this protein in sodium homeostasis, plasma osmolality and blood pressure regulation has converted it into an ideal study candidate for the development of complex diseases such as stroke, myocardial infarction or renal failure [18]. Indeed, the eNaC is responsible for the rate- limiting step of sodium reabsorption and is sensitive to amiloride, a diuretic that can block the channel and inhibit sodium assimilation, used in the management of hypertension and congestive heart failure [19]. At the genetic level, the rs5742912 SNP of SCNNIA has been associated with ischemic stroke [1 1]. Furthermore, the association resisted correction by Bonferroni and reached a p-value of 10^"7 and an OR of 3.59, which is quite rare for candidate gene approaches, the inventors also demonstrated here for the first time that this SNP is associated with changes in SCNNIA mRNA levels, as carriers of the T risk allele showed significantly higher levels than carriers of the C allele, although being a non- synonymous SNP, we expected that it could more likely affect protein activity, especially since the SNP was located in the extracellular loop, where the amiloride binds. These results are consistent with functional studies showing that other variants in the a-subunit of the human epithelial Na+ channel increase activity of the protein [20], as the inventors observe higher mRNA levels associated with the T risk allele, which is also the more common allele. SCNNIA is located in chromosome 12pl3, where a recent GWAS showed a strong association of a genetic locus with stroke, near NINJ2, indicating that this region could indeed be an important susceptibility region for stroke [5].

Another SNP located in chromosome 12 was associated with ischemic stroke in our study. Indeed, the G allele of the rs7956957 in LRP1 at 12ql3 was identified as a risk variant in all stages of our study and in all stroke etiologies, although it was not associated to mRNA levels. The SNP is in weak linkage with many other SNPs, although the only functional SNP (C766T or rs 1799986) described is located in another region of the gene, which spans 89 exons, and is not in linkage disequilibrium with the rs7956957 SNP. Nothing is known about the rs7956957 SNP, an intronic SNP that was selected in this study as a Tag-SNP for LRPl. However, the LRP or low density lipoprotein receptor-related protein has been studied in ischemic stroke, because of its role in the upregulation of the matrix metalloproteinase 9 (MMP-9), which plays a key role in the regulation of the extracellular matrix. Its upregulation has been associated with blood brain barrier disruption after thrombolytic therapy by tissue plasminogen activator (tPA) [21]. The LRP also plays a role in the clearance of MMPs through the a2-macroglobulin [22]. The effect of the rs7956957 on IS development was further examined through microarrays and we identified several interesting genes which presented different levels of expression depending on the rs7956957 genotypes such as ALOX12, ITGB3, MPL, C4A and C4B. A merged representation of the two main networks identified by the IPA software revealed that the genes involved could have an important role in biological processes such as apoptosis, cell signaling, cell death and inflammation. The merge also indicate that some genes such as LDL, NFKB, CDKNIA, and p38-MAPK could be important regulators in these networks. The mechanism of the rs7956957 effect on ischemic stroke is still unclear and might be related to splicing events, or protein activity modulating, since it does not affect LRPl expression, although we noticed that ischemic stroke cases presented significantly lower LRPl mRNA levels than healthy controls.

Two other genes that have been identified in our study, MMP12 and NOS3, are both involved in processes of inflammation. The MMP12 protein has been related to ischemic stroke thanks to its role in atherosclerotic plaque stability , while the MMP12 gene, located at chromosome l lq22 and also called human macrophage metalloelastase, has been related to coronary artery disease and hemorrhagic stroke [23-24]. The rs2276109 SNP that was associated with ischemic stroke in our cohort is a A to G substitution in the promoter region of the gene, and has been shown to be functional, by influencing the capacity of binding of the transcription factor activated protein- 1 (AP-1) in electromobility shift assays [25]. The A allele was associated with higher MMP12 promoter activity in vitro in transient transfection studies, and was associated with lower risk of ischemic stroke in our study, since the G allele was the less frequent risk allele. It thus seems that lower MMP12 promoter activity, through the G allele of the rs2276109 SNP, could be associated with higher ischemic stroke risk, but this hypothesis needs to be confirmed in further studies. We did intend to measure MMP12 gene expression and protein levels in healthy controls in our study, in order to understand better the contribution of the rs2276109 variant, but mRNA levels and protein levels were barely detectable and insufficient to draw any conclusion. We used blood samples and more precisely lymphocytes, whereas the MMP12 is principally expressed in activated macrophages and stroma cells, which could explain why we did not detect it. Thus, we speculated that the MMP12 was overexpressed during the inflammation process, and could be detected in stroke cases but not in healthy controls, but we did not detect it in stroke patients collected in the acute phase either.

The NOS3, at chromosome 7q36, was also remarkably associated with stroke in this study. Indeed, we observed an association of two SNPs in this gene, rs 10275136 and rs310585, the latter reaching p-values of 10^"5. Both associations passed Bonferroni's correction and showed strong results in the bootstrap analysis, with rates of 85.7% and 95.3% respectively. We established that the 2 SNPs were not in linkage disequilibrium and were thus independent between each others and were located in the 5' region of NOS3. They were selected as Tag SNPs and their function is unknown. The nitric oxide synthase 3 (NOS3) protein, also known as endothelial NOS (eNOS) or constitutive NOS (cNOS), has been involved in a variety of biological processes, including hypertension and coronary spasms [26]. It catalyzes the generation of nitric oxide in blood vessels from L-arginine, and is particularly important in cellular signaling and vascular function regulation [27]. At the genetic level, polymorphisms in this gene have been associated with stroke, myocardial infarction, hypertension and coronary heart [28]. Interestingly, whereas all 3 other SNPs were associated with any subtype of ischemic stroke, these two variants were specific to the cardioembolic etiology, which indicates that they might be associated with other cardiovascular disorders. Moreover, both risk alleles were associated with lower NOS3 protein activity and IS cases presented lower levels than healthy controls (data not shown), which could indicate that lower NOS3 activity increases ischemic stroke risk. This is in concordance with previous studies showing that the eNOS is an important regulator of cerebral blood flow, especially during cerebral ischemia, where it has been shown to present neuroprotective effects through improved perfusion and limited infarct size [29-30].

The A allele of the rs 10947803 SNP of the KCNK17 gene was independently associated with ischemic stroke in our study with an OR of 1.47 (p=0.010), thus supporting a role for the KCNK17 gene in ischemic stroke. Little is known about the KCNK17 gene, but it is expressed widely, and especially in liver, lung, placenta, pancreas, small intestine and aorta

[31]. Moreover, A carriers presented higher levels of KCNK17 mRNA than C carriers. It is thus possible that higher mRNA levels of the KCNK17 gene are associated with a higher risk of ischemic stroke. The mechanism is unknown for the function of the rs 10947803 variant. The SNP is intronic and thus does not belong to the promoter region. The protein coded by the KCNK17 gene is a member of the 2-pore domain superfamily of K⁺ channels [31-32]. Those channels participate in ion fluxes necessary for cell volume regulation as well as in metabolic acidosis and hypotension caused by secretion of HCO^"3 [33-35] and considering their function and localization in a wide range of tissues, they might then play an important role in ischemic stroke, and might be a main trigger common to all stroke subtypes.

Overall, we observed that six SNPs were independent risk factors for stroke in the

Spanish population. Four of those SNPs, in the KCNK17, SCNN1A and NOS3, had a clear influence on the function of the corresponding gene or protein, and one SNP in the MMP12 had also a clear effect as described in another study [25].

We next evaluated the cumulative association of these variants. Our main objective was to establish if the combination of these six SNPs could predict stroke risk better than each SNP individually, and if we could improve risk discrimination between subjects, in combination with classical risk factors, in opposite to classical risk factors alone. The results of this analysis showed that when all combinations of SNPs were analyzed, the risk increased gradually with the number of risk allele carried. Indeed, the cumulative association of the 6 SNPs had a higher positive predicted value than each SNP individually. In particular, we saw that carriers of all risk alleles presented nearly 100% risk of suffering a stroke. Furthermore, the contribution of genetic information to classical risk factors was not negligible, as indicated by the drawing of ROC curves of clinical or clinical and genetic factors, so we assigned a score to each SNP and conventional risk factor, depending on their effect on stroke. The predictive model finally obtained permitted to draw ischemic stroke risk categories, and to classify subjects in low, moderate or high risk of suffering an ischemic stroke. The inventors believe that this model could be an important tool in preventive medicine, since stroke is a huge health burden and patients could benefit from individualized and targeted primary prevention. Indeed, the combination of SNPs presented here bring new and important clinical information about the subjects, at least to the same level as classical risk factors such as hypertension, cigarette smoking or diabetes. In particular, the results obtained in our study will permit to classify better subjects that are considered to have a moderate risk based on clinical factors only, and that switch to the high risk of disease group based on this model of genetic and clinical combination.

With the present study, the inventors surprisingly found that using the combination of six SNPs disclosed above, the risk prediction measured by OR was already better than anything described in the literature before, and the inventors were able to foresee that the risk of suffering a stroke exponentially increased as the number of SNPs in the model increased, as shown in the following table. In other words, the combination of six SNPs disclosed above allows foreseeing the individual's risk for stroke in a ratio at least 26 times higher than a person which, at the same environmental conditions, does not present any of these SNPs. As shown in the table below, this odd ratio dramatically and surprisingly increases as the number of SNPs increases, not just through a summatory effect, but through a multiplicative effect, the optimal model being when the number of SNPs is six. In other words, the combination of two, three, four, or five SNPs is gradually better up to six SNPs disclosed herein which is the optimal combination to predict the risk of stroke.

Table 3. Combinations of SNPs significantly associated with stroke

Methods and Kits for its implementation

The present invention also relates to a method for determining a genetic predisposition to neurovascular diseases in an individual, comprising detecting the alleles of six SNPs consisting of rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803 in an isolated nucleic acid sample of an individual. Although the present method of detection may be carried out with less SNPs, only the combination of the six SNPs used herein provides efficient and satisfactory results.

In a particular embodiment, the alleles detected according to the embodiments for the method of the present invention are a G allele for rs7956957, A allele for rs310585, G allele for rs2276109, C allele for rs 10275136, T allele for rs5742912 or A allele for rs 10947803.

The isolated nucleic acid sample of the individual used in any of the embodiments for the method of the present invention may comprise DNA or RNA. Furthermore, said nucleic acid sample can be amplified, usually by a polymerase chain reaction (PCR) in the way well known in the art.

The detection of the SNPs can be carried out by any of the methods well known in the art, for example, without being limited to, hybridization of a nucleic acid probe or oligonucleotide sequence, or by radioactive, enzymatic, luminescent or fluorescent markers [35: page 4183 and 36: pages 246-247]. Said probe or oligonucleotide sequence comprises a sequence that is fully complementary to a nucleic acid sequence comprising the SNPs disclosed in the present invention (SEC ID No. 1 to SEC ID No. 6). In a preferred embodiment, the present invention relates to the method according to any of the embodiments disclosed above wherein the six SNPs are combined with one or more clinical variables associated with a neurovascular disease. Preferably, said clinical variables are selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

In a particular embodiment, the present invention relates to the method according to any of the embodiments disclosed above wherein the alleles of said SNPs are inferred by genetic testing.

In another embodiment, the present invention relates to the method according to any of the embodiments disclosed above wherein the alleles of said SNPs are inferred from the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNN1A, KCNK17, MMP12 and/or LRP, which are modulated by the presence of the genetic markers, as shown in the results section.

In a particular embodiment, the present invention relates to the method according to any of the embodiments disclosed above wherein said six SNPs are combined with SNPs rs7193343, rs6007897, rs4044210, rsl l064005, rs22381 12, rsl2319392, rs2228576, rs9634156, rsl3376333, rs6725887, rs2306374, rsl2526453, rs3798220, rsl 122608, rs9982601, rsl 801282, rs5219, rs3754777, rsl3333226, rs4977574, rsl0012946.

In a particular embodiment, the present invention relates to the method according to any of the embodiments disclosed above further comprising comparing the combination of alleles obtained to that of other individuals affected by neurovascular diseases, and detecting a genetic predisposition to a neurovascular disease if the individual to be diagnosed presents the same genetic background as affected individuals. In a more preferred embodiment, said other individuals have a blood relationship with the individual.

Preferably, in all the previous method embodiments said neurovascular disease is stroke.

In a particular embodiment, the method according to any of the embodiments disclosed above for determining an individual's risk for a neurovascular disease, preferably stroke, is applied to an individual belonging to the Mediterranean population.

In a more particular embodiment, said individual belongs to the Spanish population. The present invention further relates to a kit for determining an individual's risk for a neurovascular disease comprising:

(a) Six probes that permit to detect the alleles of SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803.

(b) Instructions to use the kit to determine the individual's risk of stroke considering the combination of SNPs carried. Said six probes in the kit can be labeled. Optionally, the kit can comprise reagents such as dilution buffers, reaction buffers, polymerase enzymes and/or primers. When using radiolabeled probes, hybridization can be detected by autoradiography, scintillation, counting or gamma counting.

In certain embodiments, the kit can further comprise amplification or sequencing primers which can be sequence-specific.

The kit can also comprise reagents for labeling one or more of the sequence-specific oligonucleotides, or comprise labeled sequence-specific oligonucleotides. Useful labels include radioisotopes as well as non-radioactive reporting groups. Isotopic labels include ³H,

35 32 125 57 14

J^JS, ^J , ^{^}I, ^D'Co and "C. Isotopic labels can be introduced into the oligonucleotide by techniques known in the art. Non-isotopic labels include fluorescent molecules, chemiluminiscent molecules, enzymes, cofactors, enzyme substrates, haptens or other ligands.

In a preferred embodiment, the present invention relates to the kit according to any of the embodiments disclosed above wherein the six SNPs are combined with one or more clinical variables associated with a neurovascular disease. Preferably, said clinical variables are selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

In a particular embodiment, in the kit according to the previous embodiments, the alleles of said SNPs are inferred by genetic testing of other markers or by measuring the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNN 1 A, KCNK 17, MMP 12 and/or LRP.

Preferably, in all the previous kit embodiments said neurovascular disease is stroke.

In a particular embodiment said kit for determining an individual's risk for neurovascular disease, preferably stroke, is applied to an individual belonging to the Mediterranean population.

In a more particular embodiment, said individual belongs to the Spanish population.

The present invention further relates to the use of the combination of six SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803 and all the combination embodiments disclosed herein in a kit for determining an individual's risk for a neurovascular disease, preferably stroke. In a particular embodiment, said use is applied to an individual belonging to the Mediterranean population.

In a more particular embodiment, said individual belongs to the Spanish population.

The present invention further relates to the use of the combination of the six SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of neurovascular disease, preferably stroke, and all the combination embodiments disclosed herein as a genetic testing tool for a neurovascular disease, preferably stroke. In a particular embodiment, said use is applied to an individual belonging to the Mediterranean population.

In a more particular embodiment, said individual belongs to the Spanish population. REFERENCES

1. Lloyd- Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, Ford E, Furie K, Go A, Greenlund K, Haase N, Hailpern S, Ho M, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott M, Meigs J, Mozaffarian D, Nichol G, O'Donnell C, Roger V, Rosamond W, Sacco R, Sorlie P, Stafford R, Steinberger J, Thorn T, Wasserthiel-Smoller S, Wong N, Wylie-Rosett J, Hong Y. Heart disease and stroke statistics- 2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009; 1 19:e21-181.

2. Jorgensen N, Cabanas M, Oliva J, Rejas J, Leon T. The cost of informal care associated to incapacitating neurological disease having high prevalence in Spain. Neurologia. 2008; 23:29- 39.

3. Domingues-Montanari S, Mendioroz M, del Rio-Espinola A, Fernandez- Cadenas I, Montaner J. Genetics of stroke: a review of recent advances. Expert Rev Mol Diagn. 2008; 8:495-513.

4. Domingues-Montanari S, Fernandez-Cadenas I, Del Rio-Espinola A, Corbeto N, Krug T, Manso H, Gouveia L, Sobral J, Mendioroz M, Fernandez-Morales J, Alvarez- Sabin J, Ribo M,

Rubiera M, Obach V, Marti-Fabregas J, Freijo M, Serena J, Ferro JM, Vicente AM, Oliveira SA, Montaner J. Association of a genetic variant in the ALOX5AP gene with higher risk of ischemic stroke. A case-control, meta-analysis and functional study. Cerebrovasc Dis. 2010; 29:528-537.

5. Ikram MA, Seshadri S, Bis JC, Fornage M, DeStefano AL, Aulchenko YS, Debette S,

Lumley T, Folsom AR, van den Herik EG, Bos MJ, Beiser A, Cushman M, Launer LJ, Shahar E, Struchalin M, Du Y, Glazer NL, Rosamond WD, Rivadeneira F, Kelly-Hayes M, Lopez OL, Coresh J, Hofman A, DeCarli C, Heckbert SR, Koudstaal PJ, Yang Q, Smith NL, Kase CS, Rice K, Haritunians T, Roks G, de Kort PL, Taylor KD, de Lau LM, Oostra BA,

Uitterlinden AG, Rotter JI, Boerwinkle E, Psaty BM, Mosley TH, van Duijn CM, Breteler

MM, Longstreth WT, Jr., Wolf PA. Genomewide association studies of stroke. N Engl J Med. 2009; 360: 1718- 1728.

6. International Stroke Genetics Consortium; Wellcome Trust Case-Control Consortium 2. Failure to validate association between 12pl3 variants and ischemic stroke. N Engl J Med. 2010; 362(16): 1547-50. 7. Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, Schnabel RB, Bis JC, Boerwinkle E, Sinner MF, Dehghan A, Lubitz SA, DAgostino RB, Sr., Lumley T, Ehret GB, Heeringa J, Aspelund T, Newton-Cheh C, Larson MG, Marciante KD, Soliman EZ, Rivadeneira F, Wang TJ, Eiriksdottir G, Levy D, Psaty BM, Li M, Chamberlain AM, Hofman A, Vasan RS, Harris TB, Rotter JI, Kao WH, Agarwal SK, Strieker BH, Wang K, Launer LJ, Smith NL, Chakravarti A, Uitterlinden AG, Wolf PA, Sotoodehnia N, Kottgen A, van Duijn CM, Meitinger T, Mueller M, Perz S, Steinbeck G, Wichmann HE, Lunetta KL, Heckbert SR, Gudnason V, Alonso A, Kaab S, Ellinor PT, Witteman JC. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009; 41 :879-881. 8. Kubo M, Hata J, Ninomiya T, Matsuda K, Yonemoto K, Nakano T, Matsushita T,

Yamazaki K, Ohnishi Y, Saito S, Kitazono T, Ibayashi S, Sueishi K, Iida M, Nakamura Y, Kiyohara Y. A nonsynonymous SNP in PRKCH (protein kinase C eta) increases the risk of cerebral infarction. Nat Genet. 2007; 39:212-217.

9. Yamada Y, Fuku N, Tanaka M, Aoyagi Y, Sawabe M, Metoki N, Yoshida H, Satoh K, Kato K, Watanabe S, Nozawa Y, Hasegawa A, Kojima T. Identification of CELSR1 as a susceptibility gene for ischemic stroke in Japanese individuals by a genome-wide association study. Atherosclerosis. 2009; 207: 144-9.

10. Domingues-Montanari S, Fernandez-Cadenas I, Del Rio-Espinola A, Mendioroz M, Fernandez- Morales J, Corbeto N, Delgado P, Ribo M, Rubiera M, Obach V, Marti-Fabregas J, Freijo M, Serena J, Montaner J. KCNK17 genetic variants in ischemic stroke. Atherosclerosis. 2010; 208(l):203-9.

11. Hsieh K, Lalouschek W, Schillinger M, Endler G, Reisinger M, Janisiw M, Lang W, Cheng S, Wagner O, Mannhalter C. Impact of alphaENaC polymorphisms on the risk of ischemic cerebrovascular events: a multicenter case-control study. Clin Chem. 2005;

51(6):952-6.

12. Munoz X, Sumoy L, Ramirez-Lorca R, Villar J, de Frutos PG, Sala N. Human vitamin Independent GAS6: gene structure, allelic variation, and association with stroke. Hum Mutat. 2004; 23(5):506-12.

13. Obach V, Revilla M, Vila N, Cervera A A, Chamorro A A. alpha(l)-antichymotrypsin polymorphism: a risk factor for hemorrhagic stroke in normotensive subjects. Stroke. 2001 ;

32(1 1):2588-91.

14. Liu J, Sun K, Bai Y, Zhang W, Wang X, Wang Y, Wang H, Chen J, Song X, Xin Y, Liu Z, Hui R. Association of three-gene interaction among MTHFR, ALOX5AP and NOTCH3 with thrombotic stroke: a multicenter case-control study. Hum Genet. 2009; 125(5-6):649-56. 15. Montaner J, Fernandez-Cadenas I, Molina CA, Ribo M, Huertas R, Rosell A, Penalba A, Ortega L, Chacon P, Alvarez- Sabin J. Poststroke C-reactive protein is a powerful prognostic tool among candidates for thrombolysis. Stroke. 2006; 37: 1205- 10.

16. Rosa A, Fonseca BV, Krug T, Manso H, Gouveia L, Albergaria I, Gaspar G, Correia M, Viana-Baptista M, Simoes RM, Pinto AN, Taipa R, Ferreira C, Fontes JR, Silva MR, Gabriel

JP, Matos I, Lopes G, Ferro JM, Vicente AM, Oliveira SA. Mitochondrial haplogroup HI is protective for ischemic stroke in Portuguese patients. BMC Med Genet. 2008; 9:57.

17. Ludwig M, Bolkenius U, Wickert L, Marynen P, Bidlingmaier F. Structural organisation of the gene encoding the alpha-subunit of the human amiloride-sensitive epithelial sodium channel. Hum Genet. 1998; 102:576-581.

18. Rossier BC. 1996 Homer Smith Award Lecture. Cum grano salis: the epithelial sodium channel and the control of blood pressure. J Am Soc Nephrol. 1997; 8:980-992.

19. Corvol P, Persu A, Gimenez-Roqueplo AP, Jeunemaitre X. Seven lessons from two candidate genes in human essential hypertension: angiotensinogen and epithelial sodium channel. Hypertension. 1999; 33: 1324- 1331.

20. Tong Q, Menon AG, Stockand JD. Functional polymorphisms in the alpha-subunit of the human epithelial Na+ channel increase activity. Am J Physiol Renal Physiol. 2006; 290:F821- F827.

21. Wang X, Lee SR, Arai K, Lee SR, Tsuji K, Rebeck GW, Lo EH. Lipoprotein receptor- mediated induction of matrix metalloproteinase by tissue plasminogen activator. Nat Med.

2003; 9: 1313- 1317.

22. Montaner J, Molina CA, Monasterio J, Abilleira S, Arenillas JF, Ribo M, Quintana M, Alvarez-Sabin J. Matrix metalloproteinase-9 pretreatment level predicts intracranial hemorrhagic complications after thrombolysis in human stroke. Circulation. 2003; 107:598- 603.

23. Lamblin N, Bauters C, Hermant X, Lablanche JM, Helbecque N, Amouyel P.

Polymorphisms in the promoter regions of MMP-2, MMP-3, MMP-9 and MMP-12 genes as determinants of aneurysmal coronary artery disease. J Am Coll Cardiol. 2002; 40:43-48.

24. Zhang B, Dhillon S, Geary I, Howell WM, Iannotti F, Day IN, Ye S. Polymorphisms in matrix metalloproteinase- 1 , -3, -9, and - 12 genes in relation to subarachnoid hemorrhage.

Stroke. 2001 ; 32:2198-2202.

25. Jormsjo S, Ye S, Moritz J, Walter DH, Dimmeler S, Zeiher AM, Henney A, Hamsten A, Eriksson P. Allele-specific regulation of matrix metalloproteinase- 12 gene activity is associated with coronary artery luminal dimensions in diabetic patients with manifest coronary artery disease. Circ Res. 2000; 86:998-1003. 26. Marsden PA, Schappert KT, Chen HS, Flowers M, Sundell CL, Wilcox JN, Lamas S, Michel T. Molecular cloning and characterization of human endothelial nitric oxide synthase. FEBS Lett. 1992; 307:287-293.

27. Garcia-Cardena G, Fan R, Stern DF, Liu J, Sessa WC. Endothelial nitric oxide synthase is regulated by tyrosine phosphorylation and interacts with caveolin- 1. J Biol Chem. 1996;

271 :27237-27240.

28. Casas JP, Cavalleri GL, Bautista LE, Smeeth L, Humphries SE, Hingorani AD.

Endothelial nitric oxide synthase gene polymorphisms and cardiovascular disease: a HuGE review. Am J Epidemiol. 2006; 164:921-935.

29. Sironi L, Cimino M, Guerrini U, Calvio AM, Lodetti B, Asdente M, Balduini W, Paoletti R, Tremoli E. Treatment with statins after induction of focal ischemia in rats reduces the extent of brain damage. Arterioscler Thromb Vase Biol. 2003; 23:322-327.

30. Huang Z, Huang PL, Ma J, Meng W, Ayata C, Fishman MC, Moskowitz MA. Enlarged infarcts in endothelial nitric oxide synthase knockout mice are attenuated by nitro-L-arginine. J Cereb Blood Flow Metab. 1996; 16:981-987.

31. Decher N, Maier M, Dittrich W, Gassenhuber J, Briiggemann A, Busch AE,Steinmeyer K. Characterization of TASK-4, a novel member of the pH-sensitive, two-pore domainpotassium channel family. FEBS Lett 2001 ; 492:84-9.

32. Girard C, Duprat F, Terrenoire C, Tinel N, Fosset M, Romey G, Lazdunski M,Lesage F. Genomic and functional characteristics of novel human pancreatic 2P domain K(+)channels.

Biochem Biophys Res Commun 2001; 282:249-56.

33. Niemeyer MI, Cid LP, Barros LF, Sepulveda FV. Modulation of the two-pore domain acid-sensitive K+ channel TASK-2 (KCNK5) by changes in cell volume. J Biol Chem 2001 ; 276:43166-74.

34. Barriere H, Belfodil R, Rubera I, Tauc M, Lesage F, Poujeol C, Guy N, BarhaninJ, Poujeol

P. Role of TASK2 potassium channels regarding volume regulation in primary cultures of mouse proximal tubules. J Gen Physiol 2003; 122: 177-90.

35. Duprat F, Girard C, Jarretou G, Lazdunski M. Pancreatic two P domain K+ channels TALK- 1 and TALK-2 are activated by nitric oxide and reactive oxygen species. J Physiol 2005; 562:235-44.

36. Niemeyer MI, Gonzalez-Nilo FD, Zuniga L, Gonzalez W, Cid LP, Sepulveda FV.

Neutralization of a single arginine residue gates open a two-pore domain, alkali-activated K+ channel. Proc Natl Acad Sci USA 2007; 104:666-71.

37. LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res. 2009; 37(13):4181-93. 38. Ding C, Jin S. High-throughput methods for SNP genotyping. Methods Mol Biol. 2009; 578:245-54.

SEQUENCE LISTING

<1 10> FUNDACIO INSTITUT DE RECERCA HOSPITAL UNIVERSITARI VALL

D'HEBRON

<120> COMBINATION OF SIX SNPs FOR DETECTING THE PREDISPOSITION TO NEUROVASCULAR DISEASES

<130> P25337ES00

<160> 6

<170> Patentln version 3.3

<210> 1

<21 1> 40

<212> DNA

<213> Homo Sapiens

<220>

<221> Variation

<222> (21).. (21)

<223> n = a or g

<400> 1

atcaagggatgatatcaactNtgagtcactcataggattca 40

<210> 2

<21 1> 40

<212> DNA

<213> Homo Sapiens

<220>

<221> variation

<222> (21).. (21)

<223> n = t or c

<400> 2

actgagagcccccagtgcccNggcccgtactcagagcacgg 40

<210> 3

<21 1> 40

<212> DNA

<213> Homo Sapiens

<220> <221> variation

<222> (21).. (21)

<223> n = g or a

<400> 3

ctccccatccatccatccacNcatctacccacccttctacc 40 <210> 4

<21 1> 40

<212> DNA

<213> Homo Sapiens

<220>

<221> variation

<222> (21).. (21)

<223> n = c or g

<400> 4

ggaggcagttctttccacccNgagcctgggttggggaggcc 40

<210> 5

<21 1> 40

<212> DNA

<213> Homo Sapiens

<220>

<221> variation

<222> (21).. (21)

<223> n = c or a

<400> 5

ctcccaggatgagggagatcNagaagggggcagtgtgaggc 40

<210> 6

<211> 40

<212> DNA

<213> Homo Sapiens

<220>

<221> variation

<222> (21).. (21)

<223> n = c or t

<400> 6

tctctgctggttactcacgaNggccctcggtgacatcccag 40

Claims

1.- A combination of six single nucleotide polymorphisms (SNPs), namely rs2276109, rs7956957, rs310585, ,rsl0275136, rs5742912 and rsl0947803, indicative of an individual's risk of neurovascular disease.

2.- The combination according claim 1 wherein said six SNPs are combined with one or more of SNPs rs7193343, rs6007897, rs4044210, rsl 1064005, rs22381 12, rsl2319392, rs2228576, rs9634156, rsl3376333, rs6725887, rs2306374, rsl2526453, rs3798220, rsl 122608, rs9982601, rsl 801282, rs5219, rs3754777, rsl3333226, rs4977574, rsl0012946.

3- The combination according to claims 1 and 2, wherein said single nucleotide polymorphisms are combined with one or more clinical variables associated with a neurovascular disease.

4. - The combination according to claim 3, wherein said clinical variable is selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

5. - A combination of six isolated polynucleotides included in neurovascular disease- associated genes comprising each of said polynucleotides the SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803, indicatives of an individual's risk of neurovascular disease, said isolated polynucleotides corresponding to SEC ID No. 1 to 6

6. - The combination according to any of claims 1 to 5 wherein the alleles of said SNPs are inferred by genetic testing or by measuring the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNN1A, KCNK17, MMP12 and/or LRP.

7. - The combination according to any of the preceding claims wherein the neurovascular disease is stroke.

8. - A method for determining a genetic predisposition to neurovascular diseases in an individual, comprising detecting the alleles of six SNPs, namely rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803 in a isolated nucleic acid sample of an individual.

9. - The method according to claim 8, wherein a G allele for rs7956957, A allele for rs310585, G allele for rs2276109, C allele for rsl0275136, T allele for rs5742912 or A allele for rs 10947803 are detected.

10. - The method according to any of claims 8-9, wherein the nucleic acid sample comprises DNA.

1 1. - The method according to any of claims 8-9, wherein the nucleic acid sample comprises RNA.

12. - The method according to any of claims 8-1 1, wherein the SNPs are detected by hybridization of a nucleic acid probe or oligonucleotide sequence, or by radioactive, enzymatic, luminescent or fluorescent markers.

13. - The method according to any of claims 8-12, wherein said six SNPs are combined with one or more clinical variables associated with neurovascular diseases.

14. - The method according to claim 13, wherein said clinical variables are selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

15. - The method according to any of claims 8 to 14, wherein the alleles of said SNPs are inferred by genetic testing or by measuring the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNNIA, KCNK17, MMP12 and/or LRP.

16. - The method according to any of claims 8 to 15, wherein said six SNPs are combined with one or more of SNPs rs7193343, rs6007897, rs4044210, rsl 1064005, rs22381 12, rsl2319392, rs2228576, rs9634156, rsl3376333, rs6725887, rs2306374, rsl2526453, rs3798220, rsl 122608, rs9982601, rsl 801282, rs5219, rs3754777, rsl3333226, rs4977574, rsl 0012946.

17. - The method according to any of claims 8 to 16, further comprising comparing the combination of alleles obtained to that of other individuals affected by a neurovascular disease, and detecting a genetic predisposition to it if the individual to be diagnosed presents the same genetic background as affected individuals.

18. - The method according to claim 17, wherein the other individuals have a blood relationship with the individual diagnosed.

19. - The method according to any of claims 8 to 18, wherein said neurovascular disease is stroke.

20.- A kit for determining an individual's risk for neurovascular diseases comprising,

(a) Six probes that permit to detect the alleles of SNPs rs2276109, rs7956957, rs310585, rsl0275136, rs5742912 and rsl0947803.

(b) Instructions to use the kit to determine the individual's risk of neurovascular diseases considering the combination of SNPs carried.

21.- The kit according claim 20, wherein one or more probes are labeled.

22. - The kit according to any of claims 20 to 21 that includes a reagent to detect the label.

23. - The kit according to any of claims 20 to 22, wherein said six SNPs are combined with clinical variables associated with stroke.

24. - The kit according to claim 23, wherein said clinical variables are selected from age, gender, smoking, hypertension, diabetes and dyslipidemic status.

25. - The kit according to claims 20 to 24, wherein the alleles of said SNPs are inferred by genetic testing or by measuring the levels of activity or concentration of the proteins or corresponding RNA expression levels of NOS3, SCNNIA, KCNK17, MMP12 and/or LRP.

26. - The kit according to any of claims 20 to25, wherein said neurovascular disease is stroke.

27. - Use of the combination according to any of claims 1 to 7 in a kit for determining an individual's risk for neurovascular diseases.

28.- Use of the combination according to any of claims 1 to 7 as a genetic testing tool for neurovascular diseases.

29. - Use according to claim 27 or 28 wherein said neurovascular disease is stroke.

30. - The combination according to any of claims 1 to 7, wherein said individual belongs to the Mediterranean population.

31.- The combination according to any of claims 1 to 7, wherein said individual belongs to the Spanish population.

32. - The method according to any of claims 8 to 19, wherein said individual belongs to the Mediterranean population.

33. - The method according to any of claims 8 to 19, wherein said individual belongs to the Spanish population.

34. - The kit according to any of claims 20 to 26, wherein said individual belongs to the Mediterranean population.

35. - The kit according to any of claims 20 to 26, wherein said individual belongs to the Spanish population.

36.- The use according to any of claims 27 to 29, wherein said individual belongs to the Mediterranean population.

37.- The use according to any of claims 27 to 29, wherein said individual belongs to the Spanish population.