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Published in final edited form as: Stroke. 2015 Jan 22;46(3):615–619. doi: 10.1161/STROKEAHA.114.007930

Genetic overlap between diagnostic subtypes of ischemic stroke

Elizabeth G Holliday 1,2, Matthew Traylor 3, Rainer Malik 4, Steve Bevan 3, Guido Falcone 5,6, Jemma C Hopewell 7, Yu-Ching Cheng 8, Ioana Cotlarciuc 9, Joshua C Bis 10, Eric Boerwinkle 11, Giorgio B Boncoraglio 12, Robert Clarke 7, John W Cole 13,14, Myriam Fornage 11, Karen L Furie 15, M Arfan Ikram 16, Jim Jannes 17, Steven J Kittner 13,14, Lisa F Lincz 18,19,20, Jane M Maguire 19,21, James F Meschia 22, Thomas H Mosley 23, Mike A Nalls 24, Christopher Oldmeadow 2, Eugenio A Parati 12, Bruce M Psaty 10,25,26, Peter M Rothwell 27, Sudha Seshadri 28, Rodney J Scott 19,20,29, Pankaj Sharma 30, Cathie Sudlow 31, Kerri L Wiggins 10, Bradford B Worrall 32, Jonathan Rosand 5,6, Braxton D Mitchell 8, Martin Dichgans 4,33, Hugh S Markus 3, Christopher Levi 19,34, John Attia 1,2,*, Naomi R Wray 35,*; on behalf of the Australian Stroke Genetics Collaborative, the Wellcome Trust Case Control Consortium 2 and the International Stroke Genetics Consortium
PMCID: PMC4342266  NIHMSID: NIHMS651571  PMID: 25613305

Abstract

Background and Purpose

Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for shared genetic etiology among the three major subtypes: large artery atherosclerosis (LAA), cardioembolism (CE) and small vessel disease (SVD), to inform potential cross-subtype analyses.

Methods

Analyses used genome-wide summary data for 12,389 ischemic stroke cases (including 2,167 LAA, 2,405 CE and 1,854 SVD) and 62,004 controls from the Metastroke consortium. For 4,561 cases and 7,094 controls, individual-level genotype data were also available. Genetic correlations between subtypes were estimated using linear mixed models (LMM) and polygenic profile scores. Meta-analysis of a combined LAA-SVD phenotype (4,021 cases, 51,976 controls) was performed to identify shared risk alleles.

Results

High genetic correlation was identified between LAA and SVD using LMM (rg=0.96, SE=0.47, P=9×10−4) and profile scores (rg=0.72; 95% CI: 0.52 – 0.93). Between LAA and CE, and SVD and CE, correlation was moderate using LMM but not significantly different from zero for profile scoring. Joint meta-analysis of LAA and SVD identified strong association (P=1×10−7) for SNPs near the opioid receptor μ1 (OPRM1) gene.

Conclusions

Our results suggest that LAA and SVD, which have been hitherto treated as genetically distinct, may share a substantial genetic component. Combined analyses of LAA and SVD may increase power to identify small-effect alleles influencing shared pathophysiological processes.

Keywords: ischemic stroke, genetic epidemiology, atherosclerosis, lacunar stroke

Introduction

Ischemic stroke (IS) is a complex disease influenced by numerous clinical, genetic and lifestyle risk factors. While conventional factors such as hypertension, dyslipidaemia, diabetes and smoking are well-established, genetic factors contribute up to 30-40% of risk 1 and are poorly understood. Despite recent advances in high throughput genotyping, gene discovery for IS has progressed slowly due to the technical nature of case ascertainment and etiological heterogeneity of the IS diagnosis. The latter produces pathophysiological differences and implies genetic differences between patients, complicating efforts to identify susceptibility genes.

To assist diagnosis and clinical management, schemes have been developed to categorise IS into diagnostic subtypes 2, 3. The major types are large artery atherosclerosis (LAA), cardioembolism (CE) and small vessel (lacunar) disease (SVD). By exploiting these phenotypically more homogeneous categorisations, genome-wide association studies (GWAS) have identified a number of genetic associations specific to individual subtypes 4-7. In contrast, only two genome-wide significant associations have been identified for broadly defined IS 8, 9, in spite of its ~5-fold larger sample sizes. Power of association studies is a balance between sample size and the (unknown) effect sizes of risk loci, with estimable effect size depending on genetic homogeneity. To date, GWAS of IS have focussed on individual subtypes which reduces sample size and may reduce power at some loci if there is a shared genetic basis between subtypes 10.

This study aimed to estimate genetic correlation between the three major IS subtypes using individual-level GWAS data and meta-analysis summary statistics from the International Stroke Genetics Consortium (ISGC) 6, 7 and Metastroke 5. Genetic correlations were estimated using two different methods: linear mixed models 11 and polygenic profile scoring 12.

Methods

Data Sources

The Metastroke study included 15 individual studies contributing 12,389 total ischemic stroke cases and 62,004 controls of European ancestry (Data Supplement, Table I). Details of these 15 studies, including genotyping, phenotyping and participants’ demographic details have been previously described in detail 5. Cases and controls did not overlap between studies and were confirmed as unrelated using genotypic data. Stroke subtyping was performed using the TOAST system 2, identifying 2,167 cases with large artery atherosclerosis (LAA), 2,405 with cardioembolism (CE) and 1,854 cases with small vessel disease (SVD); the remainder had other, undetermined, or cryptogenic etiology. Each study conducted genotype imputation using either HapMap Phase 2 or 1000 Genomes reference panels, fitted additive logistic regression models for all SNPs and provided regression summary statistics for IS and its subtypes (if available). Individual-level GWAS data was also available for three of the largest Metastroke cohorts: two from the Wellcome Trust Case Control Consortium 2 Study (WTCCC2-UK and WTCCC2-Munich) 6 and the Australian Stroke Genetics Collaborative (ASGC) 7, which were genotyped using Illumina arrays with similar content. All studies were approved by appropriate ethics committees and participants provided written informed consent.

Linear mixed modelling

Genotype data for the three samples with individual-level data were combined to yield a single dataset using PLINK 13. Stringent quality control removed SNPs not directly genotyped in all samples, with >0.5% missing data, Hardy-Weinberg P-value <0.05, minor allele frequency <1%, or differential missingness (P<0.05) between samples. We excluded samples with >1% data missingness and one from each pair with an absolute value of genome-wide similarity >0.05 14. Principal components of ancestry were calculated in the pooled sample following three iterations of principal components analysis with outlier removal (>5 standard deviations from the mean on PC1-5) 15.

Heritability within and genetic correlations (rg) between subtypes were estimated using linear mixed models 14, adjusting for 20 principal components. Likelihood ratio statistics were used to test whether estimates were significantly different to zero. Heritability estimates were transformed to the liability scale assuming 2% lifetime prevalence for IS, to which the three major subtypes each contribute approximately 20% (total 60%), equating to 0.4% prevalence (20% ×2%) for each subtype 5, 16. The remaining 0.8% prevalence (40% × 2%) was assumed to reflect other stroke types.

Polygenic profile scoring

The profile scoring approach uses SNP association statistics from a given phenotype to build a linear predictor, and tests this for association with the same or a different phenotype in independent data. To facilitate interpretation of cross-subtype analyses, we first assessed association of profile scores within IS and its three subtypes. For each, this was performed using “leave-one-out” validation for the three “target” datasets with individual-level data. In turn, each of the three was set aside and a GWAS “discovery” meta-analysis was performed using all other Metastroke datasets. SNPs with data from at least five Metastroke studies were retained and pruned for linkage disequilibrium (LD: r2>0.2 within 1Mb) using PLINK's --clump algorithm 13, which preferentially retains the most associated SNP in an LD region. From the pruned set we extracted subsets passing ten graded significance thresholds (PT =1×10−4, 1×10−3 , 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1). For subsets passing each threshold, PLINK's --score function was used to calculate profile scores for individuals in the “left-out” dataset. These scores represent an average risk allele burden across all SNPs in the score, with weights assigned as the log odds ratio from the discovery meta-analysis.

Associations of profile scores with stroke subtypes were assessed by logistic regression adjusted for three ancestry principal components. Variance explained by the score was computed as the difference in Nagelkerke's pseudo-R2 between the model including the profile score and principal components, and that including only principal components. Results for the three target datasets were combined via random-effects meta-analysis to estimate overall significance. Overall variance explained was estimated as the sample-size weighted mean of target dataset-specific pseudo-R2 estimates.

To assess polygenic sharing between subtypes we used the same approach, with different subtypes alternately specified as “discovery” and “target” traits. There was no sample overlap between discovery and target analyses. Using profile score results, genetic correlations (rg) were estimated using a quantitative genetics framework 12. At α=0.05, we had 98% power to detect polygenic scores explaining ≥0.2% of variance in case/control status for any target subtype, 81-83% power to detect scores explaining ≥0.1% of variance (varying by subtype) and 52-54% power for scores explaining ≥0.05% of variance 12.

Joint meta-analysis of LAA and SVD

Joint, fixed-effects meta-analysis of allelic effects for LAA and SVD was performed using Metal 17, for 2,167 LAA cases, 1,854 SVD cases (4,021 total cases) and 51,976 controls from 12 studies (Data Supplement, Table I). To control type 1 error due to overlapping controls for LAA and SVD within cohorts, a covariance correction was applied 18. Power to detect associated SNPs was calculated 19 assuming an additive model, perfect LD between risk and marker alleles and a significance level of α=5×10−8. For a genetic risk ratio of 1.2, we had 37%, 89% and 98-99% power to identify risk alleles with frequency 0.1, 0.2 and 0.3-0.5 respectively. For a true risk ratio of 1.1, power was low, ranging from 0.2% to 10% across allele frequencies.

Results

Linear mixed models

After stringent quality control, individual-level genotype data was available for 4,561 IS cases and 7,094 controls (Table 1) at 345,336 directly genotyped SNPs. Using linear mixed models (LMM), the estimated proportion of variance in case-control status explained by the SNPs (h2SNP) was significant for all stroke traits (Data Supplement, Table II). Higher and more significant values were estimated for IS (h2SNP =0.18; P=1×10−14), LAA (h2SNP=0.19; P=2×10−5) and CE (h2SNP=0.24; P=2×10−6), while the estimate for SVD was lower (h2SNP=0.10; P=0.04).

Table 1.

Ischaemic stroke samples used for testing polygenic scores

Study IS LAA CE SVD Controls
ASGC 1,071 375 226 287 1,212
WTCCC2-Munich 1,140 338 322 104 775
WTCCC2-UK 2,350 494 450 471 5,107
Total 4,561 1,207 998 862 7,094
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To estimate the genetic correlation (rg) between subtype cases, controls were randomly allocated to one of the two subtype groups in each analysis. This allocation and estimation process was repeated ten times and the mean and standard deviation of parameter estimates, and mean standard errors (SE) derived (Data Supplement, Table III). The rg value was highest and significantly different from zero between LAA and SVD at 0.96 (SD=0.059; P=9×10−4), although the large standard error (0.47) indicates low precision. Reduced, but nominally significant correlation was observed between LAA and CE (rg=0.39, SE=0.21, P=0.024) and between CE and SVD (rg=0.64, SE=0.40, P=0.017).

Polygenic profile scoring

While LMM analyses were restricted to samples with individual-level data, profile scoring could utilise Metastroke samples with summary statistics for “discovery” meta-analyses. In analyses within traits, profile scores for IS, LAA and CE showed strong association with the same trait in independent target cohorts (Data Supplement, Tables IV-VI, Figure I). For IS, strong association was observed across most of the discovery P-value distribution with maximum association observed for PT<1 (Pscore =1.1×10−8), typical of relatively small discovery samples 20. There was little effect size heterogeneity across target cohorts. For LAA, maximum association (Pscore =1.7×10−8) was observed for PT<0.05 with no heterogeneity. For CE, maximum association (Pscore=2×10−4) was observed for predictors including SNPs reaching PT<0.001 and PT<0.01, with no heterogeneity. For all three subtypes, profile scores explained a small proportion of observed case-control variance, being highest for LAA (pseudo-R2=0.45%) and lowest for SVD (pseudo-R2=0.05%). We also note that SVD-based scores did not associate with SVD in target cohorts and many showed effect heterogeneity between studies (Data Supplement, Table VII).

Analyses between stroke subtypes detected significant polygenic sharing between LAA and SVD (Data Supplement Tables VIII, IX, Figure II). The majority of SVD-based scores associated with LAA, with no heterogeneity. The highest association was observed for a score including ~36,000 SNPs reaching discovery PT<0.1 (Pscore =2×10−4) which explained an estimated 0.19% of observed LAA case/control variance. In the reverse analysis, three LAA-based profile scores associated with SVD at P<0.05, e.g., the score including ~20,000 SNPs reaching PT <0.05 (Pscore=0.032, R2 =0.08%). In analyses of the other two subtype pairs (LAA and CE, CE and SVD), no co-association of profile scores was observed (Data Supplement Tables X-XIII, Figures III-IV).

Using profile score results within LAA, the estimated proportion of LAA variance in liability explained by the score most strongly associated with LAA and SVD (PT=0.05) was 12.8% (Data Supplement, Table V). SVD-based scores did not associate with SVD in target samples, but the score most significantly associated with LAA (PT=0.1) explained 0.8% of SVD liability variance (Data Supplement, Table VII). Using these estimates and the observed cross-trait association results, the estimated genetic correlation 12 between LAA and SVD was rg=0.72, which was significantly different from zero (95% CI: 0.52, 0.92). The SNP-based correlation was not significantly different from zero for LAA and CE (rg=0.13, 95% CI: 0, 0.56), or CE and SVD (rg=0.64, 95% CI: 0, 0.92).

Quantitative bias analysis – subtype misclassification

Bias analysis was performed to assess the extent to which the genetic correlation (rg) between LAA and SVD could result from subtype misclassification 21 (see Data Supplement, Methods and Table XIV). Allowing for rates of subtype misclassification consistent with reported values of inter-rater reliability 22, rg was still significantly different from zero. Assuming all misclassified LAA cases were truly SVD and vice versa, the estimate was rg=0.63 (95% CI: 0.34, 0.74). Assuming all misclassified cases were neither LAA nor SVD the estimate was rg=0.75 (95% CI: 0.43, 0.98). This suggests robustness of the observed genetic correlation to likely levels of subtype misclassification.

GWAS meta-analysis of LAA and SVD

Given evidence for shared common variants between LAA and SVD, joint meta-analysis of LAA and SVD was performed (Data Supplement, Figures V, VI). While no SNPs reached genome-wide significance (P<5×10−8), suggestive association (P=1×10−7, at rs17084671; P=2×10−7 at rs6938958 and rs7763080) was observed for a cluster of SNPs at chromosome 6q25.2 (Data Supplement, Table XV), ~100kb upstream of the opioid receptor μ1 (OPRM1) gene.

Discussion

Genome-wide association studies of ischemic stroke (IS) have revealed the importance of diagnostic subtype classifications. However, exclusive reliance on discrete subtypes reduces sample size and assumes an absence of risk alleles influencing multiple subtypes. Using some of the largest extant GWAS collections and two different analytic approaches, this study suggests the presence of extensive genetic overlap between large artery atherosclerotic and small vessel ischemic stroke. We estimated the genetic correlation between these subtypes exceeds 0.7, but larger samples will increase the accuracy of this estimate.

We were careful to eliminate potential sources of bias in our analyses. In individual-level data, European ancestry was strictly defined and principal components of ancestry included as covariates. We also checked that positive score effects were present in multiple target studies and not driven by a single study. Misclassification of TOAST subtypes was considered an important potential source of error since MRI, which increases diagnostic accuracy particularly for SVD, was only used for subtyping approximately 50% of all cases 5. However, sensitivity analyses suggested that typical rates of misclassification would have minimal effects on the estimated correlation.

Although SVD-based profile scores did not show significant association with SVD, they associated with LAA in the cross-trait analysis. In the complementary analysis, which used the more powerful LAA discovery sample, LAA-based profile scores showed significant association with both LAA and SVD. This does not imply lack of consistency or polygenic architecture for SVD. Indeed, our and a previous LMM analysis 1 detected significant SNP-based heritability for SVD. Profile score analyses are influenced both by sample size and phenotypic homogeneity of discovery and target traits. SVD had the lowest case numbers and is also phenotypically heterogeneous. In profile analyses conducted exclusively within SVD, these factors will reduce both the accuracy of polygenic predictors, and statistical power in target samples.

The proportion of observed LAA and SVD variance explained by SVD-based and LAA-based profile scores (pseudo-R2) was 0.2% and 0.08%, respectively. Although these values are low, this does not mean the true genetic overlap is small. By combining sampling errors in effect estimates across all SNPs in the score, profile scoring produces estimates of explained variance typically lower than true values 20, 23, but which will increase as sample size increases. Pseudo-R2 measures for binary traits can be difficult to interpret because they can depend on ascertainment, i.e., the proportion of cases in the sample 24. Profile scoring results were used to estimate liability-scale variance explained and genetic correlations using theory that accounts for sample size and ascertainment. For example, within LAA, the pseudo-R2 for the maximum profile score was 0.48%, but the estimated LAA variance explained by the score, adjusted for sample size and ascertainment, was 12.8%. Thus, while estimates of observed cross-trait variance explained are small, they can signify a higher genetic correlation. When genetic correlation has been estimated from the same dataset the results from the profile score and LMM agree well 25. Here, the use of the profile score method allowed the use of a larger sample via datasets for which only association summary statistics were available.

The SNPs most strongly associated with the joint LAA-SVD trait were near the OPRM1 gene, alleles within which have previously shown suggestive association with coronary heart disease (CHD: P=5×10−6), 26 which has an atherosclerotic etiology. Estimated genetic correlation between LAA and SVD is also consistent with an atherosclerotic etiology in the majority of LAA and a subset of SVD cases. The primary pathophysiological mechanism for LAA is presumed to be atherosclerosis of the large cerebral arteries 2. For SVD, pathological and imaging studies suggest the presence of significant disease heterogeneity, with two major underlying vascular pathologies being hypothesised 27, 28. The first involves localised atherosclerosis of the larger perforating arteries, typically resulting in a larger, isolated lacunar infarct. The second involves diffuse, nonatherosclerotic arteriopathy of the smaller perforating arteries, associated with multiple, smaller infarcts and often co-existent radiological leukoaraiosis 28. Earlier risk factor analyses suggested conventional “atherosclerotic” factors were more common in the isolated lacunar infarct subtype. This subgroup could thus account for the genetic overlap between the broader SVD category and LAA.

Summary

Our analyses strongly suggest that LAA and SVD – which have been hitherto considered genetically distinct – may have a shared genetic etiology. Further investigation of the genetic relationship between ischemic stroke subtypes is merited. Although recent GWAS have identified several subtype-specific genetic associations, the pace of discovery has been constrained by small numbers for individual subtypes. If there exist small-effect variants influencing multiple subtypes, joint subtype analyses will offer higher power to identify these and may also identify biological mechanisms shared by these traditionally distinct clinical diagnoses.

Supplementary Material

1

Acknowledgments

Sources of Funding

EGH was supported by the Australian Heart Foundation and National Stroke Foundation (100071). NRW was supported by the Australian National Health and Medical Research Council (NHMRC: 613602). H.M. is supported by a National Institute for Health Research (NIHR) Senior Investigator award. H.M. and S.B. are supported by the Cambridge University Hospital National Institute for Health Research Biomedical Research Centre (NIHR BRC). J.R. and B.B.W. received Research Funding from the NIH. Acknowledgements of funding for the individual Metastroke studies are detailed in the original publication by Traylor et al 5.

Footnotes

Disclosures

J.R. has a consulting relationship with Boehringer Ingelheim. B.B.W. is an Associate Editor of the American Academy of Neurology (AAN) journal ‘Neurology’.

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Supplementary Materials

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