Stroke Genome-Wide Association Studies

The field of stroke genetics continues to make substantive advances. Large-scale partnerships have led to meta-analyses in ischemic and hemorrhagic stroke, which are yielding reproducible genetic risk factors. Most ischemic stroke risk factors seem to be specific to type, for example, large-vessel stroke. Progress is being made in understanding the pathophysiology of single-gene stroke syndromes. Genetics is also being used to potentially advance pharmacotherapeutics. In the genome-wide association study (GWAS) era, several themes are emerging. First, heritability seems to vary by ischemic stroke subtype. Using complex trait analysis on a GWAS data set of >3000 individuals, Bevan et al1 found a 37.9% heritability for ischemic stroke overall, but the heritability varied widely by subtype from a high of 40.3% for large-vessel stroke to a low of 16.1% for small-vessel stroke.1 Second, genetic risk factors seem to be subtype-selective. Third, GWAS have yielded more reliable discoveries than candidate gene studies uninformed by GWAS. In the previously cited study by Bevan,1 no candidate gene previously reported as associated with ischemic stroke could be replicated using GWAS data, but 3 loci from related cardiovascular GWAS were significant even after rigorous correction for multiple comparisons. In the area of ischemic stroke genetics, a partnership between the International Stroke Genetics Consortium and the Wellcome Trust Case Control Consortium 2, which included GWAS data from 5859 cases and 6281 controls, replicated associations …

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

Stroke is a complex disease with multiple genetic and environmental risk factors. Blacks endure a nearly 2-fold greater risk of stroke and are 2× to 3× more likely to die from stroke than European Americans. The COMPASS (Consortium of Minority Population Genome-Wide Association Studies of Stroke) has conducted a genome-wide association meta-analysis of stroke in >22 000 individuals of African ancestry (3734 cases, 18 317 controls) from 13 cohorts. In meta-analyses, we identified one single nucleotide polymorphism (rs55931441) near the HNF1A gene that reached genome-wide significance (P=4.62×10-8) and an additional 29 variants with suggestive evidence of association (P<1×10-6), representing 24 unique loci. For validation, a look-up analysis for a 100 kb region flanking the COMPASS single nucleotide polymorphism was performed in SiGN (Stroke Genetics Network) Europeans, SiGN Hispanics, and METASTROKE (Europeans). Using a stringent Bonferroni correction P value of 2.08×10-3 (0.05/24 unique loci), we were able to validate associations at the HNF1A locus in both SiGN (P=8.18×10-4) and METASTROKE (P=1.72×10-3) European populations. Overall, 16 of 24 loci showed evidence for validation across multiple populations. Previous studies have reported associations between variants in the HNF1A gene and lipids, C-reactive protein, and risk of coronary artery disease and stroke. Suggestive associations with variants in the SFXN4 and TMEM108 genes represent potential novel ischemic stroke loci. These findings represent the most thorough investigation of genetic determinants of stroke in individuals of African descent, to date.

In genome-wide association (GWA) studies, test statistics that are efficient and robust across various genetic models are preferable, particularly for studying multiple diseases in the Wellcome Trust Case-Control Consortium (WTCCC, 2007, Nature 447, 661-678). A new test statistic, the minimum of the p-values of the trend test and Pearson's test, was considered by the WTCCC. It is referred to here as MIN2. Because the minimum of two p-values is no longer a valid p-value itself, the WTCCC only used it to rank single nucleotide polymorphisms (SNPs) but did not report the p-values of the associated SNPs when MIN2 was used for ranking. Given its importance in practice, we derive the asymptotic null distribution of MIN2, study some of its analytical properties related to GWA studies, and compare it with existing methods (the trend test, Pearson's test, MAX3, and the constrained likelihood ratio test [CLRT]) by simulations across a wide range of possible genetic models: the recessive (REC), additive (ADD), multiplicative (MUL), dominant (DOM), and overdominant models. The results show that MAX3 and CLRT have greater efficiency robustness than other tests when the REC, ADD/MUL, and DOM models are possible, whereas Pearson's test and MIN2 have greater efficiency robustness if the possible genetic models also include the overdominant model. We conclude that robust tests (MAX3, MIN2, CLRT, and Pearson's test) are preferable to a single trend test for initial GWA studies. The four robust tests are applied to more than 100 SNPs associated with 11 common diseases identified by the two WTCCC GWA studies.

Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) at multiple loci that are significantly associated with coronary artery disease (CAD) risk. In this study, we sought to determine and compare the predictive capabilities of 9p21.3 alone and a panel of SNPs identified and replicated through GWAS for CAD. We used the Ottawa Heart Genomics Study (OHGS) (3323 cases, 2319 control subjects) and the Wellcome Trust Case Control Consortium (WTCCC) (1926 cases, 2938 control subjects) data sets. We compared the ability of allele counting, logistic regression, and support vector machines. Two sets of SNPs, 9p21.3 alone and a set of 12 SNPs identified by GWAS and through a model-fitting procedure, were considered. Performance was assessed by measuring area under the curve (AUC) for OHGS using 10-fold cross-validation and WTCCC as a replication set. AUC for logistic regression using OHGS increased significantly from 0.555 to 0.608 (P=3.59×10⁻¹⁴) for 9p21.3 versus the 12 SNPs, respectively. This difference remained when traditional risk factors were considered in a subgroup of OHGS (1388 cases, 2038 control subjects), with AUC increasing from 0.804 to 0.809 (P=0.037). The added predictive value over and above the traditional risk factors was not significant for 9p21.3 (AUC 0.801 versus 0.804, P=0.097) but was for the 12 SNPs (AUC 0.801 versus 0.809, P=0.0073). Performance was similar between OHGS and WTCCC. Logistic regression outperformed both support vector machines and allele counting. Using the collective of 12 SNPs confers significantly greater predictive capabilities for CAD than 9p21.3, whether traditional risks are or are not considered. More accurate models probably will evolve as additional CAD-associated SNPs are identified.

Background— Epidemiological studies indicate a genetic contribution to ischemic stroke risk, but specific genetic variants remain unknown, with the exception of a few rare variants. Recent genome-wide association studies identified and replicated common genetic variants on chromosome 9p21 to confer risk of coronary heart disease. We examined whether these variants are associated with ischemic stroke. Methods and Results— We genotyped 6 common genetic variants on chromosome 9p21, previously associated with coronary artery disease in genome-wide association studies, in 2 population-based studies in southern Sweden, the Lund Stroke Register (n=1837 cases, 947 controls) and the Malmö Diet and Cancer study (MDC; n=888 cases, 893 controls). We examined association in each study and in the pooled dataset. Adjustments were made for cardiovascular risk factors and further for previous myocardial infarction in MDC. We found a modest increase in ischemic stroke risk for 2 common (minor allele frequencies 0.46 to 0.49) variants, rs2383207 ( P =0.04 in Lund Stroke Register, P =0.01 in MDC) and rs10757274 ( P =0.03 in Lund Stroke Register, P =0.03 in MDC), in each sample independently. The strength of the association increased when samples were pooled with an odds ratio of 1.15 (95% CI, 1.05 to 1.25; P =0.002) for the strongest variant rs2383207. Results were similar after adjustment for clinical covariates. rs1333049 also showed significant association in MDC, which increased in the pooled sample ( P =0.004). Conclusions— In this large sample (n=4565), we detected common genetic determinants for ischemic stroke on chromosome 9p21. Our findings indicate that ischemic stroke shares pathophysiological determinants with coronary heart disease and other arterial diseases and highlight the need for large sample sizes in stroke genetics.

Background Associations of several Single Nucleotide Polymorphisms (SNPs) with common diseases like Coronary Artery Disease (CAD), Crohn’s Disease (CD), Hypertension (HT), Bipolar Disorder (BD), Type 1 Diabetes (T1D), Type 2 Diabetes (T2D) and Rheumatoid Arthritis (RA) were identified in a study conducted by the Wellcome Trust Case Control Consortium (WTCCC) (1). WTCCC study compared the effects of genetic variations in 14,000 cases and 3000 shared controls and identified 24 independent associations with the diseases mentioned above using the genotype information of approximately 500,000 directly genotyped SNPs and genotype information simulated at 2.8 million loci studied by the International Hapmap Project(2). We hypothesize that there are more chances of finding association of rare SNPs with diseases by refined analysis of non-synonymous SNPs (nsSNPs) in genome wide association studies. In the present study we analyzed the association of 12,660 nsSNPs using a case control study in the WTCCC population.

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

Given that substantial genetic components have been shown in ischemic stroke, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH), heritability may be higher in early-onset than late-onset individuals with these conditions. Although genome-wide association studies (GWASs) have identified various genes and loci significantly associated with ischemic stroke, ICH, or intracranial aneurysm mainly in European ancestry populations, genetic variants that contribute to susceptibility to these disorders remain to be identified definitively. We performed exome-wide association studies (EWASs) to identify genetic variants that confer susceptibility to ischemic stroke, ICH, or SAH in early-onset subjects with these conditions. A total of 6,649 individuals aged ≤65 years were examined. For the EWAS of ischemic or hemorrhagic stroke, 6,224 individuals (450 subjects with ischemic stroke, 5,774 controls) or 6,179 individuals (261 subjects with ICH, 176 subjects with SAH, 5,742 controls), respectively, were examined. EWASs were performed with the use of Illumina Human Exome-12 v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip. To compensate for multiple comparisons of allele frequencies with ischemic stroke, ICH, or SAH, we applied a false discovery rate (FDR) of <0.05 for statistical significance of association. The association of allele frequencies of 31,245 single nucleotide polymorphisms (SNPs) that passed quality control to ischemic stroke was examined with Fisher's exact test, and 31 SNPs were significantly (FDR <0.05) associated with ischemic stroke. The association of allele frequencies of 31,253 or 30,970 SNPs to ICH or SAH, respectively, was examined with Fisher's exact test, and six or two SNPs were significantly associated with ICH or SAH, respectively. Multivariable logistic regression analysis with adjustment for age, sex, and the prevalence of hypertension and diabetes mellitus revealed that 12 SNPs were significantly [P<0.0004 (0.05/124)] related to ischemic stroke. Similar analysis with adjustment for age, sex, and the prevalence of hypertension revealed that six or two SNPs were significantly [P<0.0016 (0.05/32)] related to ICH or SAH, respectively. After examination of linkage disequilibrium of identified SNPs and results of previous GWASs, we identified HHIPL2, CTNNA3, LOC643770, UTP20, and TRIB3 as susceptibility loci for ischemic stroke, DNTTIP2 and FAM205A as susceptibility loci for ICH, and FAM160A1 and OR52E4 as such loci for SAH. Therefore, to the best of our knowledge, we have newly identified nine genes that confer susceptibility to early-onset ischemic stroke, ICH, or SAH. Determination of genotypes for the SNPs in these genes may prove informative for assessment of the genetic risk for ischemic stroke, ICH, or SAH in Japanese.

Genome-wide association studies (GWAS) have identified loci associated with ischemic stroke (IS) and cardiovascular disease (CVD) in European-descent individuals, but their replication in different populations has been largely unexplored. Nine single nucleotide polymorphisms (SNPs) selected from GWAS and meta-analyses of stroke, and 86 SNPs previously associated with myocardial infarction and CVD risk factors, including blood lipids (high density lipoprotein [HDL], low density lipoprotein [LDL], and triglycerides), type 2 diabetes, and body mass index (BMI), were investigated for associations with incident IS in European Americans (EA) N=26 276, African-Americans (AA) N=8970, and American Indians (AI) N=3570 from the Population Architecture using Genomics and Epidemiology Study. Ancestry-specific fixed effects meta-analysis with inverse variance weighting was used to combine study-specific log hazard ratios from Cox proportional hazards models. Two of 9 stroke SNPs (rs783396 and rs1804689) were significantly associated with [corrected] IS hazard in AA; none were significant in this large EA cohort. Of 73 CVD risk factor SNPs tested in EA, 2 (HDL and triglycerides SNPs) were associated with IS. In AA, SNPs associated with LDL, HDL, and BMI were significantly associated with IS (3 of 86 SNPs tested). Out of 58 SNPs tested in AI, 1 LDL SNP was significantly associated with IS. Our analyses showing lack of replication in spite of reasonable power for many stroke SNPs and differing results by ancestry highlight the need to follow up on GWAS findings and conduct genetic association studies in diverse populations. We found modest IS associations with BMI and lipids SNPs, though these findings require confirmation.

Large Multi-Ethnic Genome Wide Association Study Reveals Novel Genetic Associations with Childhood Acute Lymphoblastic Leukemia at Chromosomes 8q24 and 17q12

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Genome-wide association studies (GWASs) aim to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single nucleotide polymorphisms (SNPs). Although traditional single-locus statistical tests have identified many genetic determinants of susceptibility, those findings cannot completely explain genetic contributions to complex diseases. Marchini and coauthors demonstrated the importance of testing two-locus associations allowing for interactions through a wide range of simulation studies. However, such a test is computationally demanding as we need to test hundreds of billions of SNP pairs in GWAS. Here, we provide a method to address this computational burden for dichotomous phenotypes. We have applied our method on nine datasets from GWAS, including the aged-related macular degeneration (AMD) dataset, the Parkinson's disease dataset and seven datasets from the Wellcome Trust Case Control Consortium (WTCCC). Our method has discovered many associations that were not identified before. The running time for the AMD dataset, the Parkinson's disease dataset and each of seven WTCCC datasets are 2.5, 82 and 90 h on a standard 3.0 GHz desktop with 4 G memory running Windows XP system. Our experiment results demonstrate that our method is feasible for the full-scale analyses of both single- and two-locus associations allowing for interactions in GWAS. http://bioinformatics.ust.hk/SNPAssociation.zip nelsontang@cuhk.edu.hk; eeyu@ust.hk; Supplementary data are available at Bioinformatics online.

Genome-wide association studies have identified at least 71 Crohn's disease (CD) genetic risk loci, but the role of gene-gene interactions is unclear. The value of genetic variants in clinical practice is not defined because of limited explained heritability. We examined model predictability of combining the 71 CD risk alleles and genetic interactions in an ongoing inflammatory bowel disease genome-wide association study. The Wellcome Trust Case Control Consortium inflammatory bowel disease genome-wide association study was used as a replicate cohort. We used logic regression, an adaptive regression methodology, to search for high-order binary predictors (e.g., single-nucleotide polymorphism [SNP] interactions). The combined 71 CD SNPs had good CD risk predictability (area under the curve of 0.75 and 0.73 in the 2 cohorts). Higher cumulative allele score predicted higher CD risk, but a relatively small difference in cumulative allele scores was observed between CD and controls (49 versus 47, P < 0.001). Through LR, we identified high-order genetic interactions and significantly improved the model predictability (area under the curve, from 0.75 to 0.77, P < 0.0001). A genetic interaction model, including NOD2, ATG16L1, IL10/IL19, C13orf31, and chr21q loci, was discovered and successfully replicated in the independent Wellcome Trust Case Control Consortium cohort. The explained heritability of the 71 CD SNPs alone was 24% and increased to 27% after adding the genetic interactions. A novel approach allowed the identification and replication of genetic interactions among NOD2, ATG16L1, IL10/IL19, C13orf31, and chr21q loci. CD risk can be predicted by a model of 71 CD loci and improved by adding genetic interactions.