Abstract
Objective We sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.
Methods We evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.
Results We observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p < 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1).
Conclusions Genetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.
Author Contributions
All named authors have contributed meaningfully to the present study. Specific contributions for each author are described below.
S.L. Pulit: conception of research design, data analysis, drafting of manuscript, critical revision of manuscript
L.C. Weng: data analysis, critical revision of manuscript
P.F. McArdle: data acquisition, data analysis, critical revision of manuscript
L. Trinquart: data acquisition, critical revision of manuscript
S.H. Choi: data acquisition, critical revision of manuscript
B.D. Mitchell: data acquisition, study supervision, critical revision of manuscript
J. Rosand: data acquisition, study supervision, critical revision of manuscript
P.I.W. de Bakker: study supervision, critical revision of manuscript
E.J. Benjamin: data acquisition, study supervision, critical revision of manuscript
P.T. Ellinor: data acquisition, study supervision, critical revision of manuscript
S.J. Kittner: data acquisition, study supervision, critical revision of manuscript
S.A. Lubitz: conception of research design, study supervision, drafting of manuscript, critical revision of manuscript
C.D. Anderson: conception of research design, study supervision, drafting of manuscript, critical revision of manuscript
Introduction
Atrial fibrillation affects nearly 34 million individuals worldwide1 and is associated with a five-fold increased risk of ischemic stroke,2 a leading cause of death and disability.3,4 Atrial fibrillation promotes blood clot formation in the heart which can embolize distally, and is a leading cause of cardioembolism. Secondary prevention of cardioembolic stroke is directed at identifying atrial fibrillation as a potential cause, and initiating anticoagulation to prevent recurrences. Yet atrial fibrillation can remain occult even after extensive workup owing to the paroxysmal nature and fact that it can be asymptomatic. Since both atrial fibrillation and stroke are heritable, and since there is a compelling clinical need to determine whether stroke survivors have atrial fibrillation as an underlying cause, we sought to determine whether genetic risk of cardioembolic stroke can be approximated by measuring genetic susceptibility to atrial fibrillation.
Recent genome-wide association studies (GWAS) have demonstrated that both atrial fibrillation5 and ischemic stroke6,7 are complex disorders with polygenic architectures. The top loci for cardioembolic stroke, on chromosome 4q25 upstream of PITX2 and on 16q22 near ZFHX3, are both leading risk loci for atrial fibrillation.8–10 Despite overlap in top risk loci, the genetic susceptibility to both atrial fibrillation and cardioembolic stroke is likely to involve the aggregate contributions of hundreds or thousands of loci, consistent with other polygenic conditions.11
To understand whether genetic risk for atrial fibrillation is an important and potentially useful determinant of overall cardioembolic stroke risk, we analyzed 13,390 ischemic stroke cases and 28,026 referents from the NINDS-Stroke Genetics Network (SiGN)12 with genome-wide genotyping data. First, we assessed whether stroke patients with atrial fibrillation have a genetic predisposition to the arrhythmia, leveraging additional GWAS data from the Atrial Fibrillation Genetics Consortium (AFGen). Second, we compared genetic risk factors for atrial fibrillation and stroke to ascertain the extent to which heritable risk of cardioembolic stroke is explained by genetic risk factors for atrial fibrillation.
Methods
The Stroke Genetics Network (SiGN)
The Stroke Genetics Network (SiGN) was established with the aim of performing the largest genome-wide association study (GWAS) of ischemic stroke to date. The study design has been described previously12 and is summarized in the Supplementary Methods. Briefly, subjects in SiGN were classified into stroke subtypes using the Causative Classification System (CCS), which subtypes cases through an automated, web-based system that accounts for clinical data, test results, and imaging information.13,14 Within CCS, there are two sub-categories: CCS causative, which does not allow for competing subtypes in a single sample; and CCS phenotypic, which does. Additionally, ∼74% of samples were subtyped using the TOAST subtyping system.15 After quality control (QC), the SiGN dataset comprised 16,851 ischemic stroke cases and 32,473 stroke-free controls (Supplementary Methods and Supplementary Table 1). Here, we analyze only the European-and Africanancestry samples (13,390 cases and 28,026 controls).
Standard Protocol Approvals, Registrations, and Patient Consents
All cohorts included in the SiGN dataset received approval from the cohort-specific ethical standards committee. Cohorts received written informed consent from all patients or guardians of patients participating in the study, where applicable. Details on sample collection have been described previously.12
Identifying atrial fibrillation cases and controls
We defined atrial fibrillation in SiGN on the basis of five variables available in the CCS phenotyping system: (i) atrial fibrillation, (ii) paroxysmal atrial fibrillation, (iii) atrial flutter, (iv) sick sinus syndrome, and (v) atrial thrombus. This definition yielded 3,190 atrial fibrillation cases for analysis. We also defined a strict case set based on “atrial fibrillation” only (N = 1,751 cases) for sensitivity analyses (Supplementary Methods and Supplementary Figure 1).
From the 28,026 controls, we established a set of 3,861 control individuals in whom atrial fibrillation was indicated as not present. For the remaining subjects, we assumed that individuals did not have atrial fibrillation since atrial fibrillation status for most control samples in SiGN is unknown.
Genome-wide association testing of ischemic stroke subtypes and atrial fibrillation in SiGN
We merged genotype dosages together and kept SNPs with imputation quality > 0.8 and minor allele frequency (MAF) > 1% (Supplementary Methods). We performed association testing using a linear mixed model implemented in BOLT-LMM.16 We adjusted the model for the top ten principal components (PCs) and sex, in addition to the genetic relationship matrix (GRM; Supplementary Methods).16 We performed GWAS in atrial fibrillation and each of the stroke subtypes available in SiGN. Results were unadjusted for age, as adjusting for age in the atrial fibrillation GWAS gave results highly concordant with the age-unadjusted results (Supplementary Results).
Heritability calculations
We calculated additive SNP-based heritability estimates for ischemic stroke, stroke subtypes, and atrial fibrillation using restricted maximum likelihood implemented in BOLT-REML (Supplementary Methods).16
Genetic correlation between atrial fibrillation and ischemic stroke subtypes
We used summary-level data from a prior Atrial Fibrillation Genetics (AFGen) Consortium meta-analysis of atrial fibrillation5 to calculate a z-score for each SNP in that GWAS. Additionally, we calculated a z-score for each SNP from our SiGN GWAS of each stroke subtype and atrial fibrillation. As a null comparator, we downloaded SNP z-scores from a GWAS of educational attainment17 available through LDHub (http://ldsc.broadinstitute.org/, accessed 11-1-2017). We calculated Pearson’s r between z-scores from two traits to evaluate correlation (Supplementary Methods and Supplementary Figure 3).
Constructing an atrial fibrillation polygenic risk score
To construct an atrial fibrillation polygenic risk score (PRS), we used SNPs from a previously-derived atrial fibrillation PRS (Supplementary Methods).18 Briefly, the PRS was derived from an atrial fibrillation GWAS of 17,931 cases and 115,142 controls.5 This PRS comprised 1,168 SNPs with p < 1 × 10−4 and LD pruned at an r2 threshold of 0.5.18 Of these 1,168 SNPs, we identified 934 SNPs in the SiGN dataset with imputation info > 0.8 and MAF > 1%. We used these 934 SNPs to construct the atrial fibrillation PRS in the SiGN dataset. Additional details on the PRS construction can be found in the Supplementary Methods.
Testing an atrial fibrillation polygenic risk score in ischemic stroke subtypes
We tested for association between the atrial fibrillation PRS and stroke subtypes using logistic regression (Supplementary Methods). We included sex and the top 10 PCs as additional covariates. We optionally adjusted the association tests for age, diabetes mellitus, cardiovascular disease, smoking status (current smoker, former smoker, or never smoked), and hypertension.
We calculated the variance explained by the atrial fibrillation PRS in cardioembolic stroke by constructing a model in BOLT-REML that consisted of: (1) a variance component made up of SNPs for the GRM, and (2) a variance component made up of SNPs from the PRS (Supplementary Methods).
Data availability
Code, supporting data, and downloadable supplemental tables are available here: https://github.com/UMCUGenetics/Afib-Stroke-Overlap. The Supplementary Information contains additional information regarding data access, methods, and links to summary-level data.
Results
We began by testing our ability to rediscover known atrial fibrillation genetic associations in the SiGN dataset, assembled to study the genetics of ischemic stroke. We ran a genome-wide association study (GWAS) in SiGN using 3,190 cases with atrial fibrillation or paroxysmal atrial fibrillation, as well as other diagnoses suggestive of underlying atrial fibrillation19,20 (Methods, Table 1 and Supplementary Table 1) and 28,026 controls (Supplementary Figure 1). We found the top-associated SNPs to be highly concordant with a prior GWAS of atrial fibrillation performed by the Atrial Fibrillation Genetics (AFGen) Consortium (Supplementary Table 2). Adjusting the GWAS for age did not substantially change our findings (r = 0.83 between SNP effects from the age-unadjusted and age-adjusted GWAS).
Extending our analysis beyond these top associations, we next assessed whether stroke patients with atrial fibrillation have a similar overall genetic predisposition to the arrhythmia as seen in the independent AFGen GWAS. Additionally, we assessed the overlap between genetic predisposition to atrial fibrillation and each stroke subtype, allowing for the known phenotypic concordance between cardioembolic stroke and atrial fibrillation (89.5% of cardioembolic stroke cases in SiGN also have atrial fibrillation, Supplementary Table 1). We performed a series of GWAS in the SiGN data for atrial fibrillation and each of the stroke subtypes using BOLT-LMM16 (Methods), and calculated the z-score (beta/standard error) of each SNP in each phenotype. We then used summary-level results available from the prior (independent) GWAS of atrial fibrillation5 (from AFGen) and calculated the z-score for each SNP in that dataset.
Measuring Pearson’s correlation (r) between AFGen z-scores and z-scores from the atrial fibrillation GWAS in SiGN, we found only a modest correlation (r =0.07 across ∼7.8M SNPs, Figure 1). However, when we iteratively subsetted the AFGen GWAS results by the (absolute values of) z-scores of the SNPs, we found that correlation with the atrial fibrillation GWAS in SiGN increased as the z-score threshold became more stringent. For example, for those ∼4.5M SNPs with |z| > 1 in AFGen, correlation with atrial fibrillation SNPs in SiGN was 0.12; for those ∼1.9M SNPs with |z| > 3.5 in AFGen, correlation with the SiGN atrial fibrillation GWAS rose to 0.77 (Figure 1 and Supplementary Table 3). These correlations, calculated to include even modestly-associated SNPs, indicate that atrial fibrillation in AFGen and atrial fibrillation in stroke (SiGN) share a large proportion of genetic risk factors. Removing ±2Mb around the PITX2 and ZFHX3 loci only modestly impacted the correlation between AFGen and atrial fibrillation in SiGN (r = 0.63 for SNPs with |z| > 3.5; Supplementary Figure 2 and Supplementary Table 3). Correlations between AFGen and cardioembolic stroke in SiGN were unsurprisingly highly similar to that of the results with atrial fibrillation in SiGN (r = 0.77 for AFGen SNPs with |z| > 3.5), likely due to the high concordance between the atrial fibrillation and cardioembolic stroke phenotypes (Figure 1 and Supplementary Figure 3).
Continuing this analysis across the other stroke subtypes (large artery atherosclerosis, small artery occlusion, and undetermined stroke; Figure 1), we found near-zero correlation between AFGen and either large artery atherosclerosis or small artery occlusion (Figure 1) indicating no genetic overlap between the phenotypes. However, the correlation between atrial fibrillation and the undetermined stroke subtypes (a highly heterogeneous subset of cases21,22 that cannot be classified with standard subtyping systems13,15) increased steadily as we partitioned the AFGen data by z-score (all undetermined vs. AFGen r = 0.04 for AFGen SNPs with |z| > 1 and r = 0.16 for AFGen SNPs with |z| > 3.5; Figure 1 and Supplementary Table 3), indicating that genome-wide, there is residual genetic correlation between atrial fibrillation and the undetermined stroke categories, some of which could represent causal atrial fibrillation stroke mechanisms in that subgroup. As an additional null comparator, we performed correlations between the AFGen results with z-scores derived from the latest GWAS of educational attainment17 and found that correlation remained at approximately zero regardless of the z-score threshold used (Figure 1 and Supplementary Table 3).
To further understand the overlap between genetic risk factors for atrial fibrillation and cardioembolic stroke and to evaluate the degree to which cardioembolic stroke is comprised of risk factors beyond those for atrial fibrillation, we performed a restricted maximum likelihood analysis implemented in BOLT-REML16 to estimate SNP-based heritability of atrial fibrillation and cardioembolic stroke. Using phenotypes derived from the CCS subtyping algorithm23 (Methods), we estimated heritability of atrial fibrillation and cardioembolic stroke at 20.0% and 19.5%, respectively. These estimates are consistent with previous estimates in larger samples (Supplementary Figure 4),24,25 and the similar heritabilities suggest that cardioembolic stroke does not have a substantial heritable component beyond the primary atrial fibrillation risk factor. For comparison, we calculated heritability in the other stroke subtypes15 and found estimates to be similarly modest (range: 15.5% - 23.0%; Supplementary Figures 4-6 and Supplementary Table 4).
Up to this point, our results indicated that atrial fibrillation in ischemic stroke is genetically similar to that discovered in prior genetic studies of atrial fibrillation alone, and that the bulk of the genetic risk for cardioembolic stroke appears attributable to atrial fibrillation genetic risk factors. Next, we sought to explicitly test what proportion of cardioembolic stroke risk could be explained by atrial fibrillation loci, independent of known clinical risk factors for atrial fibrillation. First, we identified SNPs from an atrial fibrillation polygenic risk score (PRS) independently derived from the AFGen GWAS5 (Methods). Of the 1,168 SNPs used to generate this pre-established PRS, we identified 934 in the SiGN dataset with imputation quality > 0.8 and minor allele frequency >1%. We computed the PRS per individual (Methods), weighting the imputed dosage of each risk allele by the effect of the SNP (i.e., the beta coefficient) as reported in AFGen5.
We tested the association of the atrial fibrillation PRS with cardioembolic stroke, using a logistic regression and adjusting for the top ten principal components and sex (Methods). As expected from our earlier results, we found the PRS to be strongly associated with cardioembolic stroke (odds ratio (OR) per 1 standard deviation (sd) of the PRS = 1.93 [95% confidence interval (CI): 1.34 - 1.44], p = 1.01 × 10−65; Figure 2 and Supplementary Table 5), confirming the high genetic concordance of these phenotypes across SNPs which, individually, confer only a modest average association with atrial fibrillation. Next, we adjusted the association model for clinical covariates associated with atrial fibrillation including age, diabetes mellitus, cardiovascular disease, smoking, and hypertension.26 Using a (smaller) set of cases and controls with complete clinical risk factor information, we found that inclusion of these clinical risk factors in the model only modestly reduced the PRS signal in cardioembolic stroke (OR per 1 sd = 1.40 [95% CI: 1.34 - 1.47], p = 1.45 × 10−48; Supplementary Tables 5-7). These results indicate a strong relationship between atrial fibrillation genetic risk factors and cardioembolic stroke risk, independent of the clinical factors that associate with atrial fibrillation. Expanding the set of SNPs used to construct the PRS to the original 934 SNPs ±25kb, ±50kb, and ±100kb (Methods) revealed a persistently strong, though somewhat attenuated, association between the PRS and cardioembolic stroke (PRS including SNPs within 100kb, p = 4.47 × 10−44, Supplementary Table 6). None of the other stroke subtypes were significantly associated with the atrial fibrillation PRS (all p > 0.013, Figure 2 and Supplementary Figure 6).
Because atrial fibrillation status was missing for most controls in the SiGN dataset, we performed sensitivity analyses using only the 3,861 controls confirmed as having no atrial fibrillation. While reducing the set of controls to this refined group did not substantially change results for the primary stroke subtypes, we found the atrial fibrillation PRS was modestly associated (p < 5 × 10−3, after adjusting for five subtypes and two control groups) with the overall undetermined subtype (OR per 1 sd = 1.07 [95% CI: 1.02 - 1.13], p = 4.15 × 10−3) (Figure 2 and Supplementary Table 5). Further examination of the two mutually exclusive subgroups of the undetermined group revealed that the PRS associated significantly with the incomplete/unclassified categorization (OR per 1 sd = 1.09 [95% CI: 1.03 - 1.16], p = 3.17 × 10−3) (Figure 2) but not with cryptogenic/cardioembolic minor (OR per 1 sd = 1.06 [95% CI: 1.00 - 1.13], p = 5.10 × 10−2). Correcting for clinical covariates only modestly changed the signal in the incomplete/unclassified phenotype (p = 9.7 × 10−3, Figure 2), supporting the robustness of the observed association, independent of clinical risk factors.
Lastly, we created a model in BOLT-LMM, fitting two genetic variance components: one component including SNPs for the genetic relationship matrix, and the second component including the original PRS SNPs from the atrial fibrillation PRS (including ±100kb around these SNPs, to include a sufficient number of markers to estimate variance explained). We found that the SNPs from the atrial fibrillation PRS explained 4.1% of the total (20.0%) heritability in atrial fibrillation. In evaluating variance explained in cardioembolic stroke, we found a nearly identical result: the component representing the atrial fibrillation risk score explained 4.5% (s.e. = 1.00%) of the total 19.5% genetic heritability in cardioembolic stroke. Thus, atrial fibrillation genetic risk accounts for 23.1%, or approximately one-fifth, of the total heritability of cardioembolic stroke.
Discussion
Our results suggest that individuals with cardioembolic strokes have an enrichment for atrial fibrillation genetic risk, despite the fact that cardioembolic stroke often affects older adults with multiple clinical comorbidities27 that could increase risk for atrial fibrillation due to non-genetic factors. The fact that cardioembolic stroke and atrial fibrillation share a highly-similar genetic architecture extends our understanding of the morbid consequences of heritable forms of the arrhythmia. Furthermore, the observation that atrial fibrillation genetic risk was only associated with cardioembolic stroke, and (consistently) lacked association in large artery atherosclerosis or small artery occlusion,28 raises the possibility that atrial fibrillation genetic risk may be informative in the management of ischemic stroke survivors in whom the mechanism may be unclear.
The use of polygenic risk scores for complex traits has proved an efficient means of understanding how genetic predisposition to diseases can overlap. Given the onslaught of genotyping data available for common diseases, PRS’s can now be used to stratify patients by risk (e.g., in breast cancer29,30) or predict outcome (e.g., in neuropsychiatric disease29). More recently, PRS’s have been used to identify individuals in the general population with a four-fold risk for coronary disease,31 proposed for inclusion in clinical workups of individuals with early-onset coronary artery disease,32 and used to identify patients for whom lifestyle changes or statin intervention would be beneficial.33,34 While previous work has also shown an association between an atrial fibrillation PRS and cardioembolic stroke,28 we have extended this work to formally quantify the extent to which an atrial fibrillation PRS captures genetic risk for cardioembolic stroke. These findings lay the groundwork for future work that can potentially leverage this overlap to develop atrial fibrillation PRS’s that could be used to predict individuals at highest risk of cardioembolic stroke (to improve diagnostic resource allocation) or help distinguish between clinical subtypes of stroke.
Though our analysis was aimed at understanding the genetic overlap between cardioembolicstroke andatrialfibrillation,we additionallyobserved genetic correlation between atrial fibrillation and undetermined stroke, a finding not observed in a previous investigation of atrial fibrillation PRS in ischemic stroke subtypes, albeit in a smaller sample.28 Perhaps contrary to expectation, we specifically found the atrial fibrillation polygenic risk score to be more strongly associated with the subset of etiology-undetermined strokes with an incomplete clinical evaluation, as opposed to those with cryptogenic stroke of a presumed, but not demonstrated, embolic source. These associations could be due to physician biases in diagnostic workups, rather than supporting a low prevalence of occult atrial fibrillation in presumed embolic strokes of undetermined source. Identifying stroke patients with atrial fibrillation is an important clinical challenge, as occult atrial fibrillation is well-known to cause strokes,35,36 and since such patients are at high risk for recurrent stroke, which is preventable with anticoagulation.37,38 Together, our findings indicate that atrial fibrillation genetic risk may augment clinical algorithms to determine stroke etiology, but will require further study.
The work presented here benefits from a number of improvements, including increased sample size; analysis of samples from a multicenter consortium, potentially enhancing the generalizability of the findings; and use of the CCS subtyping system, which provides more nuanced phenotyping, particularly in the cryptogenic subtype. Nevertheless, some limitations remain. Stroke is a heterogeneous condition that occurs later in life and has high lifetime prevalence (>15%39), features that can reduce statistical power. Further, sample sizes have lagged behind other GWAS efforts, a challenge further compounded by subtyping (nearly one-third of all cases are categorized as undetermined23). Reduced sample sizes impact power for discovery and make other analytic approaches – such as standard approaches for measuring trait correlation16 – unfeasible. Also, our sample is primarily comprised of Euroepan-ancestry samples, and work in non-Europeans, particularly in Africanancestry samples where risk of stroke is double that of European samples, is crucial. Finally, the current analysis does not analyze rare variation, which also likely contributes to disease susceptibility.5
We have shown that the cumulative genetic risk for atrial fibrillation in individuals with a stroke is similar to that reported in a larger population-based cohort.25 Genome-wide variation related to atrial fibrillation is substantially associated with cardioembolic stroke risk. Moreover, atrial fibrillation genetic risk was specific for cardioembolic stroke, and was not associated with the other primary stroke subtypes. The observation that atrial fibrillation genetic risk associated with strokes of undetermined cause supports the notion that undetected atrial fibrillation underlies a proportion of stroke risk in these individuals. Further work will need to incorporate emerging discoveries of rare genetic variants in atrial fibrillation, and explore the potential for genetic risk tools, including PRS’s performed via clinical-grade genotyping, to assist in the diagnostic workup of individuals with ischemic stroke.
Shared Genetic Contributions to Atrial Fibrillation and Ischemic Stroke Risk
Code and data release
For access to information related to this project, including code, sample identifiers, SNP identifiers, links to summary-level data, and SNP weights used in the construction of the polygenic risk score, please see this GitHub repository: https://github.com/UMCUGenetics/Afib-Stroke-Overlap.
Supplementary Results
Including age as a covariate in the GWAS of atrial fibrillation
To check for the effects of age on our initial GWAS findings, we ran a GWAS of atrial fibrillation including age as a covariate. Controls without age information were dropped from this analysis. Given the structure of the SiGN dataset -- which includes groups of cases and controls that have been carefully matched on genotyping array and ancestry -- we also dropped the cases for which their matched controls were missing age information.
Our age-adjusted analysis included 2,487 atrial fibrillation cases and 22,072 controls. We performed the GWAS in BOLT-LMM, adjusting for 10 PCs, sex and age. We then checked the correlation between the SNP effects (betas) from the GWAS unadjusted for age and the SNP effects from the GWAS adjusted for age. Correlation was strong (r = 0.83).
Appendix I
Members of the Atrial Fibrillation Genetics (AFGen) Consortium
Please note that the AFGen Consortium participants evolve over time. Further information on the AFGen Consortium can be found at www.afgen.org.
Appendix II
Members of the International Stroke Genetics Consortium (ISGC)
Please note that ISGC participants evolve over time. Further information on the ISGC can be found at http://www.strokegenetics.org/.
Acknowledgements
This work uses data from the National Institute of Neurological Disorders and Stroke - Stroke Genetics Network (NINDS-SiGN) Consortium and the Atrial Fibrillation Genetics (AFGen) Consortium. Members of NINDS-SiGN, the International Stroke Genetics Consortium, and AFGen are provided as an appendix to this paper.
Drs. Pulit, Mitchell, McArdle, and Kittner are supported by NIH grant R01NS100178. The NINDS-SiGN Consortium is supported by the NIH grants R01NS100178 and U01NS069208.
Dr. Pulit is supported by Veni Fellowship 016.186.071 (ZonMW) from the Dutch Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO).
Dr. Anderson is supported in part by K23NS086873, R01NS103924, an American Heart Association Strategically Focused Research Network in Atrial Fibrillation Award, and a Massachusetts General Hospital Center for Genomic Medicine Catalyst Award.
Dr. Lubitz is supported by NIH grants K23HL114724, an American Heart Association Strategically Focused Research Network in Atrial Fibrillation Award, and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105.
Dr. Weng is supported by an American Heart Association Postdoctoral Fellowship Award 17POST33660226.
Drs. Ellinor and Benjamin are supported by the NIH grants R01HL092577 and R01HL128914, and an American Heart Association Strategically Focused Research Network in Atrial Fibrillation Award. Dr. Benjamin is additionally supported by the NIH grants 1RC1HL101056 and 1R01HL102214. Dr. Ellinor is additionally supported by the NIH grants R01HL104156 and K24HL105780; the National Heart, Lung, and Blood Institute (NHLBI); American Heart Association Established Investigator Award 13EIA14220013; and the Fondation Leducq 14CVD01.
Footnotes
Disclosures/Competing Financial Interests Dr. Pulit reports no disclosures. Dr. Weng reports no disclosures. Dr. McArdle reports no disclosures. Dr. Trinquart reports no disclosures. Dr. Choi reports no disclosures. Dr. Mitchell reports no disclosures. Dr. Rosand reports no disclosures. Dr. de Bakker reports no disclosures. Dr. Benjamin reports no disclosures. Dr. Ellinor is a principal investigator on a Bayer HealthCare grant to the Broad Institute related to the genetics and development of new therapeutics for cardiovascular diseases. Dr. Kittner reports no disclosures. Dr. Lubitz receives sponsored research support from Boehringer Ingelheim, Bristol Myers Squibb, Bayer HealthCare, Biotronik, and has consulted for St. Jude Medical / Abbott and Quest Diagnostics. Dr. Anderson has consulted for ApoPharma.
Author Contributions
All named authors have contributed meaningfully to the present study. Specific contributions for each author are described below.
S.L. Pulit: conception of research design, data analysis, drafting of manuscript, critical revision of manuscript
L.C. Weng: data analysis, critical revision of manuscript
P.F. McArdle: data acquisition, data analysis, critical revision of manuscript
L. Trinquart: data acquisition, critical revision of manuscript
S.H. Choi: data acquisition, critical revision of manuscript
B.D. Mitchell: data acquisition, study supervision, critical revision of manuscript
J. Rosand: data acquisition, study supervision, critical revision of manuscript
P.I.W. de Bakker: study supervision, critical revision of manuscript
E.J. Benjamin: data acquisition, study supervision, critical revision of manuscript
P.T. Ellinor: data acquisition, study supervision, critical revision of manuscript
S.J. Kittner: data acquisition, study supervision, critical revision of manuscript
S.A. Lubitz: conception of research design, study supervision, drafting of manuscript, critical revision of manuscript
C.D. Anderson: conception of research design, study supervision, drafting of manuscript, critical revision of manuscript