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GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes

Nature Communicationsvolume 9, Article number: 5141 (2018) | Download Citation

Abstract

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Introduction

Atherosclerosis is characterized by an accumulation of lipid-rich and inflammatory deposits (plaques) in the sub-intimal space of medium and large arteries. Plaque enlargement leads to blood flow limitation, organ ischemia, and/or tissue necrosis. Plaque rupture can lead to abrupt vascular occlusion, which underlies clinical cardiovascular events, including myocardial infarction and ischemic stroke. Coronary heart disease (CHD) accounts for one in seven deaths, and stroke accounts for one in 20 deaths in the US1. Because atherosclerosis has a long pre-clinical phase, early detection of atherosclerosis using non-invasive methods may help identify individuals at risk for atherosclerotic clinical events2, and provides an opportunity for prevention. Subclinical atherosclerosis can be detected by B-mode ultrasound measurement of common carotid artery intima-media thickness (cIMT) or carotid plaques1.

Subclinical and clinical atherosclerosis has known genetic components3. Genome-wide association studies (GWAS) of subclinical atherosclerosis have previously identified three loci significantly associated with cIMT at ZHX2, APOC1, and PINX1, and two loci associated with common carotid artery plaque at PIK3CG and EDNRA4. An exome-wide-association study identified significant associations of the APOE ε2 allele with cIMT and coronary artery calcification5. The APOE single nucleotide polymorphism (SNP) rs7412 is in linkage disequilibrium (LD) with the APOC1 variant, thus representing the same signal. Additional GWAS-identified associations were reported for carotid plaque at the 9p21 and SFXN2 loci6, and for cIMT at the CFDP1-TMEM170A locus7. However, these prior studies were of limited sample size and genomic coverage, and failed to investigate the etiological role that subclinical atherosclerosis may have on atherosclerotic clinical events.

Herein, we perform a large meta-analysis of GWAS of subclinical atherosclerosis by analyzing 1000 Genomes imputed genotype data obtained from collaborations between the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium8 and the University College London-Edinburgh-Bristol (UCLEB) consortium9. One of the greatest challenges in the translation of GWAS findings to biological understanding is related to the limited access to RNA expression data from disease-relevant tissues. Consequently, we sought to reliably identify the tissue-specific gene regulatory functions responsible for the GWAS signals by prioritizing candidate genes for established and novel loci of cIMT and carotid plaque using statistical methods for colocalization10. These methods integrate identified loci with expression quantitative loci (eQTLs) inferred from cardiovascular disease-relevant genetics of RNA expression, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study, where arterial wall and metabolic-related RNA samples were collected from up to 600 patients with CHD11. We also evaluate the relationships of cIMT and carotid plaque with clinically apparent CHD and stroke using summary data from two large consortia. In summary, our study sequentially assesses the genetic epidemiology and tissue-specific patterns of gene regulation involved in the formation of subclinical atherosclerosis traits across cardiovascular disease-related tissues.

Results

Study description

The study design is shown in Fig. 1. We undertook meta-analysis of GWAS in individuals of European ancestry for cIMT (up to 71,128 participants from 31 studies) and carotid plaque (up to 48,434 participants from 17 studies; 21,540 with defined carotid plaque) (Supplementary Table 1). cIMT and plaque were evaluated using high-resolution B-mode ultrasonography and reading protocols as previously reported4. Carotid plaque was defined by atherosclerotic thickening of the common carotid artery wall or the proxy measure of luminal stenosis greater than 25% (Supplementary Table 2). Each cohort performed association analyses using standardized protocols (Methods) for variants imputed based on the 1000 Genomes Project (1000G) phase 1 v3 reference. Extensive quality control (QC) was applied to data, and there was little evidence for population stratification in any of the studies for either trait (Supplementary Table 3). The study-specific results were combined using fixed-effect meta-analyses, given the low heterogeneity across studies (0% heterogeneity)12.

Fig. 1
Fig. 1

Overall study design. a GWAS meta-analyses of cIMT and carotid plaque for gene discovery. b Local and genome-wide shared genetic basis using gene expression and clinical outcomes GWAS data

GWAS meta-analyses of cIMT and carotid plaque

For cIMT, 11 loci had at least one SNP association that reached the genome-wide association threshold (p < 5 × 10−8), of which eight were newly described and three have been previously reported (Table 1). The closest genes for the eight loci were: 1q32.2 intergenic (rs201648240), ATP6AP1L (rs224904), AIG1 (rs6907215), PIK3CG (rs13225723), MCPH1 (rs2912063), SGK223 (rs11785239), VTI1 (rs1196033), and CBFA2T3 (rs844396). For three loci previously reported, the closest genes were ZHX2 (rs148147734), PINX1 (rs200482500), and APOE (rs7412).

Table 1 Loci significantly associated with cIMT and plaque GWAS

The PIK3CG is a newly described locus for cIMT, but has been previously reported in a GWAS of carotid plaque4. The two signals on chromosome 8 near MCPH1 (rs2912063) and SGK223 (rs11785239) were confirmed to be independent through conditional analysis (Supplementary Table 4). At the PINX1 locus, the lowest association p-value variant (rs200482500) was not in LD with the previously reported associated variant in the region (rs6601530, r2 = 0.0, Table 1), thus representing an independent signal at this locus. Two additional loci for cIMT had an SNP that reached suggestive evidence for association (p < 1.0 × 10−7) including an SNP nearby APOB (rs515135) and an intronic low frequency variant at ATG4B (rs139302128, minor allele frequency [MAF] = 0.03) (Supplementary Table 5).

The GWAS meta-analysis for carotid plaque identified five loci, of which one has not been previously described (nearby gene LDLR) (Table 1). At four known loci associated with carotid plaque (nearby genes EDNRA, PIK3CG, CFDP1-TMEM170A, and at the 9p21 region), the most significantly associated variants were in LD with the previously reported SNPs (Table 1)4,6,7, indicating that these SNPs mark the same association at each locus. Two suggestive loci (p < 10−7) were also identified nearby the genes TMCO5B and STEAP2-AS1 (Supplementary Table 5). Conditional analyses confirmed the presence of a single independent signal at each locus. Manhattan and QQ plots from the meta-analysis of cIMT and carotid plaque are shown in Supplementary Figure 1 and regional plots in Supplementary Figure 2. Forest Plots for all loci are shown in Supplementary Figure 3.

Regulatory annotations of GWAS SNPs for cIMT/carotid plaque

To better define potentially causal variants within the identified genetic risk loci, we jointly analyzed the GWAS data with functional genomic information such as annotations on active transcription sites or open chromatin regions (i.e., performed a fine-mapping functional genome-wide association analysis using fGWAS13). Only variants in the PINX1 region were found to have a high probability that its association with cIMT is driven by SNPs that fall within transcription sites in adipose-derived mesenchymal stem cells at a DNaseI-hypersensitive site (Supplementary Figure 4), a finding that provides a down-stream mechanistic explanation for the cIMT signal in the PINX1 locus.

To further explore the regulatory functions of variants in the identified loci for cIMT and carotid plaque, we investigated whether the identified lead SNPs were also eQTLs using vascular RNAseq data from GTEx (aorta, coronary and tibial arteries, heart atrial appendage, and heart left ventricle) and from the coronary artery disease cohort of STARNET (i.e., from the atherosclerotic-lesion-free internal mammary artery [MAM] and atherosclerotic aortic root [AOR]). Lead SNP associated with cIMT and carotid plaque (rs13225723) in the PIK3CG locus was found to be vascular-specific eQTLs for CCDC71L and PRKAR2B in GTEx aorta as well as in STARNET AOR and MAM tissues (Table 2, Fig. 2), suggesting that the genetic regulation of these two genes are responsible for risk variation in cIMT and carotid plaque development in this locus.

Table 2 Gene expression results for significant SNPs in GTEx and STARNET tissues
Fig. 2
Fig. 2

Pairwise colocalization results for genes identified for cIMT and carotid plaque GWAS meta-analysis with STARNET expression datasets. Red indicates a high posterior probability of colocalization and blue a high probability of no colocalization of the same SNP with tissue eQTLs

Colocalization analysis of GWAS data and STARNET eQTLs

To identify further candidate genes in tissues affected by atherosclerosis that had strong evidence of sharing the same variant for cIMT and carotid plaque as found in our GWAS, we conducted pairwise colocalization analysis of these genetic variants with cis-eQTLs in the STARNET study10.

The pairwise colocalization analysis is based on coloc, a Bayesian statistical methodology that tests pairwise colocalization of SNPs in GWAS with eQTLs and, in this fashion, generates posterior probabilities for each locus weighting the evidence for competing hypothesis of either no colocalization or sharing of a distinct SNP at each locus10. We used summary statistics from all SNPs within a 200-kb window around each gene covered by the eQTL datasets (N = 18,705, see Methods), and analyzed each eQTL-GWAS dataset pair (Supplementary Table 6). A posterior probability of ≥75% was considered strong evidence of the tissue-specific eQTL-GWAS pair influencing both the expression and GWAS trait at a particular region. Results for this analysis are shown in Table 3 and Supplementary Figure 5. The strongest evidence for an effect on gene expression within the regions identified in our standard GWAS meta-analysis was for the CCDC71L and PRKAR2B genes at the previously described chromosome 7 cIMT locus (PIK3CG in Table 2, Fig. 2). These genes showed evidence of colocalization for both cIMT and carotid plaque in AOR and MAM tissues (Table 3, Fig. 3). CCDC71L had the highest probability (>95%) for colocalization for cIMT, and MAM and AOR tissue eQTLs, and for carotid plaque, and MAM and AOR tissue eQTLs. We found a low probability of colocalization of the SNP with the PIK3CG gene expression (<1%).

Table 3 Colocalization of cIMT and plaque with eQTLs in tissues from patients with CHD in STARNET tissues for genes/tissues combinations that have more than 75% probability to share the same associated variant
Fig. 3
Fig. 3

Association results at the CCDC71L locus (chromosome 7), showing a high posterior probability of a shared variant for cIMT and carotid plaque in AOR and MAM eQTLs. −log10(p) SNP association p-values for cIMT (plot A) and carotid plaque (plot B), and eQTL in AOR (plot C) and eQTL in SF (plot D). Association results in SF tissue have a low probability of a shared signal with cIMT and carotid plaque, possibly indicating a different mechanism in this tissue. eQTLs in MAM are identical to AOR and not shown. The p-values were calculated by fitting a linear regression model with cIMT or plaque as dependent variable and imputed SNPs as independent variables. Each dot is an SNP and the color indicates linkage disequilibrium (r2) with the best hit (in purple)

The eQTL associations at two additional loci (ADAMTS9, LOXL4) in MAM or AOR showed evidence of colocalization with cIMT or carotid plaque, although GWAS association p-values at these loci did not meet the genome-wide significance threshold (Table 3, Supplementary Figure 5). Albeit with weaker magnitudes, the expression of these two genes were also associated with the top colocalizing SNPs as detected in RNAseq data in GTEx aorta (rs17676309, chr3:64730121, ADAMTS9, p = 0.0003 and rs55917128, chr10:100023359, LOXL4, p = 0.0005).

Colocalization of CHD and stroke GWAS and STARNET eQTLs

We next assessed if the four genes (CCDC71L, PRKAR2B, ADAMTS9, LOXL4) identified through colocalization of cIMT/carotid plaque with tissue-specific eQTLs also showed evidence for colocalization with CHD and stroke traits (Supplementary Data 1 and Supplementary Figure 6). We used GWAS summary data for CHD (CARDIoGRAMPlusC4D), and stroke subtypes (MEGASTROKE) and AOR and MAM STARNET tissue eQTLs for these analyses. CCDC71L and PRKAR2B had suggestive evidence of sharing the same variant with large vessel disease stroke in both AOR and MAM tissues (probability of colocalization ≥20%, Supplementary Data 1). In contrast, there was strong evidence (≥75%) to reject a shared variant for CHD and eQTLs at this locus, thus suggesting there is atherosclerotic outcome specificity at vascular level for this locus (Supplementary Figure 5). Three of these genes, CCDC71L, PRKAR2B, and ADAMTS9, showed evidence for shared genetic influences of cIMT or carotid plaque on CHD/stroke outcomes when testing the joint association using moloc, a multiple-trait extension of coloc14 (Supplementary Table 7). We also highlight the expression of KIAA1462 gene in MAM, carotid plaque/cIMT, and CHD, which were positively correlated (Supplementary Figure 7). This gene has suggestive evidence of pairwise colocalization with carotid plaque (67% of probability of shared variant between carotid plaque and eQTL in MAM), as well as a high probability of shared variant between MAM eQTL expression of this gene, GWAS carotid plaque or cIMT, and CHD traits (Supplementary Table 7). We note, however, that the GWAS signal for outcomes across the datasets did not reach genome-wide significance and larger sample sizes may be needed to strengthen the evidence for involvement in disease outcomes.

Genetic correlations of cIMT/carotid plaque and clinical outcomes

To provide etiological insights into the role of measures of subclinical atherosclerosis and major atherosclerotic disease outcomes such as CHD and ischemic stroke, we quantified the genetic correlation using cross-trait LD score regression, a method that estimates genetic correlation across different traits using summary level data15. We used summary statistics between cIMT/carotid plaque with CHD and stroke meta-analysis of GWAS. Both cIMT and carotid plaque had positive significant genetic correlations with CHD (all p < 0.05 after adjusting for multiple testing), though the magnitude of the correlation was twice as strong for carotid plaque (0.52) as for cIMT (0.20) (Table 4). There was also evidence for genetic correlations between cIMT with any stroke and ischemic stroke subtype.

Table 4 Genetic correlation between CHD and stroke traits with cIMT and plaque, and cIMT with plaque using LD score and meta-GWAS

Pathway analysis and druggability

Gene Ontology (GO) analyses of genes identified in the loci for cIMT and carotid plaque according to our meta-analysis of GWAS (Table 1 and Supplementary Table 5) and in the colocalization analyses (Table 3, Supplementary Table 7) showed that cIMT genes are enriched in lipoprotein-related terms and cholesterol efflux, whereas carotid plaque genes are enriched in terms associated with fibroblast apoptosis (Supplementary Figure 8). Analysis of the cIMT genes using a GO Slim additionally identified several of the genes that were associated with terms describing cardiovascular development, cell adhesion, and immune processes, processes already considered relevant to atherosclerosis. Specifically, there is corroborating evidence from GO that CCDC71L, PRKAR2B, and TWIST1 are associated with cIMT/carotid plaque as they are involved in lipid metabolism, with similar support that ADAMTS9, CDH13, and KIAA1462 are associated with cIMT or carotid plaque risk as they are all involved in cell adhesion and, together with TWIST1, in cardiovascular system development (Supplementary Data 2).

From the loci associated with cIMT and carotid plaque, we identified seven genes (ATG4B, ALPL, LDLR, APOB, EDNRA, APOE, and ADAMTS9) whose encoded proteins are targets at various stages of the drug development process (Supplementary Tables 8 and 9). ADAMTS9 gene encodes a protein likely to be druggable16. ATG4B, ALPL, and LDLR are proteins being targeted by compounds in pre-clinical phase (tier 2), while APOB and EDNRA are proteins targeted by drugs in clinical phase or licensed (tier 1). APOB is the target of an approved FDA drug for treatment of familial hypercholesterolemia. EDNRA gene encodes for endothelin A receptor, against which several antagonists have been developed for the treatment of pulmonary arterial hypertension or which are in advanced clinical phase development for non-small cell lung cancer and diabetic nephropathy.

Discussion

We provide results of a large meta-analysis of GWAS of subclinical atherosclerosis and we integrate our results with tissue-specific gene expression data using eQTLs from both the early (MAM) and late advanced (AOR) atherosclerotic arterial wall from the STARNET study to enable reliable discovery of genes with biological evidence of an increased probability for conferring inherited risk of atherosclerosis development. Our discovery approach using GWAS meta-analyses identified 16 loci significantly associated with either cIMT or carotid plaque, of which nine are novel.

The integration of GWAS and tissue-specific cis-eQTLs for the joint analyses of tissue-specific eQTLs from CHD patients identified two potentially additional loci colocalizing with cIMT or carotid plaque: chr3:63561280-65833136 (ADAMTS9), chr10:99017729-101017321 (LOXL4). ADAMTS9 is a metalloproteinase involved in thrombosis and angiogenesis and has been associated with cardiometabolic traits (waist-to-hip ratio, waist circumference, and type 2 diabetes) in GWAS, and with coronary artery calcification in a gene-by-smoking interaction GWAS17,18. LOXL4 encodes a lysyl oxidase involved in crosslinks of collagen and elastin in the extracellular matrix. This family of proteins are involved in the development of elastic vessels and mechanical strength of the vessel wall, and their inhibition was associated with the development of abdominal aortic aneurysms and more severe atherosclerosis in experimental models19.

Some loci identified in our meta-analysis of GWAS include genes in known pathways for atherosclerosis, including LDLR, which is related to lipid pathways and CHD, and identified for associations with carotid plaque in our study. For most of the loci, however, the underlying gene implicated in signals are unknown. Our colocalization approach found both CCDC71L and PRKAR2B as the most likely genes at the chromosome 7 locus, where PIK3CG was previously the suggested gene. This finding is in agreement with a targeted sequencing study of subclinical atherosclerosis15. An additional SNP (rs342286) at this locus has been associated with platelets volume and reactivity, and cardiovascular traits. However, rs342286 is not in LD with our most significant SNP and it is not associated with cIMT or carotid plaque in our studies (p = 0.49 and 0.01, respectively). Of interest, the variant we identified in this study showed evidence for colocalization with cIMT/carotid plaque and large vessel disease stroke but not CHD, therefore showing tissue and outcome-specificity. CCDC71L has unknown function. PRKAR2B codes for one of the several regulatory subunits of cAMP-dependent protein kinase and its expression is ubiquitous. In vitro studies have shown that adenosine-induced apoptosis of arterial smooth muscle cells involves a cAMP-dependent pathway20.

Measures of cIMT and carotid plaque reflect vascular pathophysiologic and atherosclerosis processes, respectively, with carotid plaque more strongly reflecting atherosclerotic clinical events. An important contribution of this study is the supporting evidence for overall genetic correlations of CHD and stroke (any cause and ischemic stroke) with subclinical atherosclerosis traits, estimated using LD score methods. Further highlighting the potential biological relevance of our findings, the genetic correlations estimates for CHD were stronger for carotid plaque than for cIMT. However, cIMT and carotid plaque GWAS were correlated, and the genetic correlations estimates with stroke were similar for cIMT or carotid plaque, and not significant for carotid plaque. The colocalization analyses provided additional insights in the relationships between subclinical atherosclerosis, clinical outcomes, and tissue-specific regulation at specific genomic regions. For example, our suggestive top gene association in multi-trait colocalization for KIAA1462 included MAM eQTLs, carotid plaque, and CHD, supporting the shared genetic effects at this locus of atherosclerosis in carotid and coronary arteries. KIAA1462 has been previously reported in the same locus identified by GWAS for CHD21. This gene encodes a protein involved in cell–cell junctions in endothelial cells22, which was recently shown to be involved in pathologic angiogenic process in in vitro and in vivo experimental models23. These findings suggest that there may be important differences in vascular bed regulation at distinctive regions for atherosclerotic cardiovascular and stroke outcomes that may help to identify genes and specific targets for CHD or stroke prevention and treatment.

Additional studies in diverse and large samples across the multiple datasets are needed to explore these results further. As more summary statistics become available for other clinical end-points beyond stroke and CHD (both in terms of larger sample size and richer genome coverage), and as further refinements in clinical phenotypes emerge (e.g. from CHD to acute coronary syndrome sub-components), strategies to integrate this knowledge using methods such as moloc10 and eCAVIAR24 will continue to be essential for harnessing genome-wide findings in the drug-discovery process.

In summary, our study is a large GWAS meta-analysis of cIMT and carotid plaque. Through a sequential approach of discovery and colocalization studies, we provide deeper insights into disease causal genes of subclinical cIMT and carotid plaque formation. We confirmed three loci and identified nine novel loci in the meta-analyses of cIMT and carotid plaque. Additionally, we provide strong evidence for the role of three novel genes from our integrative analysis of GWAS and eQTL data. Moreover, the identified correlations with CHD and stroke highlight novel biological pathways that merit further assessments as novel targets for drug development.

Methods

Ethics statement

All human research was approved by the relevant institutional review boards for each study, and conducted according to the Declaration of Helsinki. All participants provided written informed consent.

Populations and phenotypes

The discovery GWAS in this study consists of a collaboration between the CHARGE 8 and the UCLEB consortia9, for genetic studies of cIMT and carotid plaque among individuals of European ancestry (Supplementary Note 1). All studies followed standardized protocols for phenotype ascertainment and statistical analyses. The descriptive characteristics of participating studies are shown in Supplementary Table 1.

cIMT and carotid plaque measures were evaluated using high-resolution B-mode ultrasonography and reading protocols as previously reported4. We used data from the baseline examination or the first examination in which carotid ultrasonography was obtained. cIMT was defined by the mean of the maximum of several common carotid artery measurements, measured at the far wall or the near wall. For most studies, this was an average of multiple measurements from both the left and right arteries. We also examined a carotid plaque phenotype, defined by atherosclerotic thickening of the carotid artery wall or the proxy measure of luminal stenosis greater than 25% (Supplementary Table 2).

Genotyping, imputation, and study-level quality control

Genotyping arrays and QC pre-imputation are shown in Supplementary Table 3. Each GWAS study conducted genome-wide imputation using a Phase 1 integrated (March 2012 release) reference panel from the 1000G Consortium using IMPUTE225 or MaCH/minimac26, and used Human Reference Genome Build 37. Sample QC was performed with exclusions based on call rates, extreme heterozygosity, sex discordance, cryptic relatedness, and outlying ethnicity. SNP QC excluded variants based on call rates across samples and extreme deviation from Hardy–Weinberg equilibrium (Supplementary Table 3). Non-autosomal SNPs were excluded from imputation and association analysis.

Pre-meta-analysis GWAS study-level QC was performed using EasyQC software27. This QC excluded markers absent in the 1000G reference panel; non A/C/G/T/D/I markers; duplicate markers with low call rate; monomorphic SNPs and those with missing values in alleles, allele frequency, and beta estimates; SNPs with large effect estimates or standard error (SE) ≥10; and SNPs with allele frequency difference >0.3 compared to 1000G reference panel. There was a total of 9,574,088 SNPs for the cIMT meta-analysis and 8,578,107 SNPs for the carotid plaque meta-analysis.

Statistical analyses

Within each study, we used linear and logistic regression to model cIMT and carotid plaque, respectively, and an additive genetic model (SNP dosage) adjusted for age, sex, and up to 10 principal components. We combined summary estimates from each study and each trait using an inverse variance weighted meta-analysis. Additional filters were applied during meta-analyses including imputation quality (MACH r2 < 0.3 and IMPUTE info <0.4), a minor allele frequency (MAF) <0.01, and SNPs that were not present in at least four studies. The genome-wide significance threshold was considered at p < 5.0 × 10−8.

To assess the evidence for independent associations at each locus attaining genome-wide significance, we performed conditional analysis in a 1-Mb genomic interval flanking the lead SNP using GCTA28. This approach uses summary meta-analysis statistics and a LD matrix from an ancestry-matched sample to perform approximate conditional SNP association analysis. The estimated LD matrix was based on 9713 unrelated individuals of European ancestry from the ARIC study, which was genotyped using an Affymetrix 6.0 array and imputed to the 1000G panel using IMPUTE225.

Gene expression analysis using GTEx

GTEx Analysis V6 (dbGaP Accession phs000424.v6.p1) eQTL results were downloaded from GTEx portal for 44 tissues, and then mapped to SNPs listed in Table 1. We used a false discovery rate (FDR) of ≤0.05.

Colocalization analyses using eQTLs

We integrated our GWAS results with cis-eQTL data using a Bayesian method (coloc)10. This method evaluates whether the GWAS and eQTL associations best fit a model in which the associations are due to a single shared variant (summarized by the posterior probability). We used gene expression datasets from multiple tissues from patients with CHD of the STARNET study, including blood, MAM, AOR, subcutaneous fat (SF), visceral fat (VAF), skeletal muscle (SKLM), and liver (LIV) obtained from 600 patients during open heart surgery11. Pairwise colocalization was tested between these expression disease tissue datasets and GWAS results from our cIMT/carotid plaque GWAS meta-analysis. We used GWAS and eQTL summary statistics of SNPs within a 200-kb window around each gene covered by the eQTL datasets. A posterior probability of colocalization ≥0.75 was considered a strong evidence for a causal gene. Next, we reported the gene(s) in the STARNET datasets that had the strongest evidence of sharing the same variant with cIMT or carotid plaque genome-wide. In an alternative analysis, we also tested loci with an SNP that reached a threshold of significant or suggestive genome-wide significance for cIMT or carotid plaque (reported in Table 1, Supplementary Table 5). For each region 200kb around the SNP with the lowest association p-value, we report the gene with the highest probability of being responsible for the GWAS signal (Supplementary Table 6).

Pairwise colocalization for these genes was also tested for publicly available GWAS for CHD case-controls (CARDIoGRAMPlusC4D) and stroke case-controls (MEGASTROKE consortium). The MEGASTROKE dataset uses genotypes imputed to the 1000G phase I haplotype panel. The European ancestry sample used to generate these results consisted of 40,585 stroke cases and 406,111 controls from 15 cohorts and two consortia: the METASTROKE and CHARGE consortia29. The phenotypes used in this analysis were any stroke (n = 39,067 cases, total n = 442,142), ischemic stroke (IS, n = 32,686 cases, total n = 423,266), and etiologic stroke subtypes:cardioembolic stroke (CE, n = 6,820 cases, total n = 314,368), large vessel disease (n = 4,113, total n = 202,263), and small vessel disease (SVD, n = 4,975, total n = 242,250). To explore multi-trait colocalizations, we used moloc14 with prior probabilities of 10−4 for GWAS/GWAS/eQTL, 10−6 for GWAS+eQTL/GWAS or GWAS+GWAS/eQTL, and 10−7 for colocalization of all three association signals.

Functional annotation and epigenetic enrichment analyses

From the Epigenome Roadmap Project30,31, we obtained regulatory information using broad classes of chromatin states (n = 127 tissues) capturing promoter-associated, transcription-associated, active intergenic, and large-scale repressed and repeat-associated states. From ENCODE32, we obtained chromatin states, uniformly processed transcription factor (TF) Chip assays and DNaseI Hypersensitivity sites (DHS) for nine cells lines. From FANTOM533, we used information from expression of enhancers in each tissue (n = 112), and enhancers that are positively differentially expressed against any other tissue (n = 110).

We used fGWAS13 to identify genomic annotations that are enriched within the cIMT results and to select the variants with support for a functional role based on the most informative annotations. We only considered cIMT for these analyses because of the small number of identified loci for carotid plaque. We first estimated the enrichment parameters for each annotation individually and identified the set of annotations with significant marginal associations. We then applied 10-fold cross-validation likelihood and forward selection to identify the set of annotations that significantly improve the model fit, and reverse selection of each annotation included in the model, as suggested in the fGWAS workflow. We reported the model with the highest cross-validation likelihood and SNPs that have regional posterior probability of association (PPA) >0.9 and directly overlap the genomic annotations considered.

Overall genetic correlation analysis

Genetic correlation between cIMT/carotid plaque, CHD, and stroke traits were calculated using LD score regression approach LD-score, which uses GWAS summary statistics and is not affected by sample overlap. This method relies on the fact that the χ2 association statistic for a given SNP includes the effects of all SNPs that are in LD with it and it calculates genetic correlation by partitioning the SNP heritabilities15. Genetic correlations between stroke traits (IS, CE, large vessel disease, and SVD) and cIMT and carotid plaque were calculated using software available at http://github.com/bulik/ldsc with GWAS summary statistics for our cIMT/carotid plaque GWAS, CARDIOGRAMPlusC4D data, and stroke GWAS. We used the LD-scores15, which are based on the 1000 Genomes European population and estimated within 1-cM windows. Based on ten tests performed (two subclinical traits and five outcomes), we set the significance threshold to p = 0.005.

PATHWAY ANALYSES. Methods for GO Slim: The Ensembl identifiers of all protein-coding genes identified as in LD with the 12 variants for cIMT and 15 variants for carotid plaque (including variants from main and suggestive signals, Table 1 and Supplementary Table 5), and five genes for which there is strong evidence of colocalization (Table 3), were mapped to UniProt accession numbers, using the UniProt ID mapping service (http://www.uniprot.org/uploadlists/). A GO Slim analysis was performed on this list using QuickGO (www.ebi.ac.uk/QuickGO) and the Generic GO Slim. The GO terms used in the final slim analysis were further refined by adding/removing GO terms to provide more detailed information about the processes covered.

Methods for GO term enrichment analysis: The VLAD gene list analysis and visualization tool (http://proto.informatics.jax.org/prototypes/vlad/) was used to perform a GO term enrichment analysis on the same UniProt accessions as listed for the GO Slim. The background annotation set was obtained from the goa_human.gaf file (dated 21 November 2017, downloaded from ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/HUMAN/) and the ontology data was obtained from the go-basic.obo file provided in the VLAD tool (analysis run 28 November 2017).

The LD block around top SNPs associated with cIMT and carotid plaque was constructed using LD information from the 1000 Genomes panel, as previously outlined in Finan et al.16. Briefly, the boundaries of the LD region were defined as the positions of the variants furthest upstream and downstream of a GWAS SNP with an r2 value of ≥0.5 and within a 1-Mbp flank on either side of the GWAS variant. Associated variants that were not present in the 1000 Genomes panel that were not in LD with any other variants were given a nominal flank of 2.5 kbp on either side of the association. Gene annotations using Ensembl version 79 were then overlapped to the LD region.

Druggable genes

We examined the druggability status for the nearest coding genes identified in our GWAS analysis on cIMT and carotid plaque, including significant (novel and replicated) and suggestive ones, as well as genes identified through colocalization analysis. The druggable gene set was calculated using the previously described criteria: novel targets of first-in-class drugs licensed since 2005; the targets of drugs currently in late phase clinical development; pre-clinical phase small molecules with protein binding measurements reported in the ChEMBL database; and genes encoding secreted or plasma membrane proteins that are targets of monoclonal antibodies and other bio-therapeutics16. We defined three tiers of druggable gene sets based on their drug development. In Tier 1, 1427 genes were targets of approved small molecules and biotherapeutic drugs and clinical-phase drug candidates. Tier 2 comprised 682 genes encoding targets with known bioactive drug-like small molecule binding partners and those with significant sequence similarity to approved drug targets. Tier 3 contained 2370 genes encoding secreted or extracellular proteins, proteins with more distant similarity to approved drug targets, and druggable genes not included in Tier 1 or 2 such as GPCRs, nuclear hormone receptors, ion channels, kinases, and phosphodiesterases.

URLs

For GTEx, see http://gtexportal.org/. For Coloc, see https://cran.r-project.org/web/packages/coloc/coloc.pdf. For, Moloc, see https://github.com/clagiamba/moloc/blob/master/man/moloc-package.Rd. For CARDIoGRAMPlusC4D, see www.cardiogramplusc4d.org/. For LD scores, www.broadinstitute.org/~bulik/eur_ldscores/. For UniProt ID, www.uniprot.org/uploadlists/. For QuickGO, www.ebi.ac.uk/QuickGO. For VLAD tool, see http://proto.informatics.jax.org/prototypes/vlad/.

Data availability

All relevant summary statistics data that support the findings of this study have been deposit in the database of Genotypes and Phenotypes (dbGaP) under the CHARGE acquisition number (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000930.v6.p1; accession phs000930.v6.p1). GWAS data for most US studies are already available in dbGAP.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A full list of consortium members can be found at the end of the article.

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Acknowledgements

The work was supported by the following grants: National Institute of Health grants: R21HL123677, R21-HL140385, DK104806-01A1, R01-MD012765-01A1 (NF), National Institutes of Health awards R01HG009120, R01HG006399, U01CA194393, T32NS048004 (CG), the American Heart Association Grant #17POST33350042 (PV), the British Heart Foundation (RG/13/5/30112) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (RCL and RPH), the British Heart Foundation FS/14/55/30806 (JCH), the German Federal Ministry of Education and Research (BMBF) in the context of the e:Med program (e:AtheroSysMed), the DFG as part of the CRC 1123 (B3), and the FP7/2007-2103 European Union project CVgenes@target (grant agreement number Health-F2-2013-601456). We thank Li-Ming Gan for assistance with the STARNET study and Jon White for assistance with UCLEB analyses. Additional acknowledgements are included in Supplementary Note 2.

Author information

Author notes

  1. These authors contributed equally: Nora Franceschini, Claudia Giambartolomei.

Affiliations

  1. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27516, USA

    • Nora Franceschini
    • , Misa Graff
    • , Gerardo Heiss
    •  & Wayne D. Rosamond
  2. Department of Pathology and Laboratory Medicine, University of California (UCLA), Los Angeles, Los Angeles, CA, 90095, USA

    • Claudia Giambartolomei
    • , Bogdan Pasaniuc
    •  & Masahiro Kanai
  3. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    • Paul S. de Vries
    • , Eric Boerwinkle
    • , Saori Sakaue
    •  & Alanna C. Morrison
  4. Institute of Cardiovascular Science, University College London, London, WC1 6BT, UK

    • Chris Finan
    • , Rachael P. Huntley
    • , Ruth C. Lovering
    • , Jorgen Engmann
    • , Sonia Shah
    • , Tina Shah
    • , John Deanfield
    •  & Aroon D. Hingorani
  5. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA

    • Joshua C. Bis
    • , Susan R. Heckbert
    • , Traci M. Bartz
    • , Kerri L. Wiggins
    • , Nick Smith
    •  & W. T. Longstreth Jr
  6. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA

    • Salman M. Tajuddin
    • , Alan B. Zonderman
    • , Lenore J. Launer
    • , Tamara B. Harris
    • , Tamara B. Harris
    •  & Michele K. Evans
  7. Department of Genetic Epidemiology, University of Regensburg, Regensburg, 93053, Germany

    • Thomas W. Winkler
  8. Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015, The Netherlands

    • Maryam Kavousi
    • , Elisabeth M. van Leeuwen
    • , Aaron Isaacs
    • , Albert Hofman
    • , André G. Uitterlinden
    • , Fernando Rivadeneira
    • , Oscar H. Franco
    • , Oscar L. Rueda-Ochoa
    • , Hieab H. H. Adams
    • , M. Arfan Ikram
    • , Abbas Dehghan
    •  & Cornelia M. van Duijn
  9. Institute of Health Informatics, University College London, London, WC1E 6BT, UK

    • Caroline Dale
    •  & Juan P. Casas
  10. Icelandic Heart Association, Kopavogur, IS-201, Iceland

    • Albert V. Smith
    •  & Vilmundur Gudnason
  11. University of Iceland, Reykjavik, 101, Iceland

    • Albert V. Smith
    •  & Vilmundur Gudnason
  12. Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, 8036, Austria

    • Edith Hofer
    •  & Reinhold Schmidt
  13. Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, 8036, Austria

    • Edith Hofer
  14. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 3015, The Netherlands

    • Ilja M. Nolte
    •  & Harold Snieder
  15. Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA

    • Lingyi Lu
    • , Carl D. Langefeld
    •  & Fang-Chi Hsu
  16. Institute for Medical Informatics, Statistics and Epidemiology, , University of Leipzig, Leipzig, 04107, Germany

    • Markus Scholz
    • , Janne Pott
    •  & Markus Loeffler
  17. LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, 04107, Germany

    • Markus Scholz
    • , Daniel Teupser
    • , Janne Pott
    • , Joachim Thiery
    • , Lesca M. Holdt
    • , Ralph Burkhardt
    •  & Markus Loeffler
  18. Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, F-33000, Bordeaux, France

    • Muralidharan Sargurupremraj
    • , Christophe Tzourio
    • , Ganesh Chauhan
    •  & Stéphanie Debette
  19. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland

    • Niina Pitkänen
    •  & Olli Raitakari
  20. Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Oscar Franzén
    • , Eric E. Schadt
    • , Panos Roussos
    • , Eric E. Schadt
    • , Simon Koplev
    •  & Johan L. M. Björkegren
  21. Clinical Gene Networks AB, Stockholm, 104 62, Sweden

    • Oscar Franzén
    • , Arno Ruusalepp
    • , Eric E. Schadt
    • , Arno Ruusalepp
    •  & Johan L. M. Björkegren
  22. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK

    • Peter K. Joshi
    • , Harry Campbell
    • , Jacqueline Price
    • , Stela McLachlan
    •  & James F. Wilson
  23. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300, RC, The Netherlands

    • Raymond Noordam
    • , Stella Trompet
    •  & Stella Trompet
  24. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • Riccardo E. Marioni
    • , Ian J. Deary
    •  & Joanna M. Wardlaw
  25. Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK

    • Riccardo E. Marioni
  26. Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Framingham, MA, 01702-5827, USA

    • Shih-Jen Hwang
  27. National Heart, Lung and Blood Institute’s Intramural Research Program, Framingham Heart Study, Framingham, MA, 01702-5827, USA

    • Shih-Jen Hwang
    • , Xiaoling Zhang
    • , Anita L. DeStefano
    •  & Andrew D. Johnson
  28. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA

    • Solomon K. Musani
    • , Adolfo Correa
    •  & Pramod Anugu
  29. Department of Neurology, University Medicine Greifswald, Greifswald, 17475, Germany

    • Ulf Schminke
  30. Department of Medicine, Columbia University, New York, NY, 10032, USA

    • Walter Palmas
  31. Department of Biochemistry, Maastricht Centre for Systems Biology (MaCSBio), CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, 6229, The Netherlands

    • Aaron Isaacs
  32. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA

    • Albert Hofman
  33. Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany

    • Alexander Teumer
    •  & Henry Völzke
  34. DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, 17475, Germany

    • Alexander Teumer
    • , Henry Völzke
    • , Marcus Dörr
    •  & Uwe Völker
  35. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, 25157, USA

    • Amanda J. Cox
  36. Menzies Health Institute Queensland, Griffith University, Southport, QLD, 4222, Australia

    • Amanda J. Cox
  37. Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, 3015, The Netherlands

    • André G. Uitterlinden
    •  & Fernando Rivadeneira
  38. MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 6BT, UK

    • Andrew Wong
    • , Diana Kuh
    •  & Rebecca Hardy
  39. Department of Medicine, University of Groningen, University Medical Center Groningen, Groningen, 2300, The Netherlands

    • Andries J. Smit
  40. Department of Epidemiology, and School of Medicine, Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA

    • Anne B. Newman
  41. Department of Epidemiology and Public Health, University College London, London, WC1E 6BT, UK

    • Annie Britton
    • , Meena Kumari
    •  & Mika Kivimaki
  42. Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Tartu, 51010, Estonia

    • Arno Ruusalepp
    • , Arno Ruusalepp
    •  & Johan L. M. Björkegren
  43. Department of Cardiac Surgery, Tartu University Hospital, Tartu, 51010, Estonia

    • Arno Ruusalepp
    •  & Arno Ruusalepp
  44. Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, 17177, Sweden

    • Bengt Sennblad
    • , Karl Gertow
    • , Rona J. Strawbridge
    •  & Anders Hamsten
  45. Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, 75108, Sweden

    • Bengt Sennblad
  46. Department of Clinical Sciences in Malmö, Lund University, Malmö, SE-205 02, Sweden

    • Bo Hedblad
    • , Cristiano Fava
    • , Olle Melander
    •  & Olle Melander
  47. Department of Human Genetics, University of California (UCLA), Los Angeles, CA, 90095, USA

    • Bogdan Pasaniuc
  48. Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, 1081, HL, The Netherlands

    • Brenda W. Penninx
  49. Applied Sciences, Premier, Inc., Charlotte, NC, 28277, USA

    • Christina L. Wassel
  50. Department of Medicine, University of Verona, Verona, 37134, Italy

    • Cristiano Fava
  51. Department of Medical Biotechnology and Translational Medicine, Università di Milano, Milan, 20133, Italy

    • Damiano Baldassarre
  52. Centro Cardiologico Monzino, IRCCS, Milan, 20138, Italy

    • Damiano Baldassarre
    • , Elena Tremoli
    •  & Fabrizio Veglia
  53. St. Elizabeth’s Medical Center, Tufts University School of Medicine, Boston, MA, 02135, USA

    • Daniel H. O’Leary
  54. Institute of Laboratory Medicine, University Hospital Munich, LMU Munich, 80539, Germany

    • Daniel Teupser
    •  & Lesca M. Holdt
  55. Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, 20133, Italy

    • Elena Tremoli
  56. Department of Clinical and Experimental Medicine, Internal Medicine, Angiology and Arteriosclerosis Diseases, University of Perugia, Perugia, 06123, Italy

    • Elmo Mannarino
  57. Centro Diagnostico Italiano, Milan, 20147, Italy

    • Enzo Grossi
  58. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030-3411, USA

    • Eric Boerwinkle
  59. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, 94309, USA

    • Erik Ingelsson
  60. Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, 75185, Sweden

    • Erik Ingelsson
    •  & Stefan Gustafsson
  61. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, G1120, USA

    • Erik Ingelsson
  62. Heart Center Leipzig, Leipzig, 04103, Germany

    • Frank Beutner
  63. Centre for Brain Research, Indian Institute of Science, Bangalore, 560012, India

    • Ganesh Chauhan
  64. Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK

    • Hugh S. Markus
    •  & Matthew Traylor
  65. Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • Ian J. Deary
  66. Department of Cardiology, Leiden University Medical Center, Leiden, 2300, RC, The Netherlands

    • J. Wouter Jukema
    • , Stella Trompet
    •  & J. Wouter Jukema
  67. Department of Internal Medicine, Radboud University Medical Center, Nijmegen, 6525, GA, The Netherlands

    • Jacqueline de Graaf
  68. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK

    • Jemma C. Hopewell
    •  & Cornelia M. van Duijn
  69. Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA

    • Jingjing Liang
    •  & Xiaofeng Zhu
  70. Institute for Laboratory Medicine, University of Leipzig, Leipzig, 04109, Germany

    • Joachim Thiery
  71. Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA

    • Kenneth Rice
    •  & Traci M. Bartz
  72. Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA

    • Kent D. Taylor
    • , Xiuqing Guo
    •  & Jerome I. Rotter
  73. Department of Internal Medicine, Rush University Medical Center, Chicago, IL, 60612, USA

    • Klodian Dhana
  74. Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, GA, 6525, The Netherlands

    • Lambertus A. L. M. Kiemeney
    •  & Tessel E. Galesloot
  75. Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, 751 05, Sweden

    • Lars Lind
  76. Department of Genetics, University of North Carolina, Chapel Hill, NC, 27516, USA

    • Laura M. Raffield
  77. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475, Germany

    • Marcus Dörr
  78. Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-University (LMU), Munich, 80539, Germany

    • Martin Dichgans
    • , Rainer Malik
    •  & Steffen Tiedt
  79. Munich Cluster for Systems Neurology (SyNergy), Munich, 81377, Germany

    • Martin Dichgans
  80. Department of Neurology, Center for Neurology and Neurosurgery, Johann Wolfgang Goethe-University, Frankfurt am Main, 60323, Germany

    • Matthias Sitzer
  81. Institute for Social and Economic Research, Essex University, Colchester, CO4 3SQ, UK

    • Meena Kumari
  82. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA

    • Mike A. Nalls
  83. Data Tecnica International, Glen Echo, MD, 20812, USA

    • Mike A. Nalls
  84. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland

    • Olli Raitakari
  85. Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, 3012, Switzerland

    • Oscar H. Franco
  86. Electrocardiography Research Group, School of Medicine, Universidad Industrial de Santander, Bucaramanga, Santander, 680003, Colombia

    • Oscar L. Rueda-Ochoa
  87. Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Panos Roussos
  88. Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, NY, 10468, USA

    • Panos Roussos
  89. Population Health Research Institute, St George’s, University of London, London, SW17 0RE, UK

    • Peter H. Whincup
  90. Inserm U1167, F-59000, Lille, France

    • Philippe Amouyel
  91. Institut Pasteur de Lille, U1167, F-59000, Lille, France

    • Philippe Amouyel
  92. Université de Lille, U1167 - RID-AGE & Centre Hospitalier Universitaire de Lille, U1167, F-59000, Lille, France

    • Philippe Amouyel
  93. Sorbonne Université, Cardiovascular Prevention Unit, Pitié Salpétrière Hospital, Paris, 75013, France

    • Philippe Giral
  94. Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA

    • Quenna Wong
  95. Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland

    • Rainer Rauramaa
  96. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland

    • Rainer Rauramaa
  97. Institute of Laboratory Medicine, University of Leipzig, Leipzig, 04109, Germany

    • Ralph Burkhardt
  98. Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, 93053, Germany

    • Ralph Burkhardt
  99. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2333, The Netherlands

    • Renée de Mutsert
    •  & Dennis O. Mook-Kanamori
  100. Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 1QU, UK

    • Richard W. Morris
  101. Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 0XH, UK

    • Rona J. Strawbridge
  102. Department of Primary Care & Population Health, University College London, London, WC1E 6BT, UK

    • S. Goya Wannamethee
  103. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77, Sweden

    • Sara Hägg
    •  & Patrik K. Magnusson
  104. Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA

    • Sudha Seshadri
  105. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Kuopio, FI-70210, Finland

    • Sudhir Kurl
  106. Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA

    • Susan R. Heckbert
    • , Nick Smith
    •  & Bruce M. Psaty
  107. Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 1QU, UK

    • Susan Ring
    •  & Deborah A. Lawlor
  108. MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 1TH, UK

    • Susan Ring
    •  & Deborah A. Lawlor
  109. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33014, Finland

    • Terho Lehtimäki
    •  & Leo-Pekka Lyytikäinen
  110. Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, 33014, Finland

    • Terho Lehtimäki
    •  & Leo-Pekka Lyytikäinen
  111. Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, S-171 77, Sweden

    • Ulf de Faire
  112. Department of Cardiology, Karolinska University Hospital, Stockholm, S-171 77, Sweden

    • Ulf de Faire
  113. Genetics Institute, University College London, London, WC1E 6BT, UK

    • Vincent Plagnol
  114. Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, 21205, USA

    • Wendy Post
  115. Section of Biomedical Genetics, School of Medicine, Boston University, Boston, MA, 02215, USA

    • Xiaoling Zhang
  116. Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA

    • Xiuqing Guo
    •  & Jerome I. Rotter
  117. Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, 8010, Austria

    • Yasaman Saba
    •  & Helena Schmidt
  118. Department of Epidemiology & Biostatistics, Imperial College London, London, SW7 2AZ, UK

    • John C. Chambers
    •  & Abbas Dehghan
  119. GGZ inGeest and Amsterdam Public Health Research Institute, Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, 1081 HV, The Netherlands

    • Adrie Seldenrijk
  120. Cardiovascular Health Research Unit and Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, 98195, USA

    • Bruce M. Psaty
  121. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands

    • Dennis O. Mook-Kanamori
  122. Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA

    • Donald W. Bowden
  123. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK

    • James F. Wilson
  124. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA

    • James G. Wilson
  125. Centre for Clinical Brain Sciences, and UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH16 4SB, UK

    • Joanna M. Wardlaw
  126. Swansea University Medical School, Swansea, SA2 8PP, UK

    • Julian Halcox
  127. Centre for Cardiovascular Genetics, Institute Cardiovascular Science, University College London, London, WC1E 6BT, UK

    • Steve E. Humphries
  128. Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany

    • Uwe Völker
  129. Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, SE-141 57, Sweden

    • Johan L. M. Björkegren
  130. Intramural Administration Management Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, 20892, USA

    • Christopher J. O’Donnell
  131. Cardiology Section, Boston Veteran’s Administration Healthcare, Boston, MA, 02130, USA

    • Christopher J. O’Donnell
  132. Harvard Medical School, Boston, MA, 02115, USA

    • Daniel I. Chasman
    • , Paul M. Ridker
    •  & Christopher J. O’Donnell
  133. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan

    • Yukinori Okada
    •  & Yoichiro Kamatani
  134. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, 565-0871, Japan

    • Yukinori Okada
  135. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan

    • Yukinori Okada
  136. INSERM U1219 Bordeaux Population Health Research Center, Bordeaux, F-33000, France

    • Aniket Mishra
  137. University of Bordeaux, Bordeaux, F-33000, France

    • Aniket Mishra
  138. Stroke Research Group, Division of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 1TN, UK

    • Loes Rutten-Jacobs
  139. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    • Anne-Katrin Giese
    •  & Natalia S. Rost
  140. J. Philip Kistler Stroke Research Center, Department of Neurology, MGH, Boston, MA, 02215, USA

    • Anne-Katrin Giese
    • , Christopher D. Anderson
    • , Hakan Ay
    • , Natalia S. Rost
    •  & Jonathan Rosand
  141. Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, 3584 CX, Netherlands

    • Sander W. van der Laan
  142. deCODE genetics/AMGEN inc, Reykjavik, 101, Iceland

    • Solveig Gretarsdottir
    • , Gudmar Thorleifsson
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  143. Center for Genomic Medicine, Massachusetts General Hospital (MGH), Boston, MA, 02114, USA

    • Christopher D. Anderson
    •  & Jonathan Rosand
  144. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA

    • Christopher D. Anderson
    • , Guido J. Falcone
    • , Ingrid E. Christophersen
    • , Carolina Roselli
    • , Steven A. Lubitz
    • , Patrick T. Ellinor
    •  & Jonathan Rosand
  145. Population Health Research Institute, McMaster University, Hamilton, L8L 2X2, Canada

    • Michael Chong
    • , Martin J. O’Donnell
    • , Salim Yusuf
    •  & Guillaume Pare
  146. Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 819-0935, Japan

    • Tetsuro Ago
  147. Albrecht Kossel Institute, University Clinic of Rostock, Rostock, 18147, Germany

    • Peter Almgren
    •  & Arndt Rolfs
  148. INSERM U1167, Institut Pasteur de Lille, Lille, F-59000, France

    • Philippe Amouyel
  149. Department of Public Health, Lille University Hospital, Lille, F-59000, France

    • Philippe Amouyel
  150. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, AA Martinos Center for Biomedical Imaging, Boston, MA, 02129, USA

    • Hakan Ay
  151. Division of Neurology, Faculty of Medicine, Brain Research Center, University of British Columbia, Vancouver, 170-637, Canada

    • Oscar R. Benavente
  152. School of Life Science, University of Lincoln, Lincoln, LN6 7TS, UK

    • Steve Bevan
  153. Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, 20133, Italy

    • Giorgio B. Boncoraglio
  154. Department of Neurology, Mayo Clinic Rochester, Rochester, MN, 55905, USA

    • Robert D. Brown Jr.
  155. MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 1TN, UK

    • Adam S. Butterworth
    • , John Danesh
    •  & Joanna M. M. Howson
  156. The National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB2 1TN, UK

    • Adam S. Butterworth
    •  & John Danesh
  157. Neurovascular Research Laboratory, Vall d’Hebron Institut of Research, Neurology and Medicine Departments-Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona, 08193, Spain

    • Caty Carrera
    •  & Israel Fernandez-Cadenas
  158. Stroke Pharmacogenomics and Genetics, Fundacio Docència i Recerca MutuaTerrassa, Terrassa, 08222, Spain

    • Caty Carrera
    •  & Israel Fernandez-Cadenas
  159. Children’s Research Institute, Children’s National Medical Center, Washington, DC, 20052, USA

    • Cara L. Carty
  160. Center for Translational Science, George Washington University, Washington, DC, 20052, USA

    • Cara L. Carty
  161. Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    • Daniel I. Chasman
    •  & Paul M. Ridker
  162. Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, 22904-4259, USA

    • Wei-Min Chen
    • , Ani Manichaikul
    • , Stephen S. Rich
    • , Michele M. Sale
    •  & Mete Civelek
  163. Department of Neurology, University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD, 21201, USA

    • John W. Cole
    •  & Steven J. Kittner
  164. Institute of Cardiovascular Research, Royal Holloway University of London, Egham, TW20 OEX, UK

    • Ioana Cotlarciuc
    •  & Pankaj Sharma
  165. Department of Psychiatry,The Hope Center Program on Protein Aggregation and Neurodegeneration (HPAN), Washington University, School of Medicine, St. Louis, MO, 98195, USA

    • Carlos Cruchaga
  166. Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, 98195, USA

    • Carlos Cruchaga
  167. Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK

    • John Danesh
  168. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Paul I. W. de Bakker
  169. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands

    • Paul I. W. de Bakker
  170. Boston University School of Public Health, Boston, MA, 02118, USA

    • Anita L. DeStefano
    •  & Qiong Yang
  171. Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, 751 05, Sweden

    • Marcel den Hoed
  172. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0SL, UK

    • Qing Duan
    • , Claudia Langenberg
    •  & Nicholas J. Wareham
  173. Department of Neurology and Stroke Center, Basel University Hospital, Basel, 4031, Switzerland

    • Stefan T. Engelter
  174. Neurorehabilitation Unit, University and University Center for Medicine of Aging and Rehabilitation Basel, Felix Platter Hospital, Basel, 4055, Switzerland

    • Stefan T. Engelter
  175. Department of Neurology, Yale University School of Medicine, New Haven, CT, 06510, USA

    • Guido J. Falcone
  176. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA

    • Rebecca F. Gottesman
  177. Neuroscience Institute, SF Medical Center, Trenton, NJ, 08629, USA

    • Raji P. Grewal
  178. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA

    • Jeffrey Haessler
    • , Charles Kooperberg
    •  & Alexander P. Reiner
  179. Department of Neurology, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds, LS1 3EX, UK

    • Ahamad Hassan
  180. National Institute for Health and Welfare, Helsinki, FI-00271, Finland

    • Aki S. Havulinna
    •  & Veikko Salomaa
  181. FIMM - Institute for Molecular Medicine Finland, Helsinki, FI-00271, Finland

    • Aki S. Havulinna
  182. Public Health Stream, Hunter Medical Research Institute, New Lambton, NSW 2305, Australia

    • Elizabeth G. Holliday
  183. Faculty of Health and Medicine, University of Newcastle, Newcastle, 2308, Australia

    • Elizabeth G. Holliday
  184. School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35487, USA

    • George Howard
  185. Aflac Cancer and Blood Disorder Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA

    • Hyacinth I. Hyacinth
  186. Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, 35487, USA

    • Marguerite R. Irvin
  187. Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    • Xueqiu Jian
  188. Neurovascular Research Group (NEUVAS), Neurology Department, Institut Hospital del Mar d’Investigació Mèdica, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain

    • Jordi Jiménez-Conde
  189. Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, FL, 32611, USA

    • Julie A. Johnson
  190. Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL, 32611, USA

    • Julie A. Johnson
  191. Department of Biology, East Carolina University, Greenville, NC, 27858, USA

    • Keith L. Keene
  192. Center for Health Disparities, East Carolina University, Greenville, NC, 27858, USA

    • Keith L. Keene
  193. University of Cincinnati College of Medicine, Cincinnati, OH, 45220, USA

    • Brett M. Kissela
    • , Dawn O. Kleindorfer
    •  & Daniel Woo
  194. RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan

    • Michiaki Kubo
  195. University of Colorado, Denver, CO, 80203, USA

    • Leslie Lange
  196. Center for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    • Carl D. Langefeld
  197. Department of Neurology, Radiology, and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, 98195, USA

    • Jin-Moo Lee
  198. Department of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Leuven, 3000, Belgium

    • Robin Lemmens
  199. VIB Center for Brain & Disease Research, University Hospitals Leuven, Department of Neurology, Leuven, 3000, Belgium

    • Robin Lemmens
  200. University of Lille, INSERM U1171, CHU Lille, Lille, F-59000, France

    • Didier Leys
  201. Department of Medical and Molecular Genetics, King’s College London, London, WC2R 2LS, UK

    • Cathryn M. Lewis
  202. SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, WC2R 2LS, UK

    • Cathryn M. Lewis
  203. Cardiovascular Epidemiology Unit, Department Public Health & Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK

    • Wei-Yu Lin
  204. Northern Institute for Cancer Research, Paul O’Gorman Building, Newcastle University, Newcastle, NE2 4AD, UK

    • Wei-Yu Lin
  205. Department of Clinical Sciences Lund, Neurology, Lund University, Lund, 221 00, Sweden

    • Arne G. Lindgren
  206. Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, 222 29, Sweden

    • Arne G. Lindgren
  207. Bioinformatics Core Facility, University of Gothenburg, Gothenburg, 405 30, Sweden

    • Erik Lorentzen
  208. University of Technology Sydney, Faculty of Health, Ultimo, NSW 2007, Australia

    • Jane Maguire
  209. Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    • Patrick F. McArdle
  210. Department of Neurology, Mayo Clinic, Jacksonville, FL, 32224, USA

    • James F. Meschia
  211. Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA

    • Thomas H. Mosley
  212. Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, FL, 39216, USA

    • Thomas H. Mosley
  213. Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 819-0395, Japan

    • Toshiharu Ninomiya
    •  & Jun Hata
  214. Clinical Research Facility, Department of Medicine, NUI Galway, Galway, H91 TK33, Ireland

    • Martin J. O’Donnell
  215. Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584, The Netherlands

    • Sara L. Pulit
  216. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK

    • Kristiina Rannikmäe
  217. Department of Neurology, University Medicine Greifswald, Greifswald, 17489, Germany

    • Alexander P. Reiner
    •  & Ulf Schminke
  218. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    • Kathryn M. Rexrode
  219. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK

    • Peter M. Rothwell
  220. Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA

    • Tatjana Rundek
    •  & Ralph L. Sacco
  221. Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, 13-8654, Japan

    • Saori Sakaue
  222. Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA

    • Bishwa R. Sapkota
    •  & Dharambir K. Sanghera
  223. Department of Neurology, Medical University of Graz, Graz, 8036, Austria

    • Reinhold Schmidt
  224. University Medicine Greifswald, Institute for Community Medicine, SHIP-KEF, Greifswald, 17489, Germany

    • Carsten O. Schmidt
  225. Department of Neurology, Jagiellonian University, Krakow, 31-007, Poland

    • Agnieszka Slowik
  226. University of Edinburgh, Edinburgh, EH8 9JZ, UK

    • Cathie L. M. Sudlow
  227. Department of Neurology, Justus Liebig University, Giessen, 35390, Germany

    • Christian Tanislav
  228. Department of Clinical Neurosciences/Neurology, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, SE-405, Sweden

    • Turgut Tatlisumak
  229. Sahlgrenska University Hospital, Gothenburg, SE-405, Sweden

    • Turgut Tatlisumak
  230. Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC 3084, Australia

    • Vincent N. S. Thijs
  231. Austin Health, Department of Neurology, Heidelberg, Victoria, 3084, Australia

    • Vincent N. S. Thijs
  232. School of Medicine, Dentistry and Nursing at the University of Glasgow, Glasgow, G12 8QQ, UK

    • Matthew Walters
  233. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA

    • Sylvia Wassertheil-Smoller
  234. Department of Human Genetics, McGill University, Montreal, H3A 0G4, Canada

    • Tomi Pastinen
  235. Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, 75006, France

    • Veronica Codoni
    •  & David A. Trégouët
  236. ICAN, Institute for Cardiometabolism and Nutrition, Paris, 75013, France

    • Veronica Codoni
    •  & David A. Trégouët
  237. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22904-4259, USA

    • Mete Civelek
  238. Seattle Epidemiologic Research and Information Center, VA Office of Research and Development, Seattle, WA, 98108, USA

    • Nick Smith
  239. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA

    • Ingrid E. Christophersen
    • , Steven A. Lubitz
    •  & Patrick T. Ellinor
  240. Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud, 3004, Norway

    • Ingrid E. Christophersen
  241. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, 119077, Singapore

    • E. Shyong Tai
  242. National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK

    • Jaspal S. Kooner
  243. Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, 162-8655, Japan

    • Norihiro Kato
    •  & Fumihiko Takeuchi
  244. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA

    • Jiang He
  245. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, 9700, RB, Netherlands

    • Pim van der Harst
  246. Department of Epidemiology and Biostatistics, Imperial College London, MRC-PHE Centre for Environment and Health, School of Public Health, London, W2 1PG, UK

    • Paul Elliott
  247. Department of Cardiology, Ealing Hospital NHS Trust, Southall, HA1 3UJ, UK

    • John C. Chambers
  248. Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA

    • Dharambir K. Sanghera
  249. Oklahoma Center for Neuroscience, Oklahoma City, OK, 73104, USA

    • Dharambir K. Sanghera
  250. Department of Pathology and Genetics, Institute of Biomedicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, SE-405, Sweden

    • Christina Jern
  251. Department of Neurology, Helsinki University Hospital, Helsinki, FI-00029, Finland

    • Daniel Strbian
  252. Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, FI-00029, Finland

    • Daniel Strbian
  253. Department of Neurology, University of Washington, Seattle, WA, 98195, USA

    • W. T. Longstreth Jr
  254. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

    • Danish Saleheen
  255. Faculty of Medicine, University of Iceland, Reykjavik, 201, Iceland

    • Kari Stefansson
  256. Departments of Neurology and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA

    • Bradford B. Worrall
  257. Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan

    • Yoichiro Kamatani

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Consortia

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Contributions

N.F., C.G., J.C.B., M.K., C.D., M.S., S.K.M., U.S., W.P., A.B.Z., A.H., A.T., A.G.U., A.B.N., B.W.P., C.D.L., E.T., F.R., H.V., I.J.D., L.J.L., M.D., O.R., O.H.F., R.S., R.M., T.B.H., T.L., U.F., W.P., A.D., A.S., A.H., C.M.D., D.A.L., D.O.M.-K., D.W.B., H.S., J.F.W., J.G.W., J.I.R., J.M.W., M.L., M.K.E., S.E.H., U.V., V.G., A.D.H., J.P.C., and C.J.O. contributed to study concept and design. C.D., M.S., S.K.M., U.S., W.P., A.I., A.C., A.B.Z., A.T., A.G.U., A.W., A.J.S., A.B., A.R., B.H., B.W.P., C.F., D.B., D.H.O., D.T., D.K., E.T., E.M., E.G., E.B., E.E.S., E.I., F.R., F.B., G.H., H.C., H.V., H.S.M., I.J.D., J.W.J., J.G., J.P., J.T., J.E., K.D.T., L.K., L.L., L.J.L., L.H., M.D., M.S., M.K., M.K., M.A.N., O.M., O.R., P.H.W., P.G., P.A., R.R., R.B., R.H., R.S., R.M., R.W.M.,. S.G.W., S.M.L., S.T., S.K., S.R.H., S.R., T.B.H., T.L., T.G., T.S., U.F., V.P., W.R., W.P., X.Z., A.S., A.H., B.M.P., C.M.D., D.A.L., D.O.M.-K., D.W.B., H.S., J.F.W., J.G.W., J.I.R., J.C.H., J.M.W., J.D., J.H., M.K.E., S.E.H., U.V., V.G., A.D.H., J.L.M.B., J.P.C., and C.J.O. contributed to acquisition of genotyping or phenotypic data. N.F., C.G., C.F., J.C.B., R.P.H., R.C.L., S.M.T., T.W.W., M.G., M.K., C.D., A.V.S., E.H., E.M.L., I.M.N., L.L., M.S., M.S., N.P., O.F., P.K.J., R.N., R.E.M., S.-J.H., S.K.M., U.S., A.I., A.T., K.R., A.J.C., B.S., C.D.L., C.W., F.V., G.C., H.S., J.P., J.L., K.G., L.M.R., M.T., M.A.N., O.M., P.R., P.A., Q.W., R.J.S., S.H., S.S., S.M.L., T.S., X.Z., X.Z., X.G., Y.S., and L.-.P.L. contributed to statistical analysis and interpretation of the data. N.F., C.G., P.S.V., J.C.B., M.K., S.K.M., A.D.H., and J.P.C. contributed to drafting of the manuscript. All authors contributed to the critical revision of the manuscript.

Competing interests

C.F. received a fee for speaking at a course by Springer Healthcare/Malesci. E.I. is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project. M.A.N.’s participation in this project was supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. M.A.N. also consults for Illumina Inc., the Michael J. Fox Foundation, and University of California Healthcare. B.M.P. serves on the DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.A.L. has received support from Roche Diagnostics and Medtronic for biomarker research unrelated to this paper. J.P.C. has received funding from GSK regarding methodological work around electronic health records, and -omics for drug discovery. All remaining authors declare no competing interests.

Corresponding authors

Correspondence to Johan L. M. Björkegren or Christopher J. O’Donnell.

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DOI

https://doi.org/10.1038/s41467-018-07340-5

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