Author + information
- Received August 8, 2013
- Revision received November 10, 2013
- Accepted December 3, 2013
- Published online April 1, 2014.
- Steven A. Lubitz, MD, MPH1,2,∗ (, )
- Kathryn L. Lunetta, PhD3,4,
- Honghuang Lin, PhD3,5,
- Dan E. Arking, PhD6,
- Stella Trompet, PhD7,8,
- Guo Li, MS9,
- Bouwe P. Krijthe, MSc10,11,
- Daniel I. Chasman, PhD12,13,
- John Barnard, PhD14,
- Marcus E. Kleber, PhD15,
- Marcus Dörr, MD16,17,
- Kouichi Ozaki, PhD18,
- Albert V. Smith, PhD19,
- Martina Müller-Nurasyid, MSc, PhD20,21,22,
- Stefan Walter, PhD23,
- Sunil K. Agarwal, MD, PhD24,
- Joshua C. Bis, PhD9,
- Jennifer A. Brody, BA9,
- Lin Y. Chen, MD, MS25,
- Brendan M. Everett, MD, MPH12,26,
- Ian Ford, PhD27,
- Oscar H. Franco, MD, PhD10,11,
- Tamara B. Harris, MD28,
- Albert Hofman, MD, PhD10,11,
- Stefan Kääb, MD, PhD20,29,
- Saagar Mahida, MB, ChB30,
- Sekar Kathiresan, MD, MPH31,
- Michiaki Kubo, MD, PhD32,
- Lenore J. Launer, PhD28,
- Peter W. Macfarlane, DSc33,
- Jared W. Magnani, MD, MSc3,34,
- Barbara McKnight, PhD35,
- David D. McManus, MD, ScM36,
- Annette Peters, PhD, MPH29,37,
- Bruce M. Psaty, MD, PhD9,38,39,40,
- Lynda M. Rose, MSc41,
- Jerome I. Rotter, MD42,
- Guenther Silbernagel, MD43,
- Jonathan D. Smith, PhD44,
- Nona Sotoodehnia, MD, MPH9,45,
- David J. Stott, MD46,
- Kent D. Taylor, PhD47,
- Andreas Tomaschitz, MD48,
- Tatsuhiko Tsunoda, PhD49,
- Andre G. Uitterlinden, PhD10,11,50,
- David R. Van Wagoner, PhD51,
- Uwe Völker, PhD17,52,
- Henry Völzke, MD17,53,
- Joanne M. Murabito, MD, ScM3,54,
- Moritz F. Sinner, MD, MPH20,
- Vilmundur Gudnason, MD, PhD19,
- Stephan B. Felix, MD16,17,
- Winfried März, MD15,55,56,
- Mina Chung, MD51,57,
- Christine M. Albert, MD, MPH12,13,26,
- Bruno H. Stricker, MB, PhD10,11,50,58,
- Toshihiro Tanaka, MD, PhD18,59,
- Susan R. Heckbert, MD, PhD9,39,40,
- J. Wouter Jukema, MD, PhD60,
- Alvaro Alonso, MD, PhD61,
- Emelia J. Benjamin, MD, ScM3,34,62,63 and
- Patrick T. Ellinor, MD, PhD1,2
- 1Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts
- 2Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- 3Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- 4Department of Biostatistics, School of Public Health, Boston University, Boston, Massachusetts
- 5Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- 6McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- 7Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- 8Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- 9Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
- 10Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- 11Netherlands Consortium on Healthy Aging (NCHA), Leiden, the Netherlands
- 12Harvard Medical School, Boston, Massachusetts
- 13Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- 14Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
- 15Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
- 16Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- 17DZHK (German Centre for Cardiovascular Research), Partner site Greifswald Greifswald, Germany
- 18Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- 19Icelandic Heart Association, Kopavogur Iceland and University of Iceland, Reykjavik, Iceland
- 20Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany
- 21Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- 22Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians University, Munich, Germany
- 23Department of Society, Human Development & Health, Harvard School of Public Health, Boston, Massachusetts
- 24Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- 25Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
- 26Division of Cardiology, Brigham and Women’s Hospital, Boston, Massachusetts
- 27Robertson Center for Biostatistics, University of Glasgow, Glasgow, Scotland
- 28Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
- 29Deutsches Forschungszentrum für Herz-Kreislauferkrankungen (DZHK), Partner site Munich Heart Alliance, Munich, Germany
- 30Leeds General Infirmary, Leeds, England
- 31Center for Human Genetic Research and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- 32Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- 33Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, Scotland
- 34Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- 35Department of Biostatistics, University of Washington, Seattle, Washington
- 36Departments of Medicine and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
- 37Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- 38Department of Health Services, University of Washington, Seattle, Washington
- 39Department of Epidemiology, University of Washington, Seattle, Washington
- 40Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- 41Division of Preventive Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts
- 42Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California
- 43Department of Angiology, Swiss Cardiovascular Centre, Bern University Hospital, Bern, Switzerland
- 44Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
- 45Division of Cardiology, University of Washington, Seattle, Washington
- 46Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, Scotland
- 47Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- 48Department of Cardiology, Medical University of Graz, Graz, Austria
- 49Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- 50Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
- 51Department of Molecular Cardiology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
- 52Interfaculty Institute for Genetics and Functional Genomics, Ernst Moritz Arndt University Greifswald, Greifswald, Germany
- 53Institute for Community Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany
- 54Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- 55Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- 56Synlab Academy, Synlab Services GmbH, Mannheim, Germany
- 57Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio
- 58Inspectorate for Health Care, the Hague, the Netherlands
- 59Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- 60Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands, and Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands
- 61Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
- 62Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts
- 63Preventive Medicine Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- ↵∗Reprint requests and correspondence:
Dr. Steven A. Lubitz, Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, Massachusetts 02114.
Objectives This study sought to identify nonredundant atrial fibrillation (AF) genetic susceptibility signals and examine their cumulative relations with AF risk.
Background AF-associated loci span broad genomic regions that may contain multiple susceptibility signals. Whether multiple signals exist at AF loci has not been systematically explored.
Methods We performed association testing conditioned on the most significant, independently associated genetic markers at 9 established AF loci using 2 complementary techniques in 64,683 individuals of European ancestry (3,869 incident and 3,302 prevalent AF cases). Genetic risk scores were created and tested for association with AF in Europeans and an independent sample of 11,309 individuals of Japanese ancestry (7,916 prevalent AF cases).
Results We observed at least 4 distinct AF susceptibility signals on chromosome 4q25 upstream of PITX2, but not at the remaining 8 AF loci. A multilocus score comprised 12 genetic markers demonstrated an estimated 5-fold gradient in AF risk. We observed a similar spectrum of risk associated with these markers in Japanese. Regions containing AF signals on chromosome 4q25 displayed a greater degree of evolutionary conservation than the remainder of the locus, suggesting that they may tag regulatory elements.
Conclusions The chromosome 4q25 AF locus is architecturally complex and harbors at least 4 AF susceptibility signals in individuals of European ancestry. Similar polygenic AF susceptibility exists between Europeans and Japanese. Future work is necessary to identify causal variants, determine mechanisms by which associated loci predispose to AF, and explore whether AF susceptibility signals classify individuals at risk for AF and related morbidity.
Atrial fibrillation (AF) is a heritable (1–6) and morbid (7) arrhythmia. Genome-wide association studies have identified 9 susceptibility regions on 8 chromosomes that implicate genes encoding transcription factors involved in cardiopulmonary development, cardiac expressed ion channels, and other signaling molecules in the pathogenesis of AF (8–12).
Genetic variants associated with AF at previously reported loci extend over broad genomic distances, often spanning tens or hundreds of thousands of bases. The large span of associated variants at some AF loci raises the possibility that the loci may contain multiple independent, or at least nonredundant, susceptibility signals. A refined understanding of the architecture of association signals at the top loci may identify additional novel susceptibility signals, help characterize functional elements involved in the pathogenesis of AF, and enable stratification of individuals according to genetic risk for arrhythmia.
We sought to determine whether additional AF susceptibility signals exist within loci previously identified in genome-wide association studies of AF among participants of European ancestry within the AFGen Consortium (12). We then sought to determine whether our observations regarding AF-associated genetic variants are generalizable in an independent sample of Japanese ancestry.
We included subjects of European ancestry from 8 prospective cohort and 12 case-control study samples derived from the AGES (Age, Gene/Environment Susceptibility) Reykjavik Study, ARIC (Atherosclerosis Risk in Communities), CCAF (Cleveland Clinic Lone AF GeneBank Study), CHS (Cardiovascular Health Study) study, FHS (Framingham Heart Study), AFNET/KORA (German Competence Network for Atrial Fibrillation/Cooperative Research in the Region of Augsburg) study, LURIC (Ludwigshafen Risk and Cardiovascular Health Study), MGH (Massachusetts General Hospital Atrial Fibrillation Study), HVH (Heart and Vascular Health Study), PHASE/PROSPER (PHArmacogenetic study of Statins in the Elderly at risk/PROspective Study of Pravastatin in the Elderly at Risk for vascular disease) study, RS-I and RS-II (Rotterdam Study I and II) studies, SHIP (Study of Health in Pomerania) study, and WGHS (Women’s Genome Health Study). Validation of our findings was performed in the BioBank Japan case-control sample. Brief summaries of each study are provided in the Appendix. The Institutional Review Boards at each of the respective studies approved all of the studies. Study participants provided written informed consent to participate in genetic research.
In each study, AF was ascertained from electrocardiograms, Holter recordings, medical records, or hospital discharge diagnostic codes (ICD-8 427.92; ICD-9-CM 427.3, 427.31, or 427.32; or ICD-10 I48 in any position), as previously described (13–19). AF was considered prevalent if ascertained in case-control studies or if it was present at or prior to DNA collection in cohort studies. Incident AF was defined if it occurred after DNA collection in participants without a history of AF. In the ARIC study, the baseline examination was used rather than DNA collection for these definitions and follow-up.
Genome-wide genotyping for array-specific single nucleotide polymorphisms (SNPs) was conducted in each study as previously described (12). Imputation was performed for up to 2.2 million autosomal SNPs on the basis of the HapMap CEU panel (20). For the AFNET/KORA study, the SNP rs12235316_G was substituted for rs10821415_A (distance ∼40 kilobases [kb], r2 = 1) on chromosome 9q22 as this SNP was unavailable on the basis of the study’s imputation. Details regarding genotyping platforms, quality control metrics, and imputation methods are provided in Online Table 1.
We defined AF loci a priori as the genomic region centered on the most significantly associated SNP from a prior meta-analysis (12) and flanked by 1 megabase (Mb) on either side. To determine whether multiple associated signals for AF exist beyond the top associated variant at each AF associated locus, we employed 2 different conditional analysis approaches.
First, we performed a traditional conditional analysis, in which we iteratively repeated association testing within each genome-wide significant locus with adjustment for the most significantly associated genome-wide association signal (p < 5 × 10–8) remaining at the locus in each cohort, until no further genome-wide significant SNPs remained. Because we previously identified a total of 3 distinct susceptibility signals at the chromosome 4q25 locus, we adjusted for the genotypes of SNPs tagging these signals (rs6817105, a perfect proxy for a previously reported signal rs2200733; rs17570669; and rs3853445) when performing association testing on chromosome 4q25 (21). At all other loci, we began the iterative association testing process by adjusting for the single most significantly associated SNP at the locus. Study-specific effect estimates were combined via meta-analyses as described subsequently.
Second, as an alternative method of discovery, we employed an approximate conditional analysis to estimate nonredundant signals directly from the summary statistics of a prior genome-wide meta-analysis (12) using the GCTA software package (22). Linkage disequilibrium and allele frequencies were estimated from 2,058 unrelated individuals from the FHS study. Potential nonredundant signals identified were then tested for association with AF in each study cohort, and the study-specific effect estimates were combined by meta-analyses.
For each approach we examined study-specific associations between SNPs and AF using logistic regression for prevalent AF, and proportional hazards regression for incident AF. In the FHS study, we used generalized estimating equations with an independence working correlation structure in a logistic model for prevalent AF, as implemented in the geepack package in R (23) and robust variance estimators (clustering on family) in a Cox model for incident AF as implemented in the survival package in R (24) to account for potential relatedness among participants. All models were fitted assuming additive genetic effects for each SNP (i.e., multiplicative relative risks). Age at DNA collection (or baseline for the ARIC study), sex, and principal components of ancestry significantly associated with AF were included in the models.
For all analyses, study-specific regression estimates were meta-analyzed using an inverse variance weighted method. Prevalent and incident AF were meta-analyzed together as previously performed (10,12). We considered a 2-sided p < 5 × 10–8 to provide significant evidence for independent associations between SNPs and AF. We considered a 2-sided p ≤ 1 × 10–7 to provide suggestive evidence of association. We calculated linkage disequilibrium metrics (r2, D’) for all AF associated variants in the same region.
Because the 2 conditional analysis approaches yielded similar results (see subsequent results), when different SNPs identified from the 2 different approaches at a given locus were in linkage disequilibrium with one another, we selected the SNP with the smaller p value for further modeling. We then fit multi-SNP models that included each of the selected nonredundant SNPs to examine adjusted SNP associations with AF.
We constructed both unweighted and weighted multimarker genetic risk scores with the selected independent AF susceptibility signals by summing the dosages of AF risk SNPs. In weighted scores, we multiplied the allele dosages for each individual by SNP-specific regression coefficient estimates derived from either the conditional analysis (for SNPs on chromosome 4q25) or a prior meta-analysis (for the remaining SNPs) (12). Unweighted scores were the sum of dosages for all SNPs included in the score.
We hypothesized that SNPs contribute additively to AF risk and therefore tested the associations between an aggregate multimarker panel of risk alleles and AF. We constructed multimarker scores for the chromosome 4q25 locus alone as well as across all AF-associated loci. We included a total of 4 nonredundant SNPs from chromosome 4q25 and 8 from the remaining loci (see subsequent results); therefore, the total number of AF risk alleles ranged from 0 to 8 at the chromosome 4q25 locus and from 0 to 24 across all loci. We created 8 categories for multimarker scores at the chromosome 4q25 locus, reflecting each of the estimated number of risk alleles. For scores across all loci, we created 12 potential categories, each reflecting increments of 2 estimated AF risk alleles, in order to avoid rare and inestimable categories of risk alleles that might occur with single risk allele increments. For weighted multimarker risk scores, 10 categories were selected on the basis of cutoff values used to derive score deciles in the MGH sample (Online Table 2).
In order to determine whether observed associations were generalizable beyond individuals of European ancestry, we performed association testing in the independent BioBank Japan sample using the same statistical methodology described above.
We examined whether nonredundant signals at the chromosome 4q25 locus were more likely to be evolutionarily conserved than the rest of the locus by comparing sequence alignments in 44 vertebrate species. We compared the average phylogenetic conservation scores between 10 kb regions centered on each of the nonredundant signals on chromosome 4q25 to that of the rest of the 1 Mb locus using the Student t test (see Online Appendix for details).
In order to examine the relations between identified genetic variants and prognosis, we assessed the relations between nonredundant AF-associated SNPs and both survival and survival free of major disease or mortality on the basis of a prior genome-wide association study of aging in individuals of European ancestry (25). Briefly, the analysis was performed by examining associations between SNPs and time to incident disease or death in 25,007 individuals older than 55 years of age. Associations were modeled using proportional hazards regression with time since DNA collection as the time scale, with adjustment for age at DNA collection and sex. Survival free of major disease or mortality was modeled by using time to death or to the first myocardial infarction, heart failure, stroke, dementia, hip fracture, or cancer diagnosis. Participants with any of the modeled outcomes at baseline were excluded from the analysis.
Characteristics of participants in the included studies are provided in Table 1. Overall, the analysis included a total of 64,683 individuals of European ancestry, including 3,302 individuals with prevalent AF and 3,869 individuals with incident AF.
We observed evidence for multiple genome-wide significant AF susceptibility signals on chromosome 4q25, but no independent signals beyond the first at the remaining 8 AF-associated loci (Fig. 1). With the traditional conditional analysis approach, we identified 2 potential signals associated with AF (rs2723288 and rs4400058) with p < 5 × 10–8 after simultaneous adjustment for previously reported signals (rs6817105, rs17570669, and rs3853445). After including rs2723288, rs6817105, rs4400058, rs17570669, and rs3853445 in a model, rs6817105 (p = 1.1 × 10–85) and rs4400058 (p = 2.2 × 10–16) remained significantly associated with AF (Table 2). One of the previously reported (21) signals (rs3853445, p = 1.0 × 10–7) remained suggestive of association whereas another was not associated with AF at the pre-specified genome-wide significance threshold (rs17570669, p = 5.2 × 10–3). Notably, rs17570669 had a low imputation quality score in 2 studies (the HVH and CHS studies; R2 = 0.12 and 0.18, respectively), and had imputation quality less than 0.8 in several others (the SHIP, RS-I, AGES, CCAF, WGHS, AFNET, and FHS studies), which may have affected the association signal. Nevertheless, we did not include it in subsequent multimarker analyses given the lack of genome-wide significant association that was observed in the adjusted association analyses.
In the approximate conditional analysis approach, we identified 4 potential signals at the chromosome 4q25 locus, tagged by SNPs rs1448818, rs6817105, rs4032974, and rs6838973 (Table 2). In models in which we adjusted for all 4 potential SNPs, rs1448818 (p = 1.6 × 10–8), rs6817105 (p = 5.1 × 10–95), and rs6838973 (p = 6.0 × 10–9) remained significantly associated with AF. Results from the traditional conditional analysis and approximate conditional analysis were similar to one another in that the significantly associated signals were in linkage disequilibrium with one another (Table 2, Online Table 3).
When different SNPs identified from the 2 different approaches at a given locus were in linkage disequilibrium with one another, we selected the SNP with the smallest p value from either approach for further modeling (rs1448818, rs6817105, rs4400058, rs6838973). We examined adjusted SNP associations with AF by meta-analyzing results from cohort-specific models that included all of the selected nonredundant SNPs on chromosome 4q25 alone, as well as across all loci (Table 3). We observed persistent genome-wide association between the selected nonredundant chromosome 4q25 SNPs and AF, but attenuation of associations at some of the other loci that was most pronounced for the chromosome 7q31 locus (rs3807989, p = 6.3 × 10–3).
The identified signals on chromosome 4q25 span a 195 kb intergenic region (Fig. 2, Online Fig. 1). The newly identified signal tagged by rs1448818 is 135 kb centromeric of the top signal at the locus, and is located 7 kb upstream of the transcription factor PITX2. The signal tagged by rs4400058 is 11 kb telomeric of the top signal. Overall, the 10 kb genomic regions flanking each nonredundant SNP identified in our analysis were associated with a greater degree of phylogenetic conservation than nucleotides at the remainder of the 1 Mb locus on chromosome 4q25 (average conservation score 0.29 ± 1.16 vs. 0.19 ± 1.03, p < 0.001) (Online Table 4).
We then constructed multimarker genetic risk scores comprising the genome-wide significant, nonredundant SNPs at chromosome 4q25 listed in Table 3 and across all AF loci to determine the composite associations between AF risk alleles and AF. Both unweighted and weighted risk scores were significantly associated with AF (Table 4).
We observed a graded risk of AF that correlated with the number of inherited AF risk alleles (Fig. 3, Online Fig. 2). The most commonly observed number of AF risk alleles across the 12 nonredundant SNPs (9,10) was observed in 25% of our sample. We observed 22% of individuals in our sample that had greater than 10 estimated AF risk alleles and that carried an increased age- and sex-adjusted risk for AF, and 42% that had fewer than 9 AF risk alleles and that carried a reduced risk for AF. By comparing the estimated relative risks between those carrying the greatest and lowest numbers of inherited AF risk alleles, we observed an estimated 4-fold difference in AF risk captured by SNPs at the chromosome 4q25 locus, and 5-fold when considering all independent loci.
We further sought to determine whether the observed associations were generalizable beyond individuals of European ancestry by examining AF-associated genetic variants in 11,309 independent individuals of Japanese ancestry from the BioBank Japan sample, 7,916 of whom had AF. We observed that AF risk alleles for the most-significant SNPs in Europeans at 4 of the 9 loci associated with AF were similarly associated with AF beyond genome-wide significance thresholds in Japanese after adjustment for one another (Table 3). Overall, effect estimates were in the same direction for 9 of the 12 tested variants in both European and Japanese samples. Results were not substantially changed in a subset in which adjustment for principal components of ancestry was possible (Online Table 5). We further observed that multimarker genetic scores were significantly associated with AF similar to those observed in individuals of European ancestry (Table 4, Fig. 3, Online Fig. 2). Among Japanese individuals, we observed an approximately 5-fold gradient of AF risk when considering the nonredundant markers on chromosome 4q25 identified in Europeans, and 4-fold when considering all loci (Fig. 3).
We also examined the associations between each independent AF-associated SNP with survival (n = 25,007, events = 8,444) and survival free of major disease or mortality (n = 16,995, events = 7,314) from prior genome-wide association studies of aging (sample characteristics provided in Online Table 6) (25). We did not observe any significant associations between each of the AF susceptibility SNPs and either survival or survival free of major disease after adjustment for multiple hypothesis testing with 12 SNPs (Online Table 7). The most significant association was with SNP rs3903239 at the PRRX1 locus on chromosome 1q24 (relative risk for G [AF risk] allele: 1.04, 95% confidence interval: 1.01 to 1.08, p = 6.6 × 10–3 for survival; and relative risk: 1.04, 95% confidence interval: 1.01 to 1.08, p = 0.02) for survival free of major disease.
We employed 2 complementary methods to systematically search for multiple AF susceptibility signals at 9 genome-wide associated loci in a total of 64,683 individuals of European ancestry, in which 7,171 individuals with AF were included. Our findings demonstrate the presence of at least 4 distinct AF susceptibility signals in a large intergenic region on chromosome 4q25. A multiallelic risk score comprising 12 AF susceptibility signals contributed to an estimated 5-fold age- and sex- adjusted gradient of AF risk. Whereas about 22% of individuals had increased age- and sex-adjusted risk of AF relative to those with the most common number of AF risk alleles, about 42% of individuals had decreased AF risk on the basis of the number of AF risk alleles that they carried. In an independent Japanese sample, we observed nearly identical findings, suggesting that the AF-genetic risk markers identified in our analysis may be generalizable beyond populations of European ancestry.
We did not observe evidence for multiple genome-wide significant susceptibility signals at AF loci other than chromosome 4q25. Our observations extend previous reports about the relations between genetic markers and AF risk, and underscore the complex nature of the AF susceptibility locus on chromosome 4q25. Prior analyses have reported independent markers on chromosome 4q25 related to AF (8,21), or post-operative AF (26). Indeed, SNP rs4400058 is in perfect linkage with a previously reported SNP rs10033464 at chromosome 4q25 in a genome-wide association study of Icelanders (8). However, rs10033464 has not been consistently associated with AF in other analyses of individuals of European ancestry (21,26,27).
In the present analysis we observed a gradient of risk that correlated with the number of AF risk alleles present. We have observed that AF risk may vary substantially between individuals with the same number of risk alleles in a subset of the present AFGen sample (21), possibly due to variable effects of particular SNPs, nonlinear interactions between specific risk alleles, or differences in other clinical or environmental AF risk factors. Few studies have explored the utility of family history (6) or genotypic information (28,29) to discriminate AF risk. Future work will be necessary to assess the best modeling strategy for incorporating genetic markers into AF risk prediction efforts, and to determine the best clinical setting in which to utilize such tools.
Genetic associations that extend beyond single ancestral groups may facilitate the identification of true biological variation underlying disease (30). Few prior analyses have examined the relations between genetic factors and AF in individuals of Japanese descent. A prior analysis from the AFGen consortium related the top SNPs identified at genome-wide susceptibility loci for AF to those in a sample of 843 individuals with AF and 3,350 without from the BioBank Japan sample, demonstrating marginal associations between the top variants at the PRRX1, PITX2, CAV1, and ZFHX3 loci. In aggregate, current and prior observations provide support for a shared genetic susceptibility to AF in individuals of European and Japanese descent, despite a lower prevalence of AF among individuals of Japanese ancestry (31,32).
Our findings implicate a broad AF susceptibility locus on chromosome 4q25. The 4 susceptibility signals we identified span 195 kb across an intergenic region on chromosome 4q25. The identified variants are upstream of PITX2, a homeodomain transcription factor involved in determining right-left cardiac symmetry, specifying pulmonary venous myocardium, and suppressing formation of a default sinus node in the left atrium (33–35). The expression of the Pitx2c isoform is reduced in left atrial samples from humans with AF as compared with those without a history of AF (36). Knockout of the Pitx2c isoform in mice is associated with increased susceptibility to pacing-induced atrial arrhythmias and shortened atrial refractory periods (37), consistent with electrical re-entry as a predominant mechanism of AF. Our present findings implicate genetic variation within 7 kb of PITX2 in the pathogenesis of AF, nearly 150 kb closer to the gene than the top AF-associated signal at the locus in the AFGen sample.
Our findings also implicate regulatory elements in the pathogenesis of AF. Examination of phylogenetic conservation demonstrates that the identified AF susceptibility signals cluster around conserved noncoding regions at chromosome 4q25. Future work will be necessary to determine the functional role of these loci and the causal elements tagged by the identified AF susceptibility SNPs. The identification of individuals at high and low genetic risk of AF may enhance the power of future sequencing efforts to identify genetic variation at the chromosome 4q25 locus underlying AF.
Our paper extends previous knowledge about the nature of AF susceptibility regions. First, our analysis is the first to systematically assess for multiple susceptibility signals at all genome-wide significant AF susceptibility loci. Second, our results provide the strongest evidence to date that multiple susceptibility signals exist at chromosome 4q25; prior analyses did not rely on stringent genome-wide significance criterion to identify multiple signals at existing susceptibility loci. Third, our analysis is the first to implicate a distinct susceptibility region within 7 kb of PITX2 in the pathogenesis of AF, whereas prior data had not distinguished this region from the peak association signal about 150 kb upstream of the gene. Fourth, our analysis extends previous observations by demonstrating that the additive effects of genetic variants tagging AF susceptibility signals in Europeans, particularly at chromosome 4q25, transcend ancestry and associate similarly with AF risk in Japanese individuals.
Our study must be interpreted in the context of the study design. First, our analysis included only individuals of European and Japanese ancestry, and therefore it is not clear whether our findings are generalizable to individuals of other ancestral backgrounds. Second, we cannot exclude the possibility that our stringent significance threshold excluded other true independent susceptibility signals with heterogeneous associations across cohorts. Indeed, we previously identified a genetic variant on chromosome 4q25 (rs17570669) that was independently associated with AF in prior work, and which was nominally associated with AF in the present analysis, but not at our predefined genome-wide significance threshold. Whether this is due to poor imputation of the SNP genotypes, heterogeneity in the association across study samples, or true absence of association when considering other more significantly associated SNPs is not clear. Third, as with any SNP-based genetic association study, the discovered SNPs are likely proxies for causal functional elements underlying AF rather than the causal variants themselves. Fourth, we assumed that the risk of AF associated with each allele is multiplicative and that the effects for each SNP contribute to AF risk additively. Our analysis does not address the potential for interactions between SNPs, or between SNPs and environmental factors, which may associate with AF risk.
We systematically examined 9 genome-wide significant AF susceptibility loci for additional independent signals. We identified at least 4 distinct signals on chromosome 4q25 upstream of PITX2, which implicate an arrhythmia susceptibility region at this locus that spans about 195 kb. In aggregate, the number of genetic risk markers for AF correlated with a marked gradient of AF risk in both samples of European and Japanese descent, and identified individuals both at increased as well as at decreased risk of AF relative to those with the most common number of risk markers. Our observations underscore the biological complexity of the chromosome 4q25 locus and importance of the region in AF pathogenesis.
For grant and funding information for the studies, please see the Online Appendix. Dr. Chasman has received research grant suppport from AstraZeneca and Amgen. Dr. Everett has received an investigator-initiated research grant from Roche Diagnostics. Dr. Kathiresan has received research grant support from Celera totaling more than $10,000; and serves on scientific advisory boards for American Genomics and Catabasis. Dr. McManus has received grants from Biotronic, Philips Healthcare, and Otsuka Pharmaceuticals. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. Lubitz, Lunetta ,Lin, Arking, Trompet, Li, Krijthe, Chasman, Barnard, Kleber, Dörr, Ozaki, Smith, Müller-Nurasyid, and Walter are joint first authors. Drs. Murabito, Sinner, Gudnason, Felix, März, Chung, Albert, Stricker, Tanaka, Heckbert, Jukema, Alonso, Benjamin, and Ellinor are joint senior authors.
- Abbreviations and Acronyms
- atrial fibrillation
- single nucleotide polymorphism
- Received August 8, 2013.
- Revision received November 10, 2013.
- Accepted December 3, 2013.
- 2014 American College of Cardiology Foundation
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