Sudden cardiac death (SCD) continues to be one of the leading causes of mortality worldwide, with an annual incidence estimated at 250,000–300,000 in the United States and with the vast majority occurring in the setting of coronary disease. We performed a genome-wide association meta-analysis in 1,283 SCD cases and >20,000 control individuals of European ancestry from 5 studies, with follow-up genotyping in up to 3,119 SCD cases and 11,146 controls from 11 European ancestry studies, and identify the BAZ2B locus as associated with SCD (P = 1. 8×10−10). The risk allele, while ancestral, has a frequency of ∼1. 4%, suggesting strong negative selection and increases risk for SCD by 1. 92–fold per allele (95% CI 1. 57–2. 34). We also tested the role of 49 SNPs previously implicated in modulating electrocardiographic traits (QRS, QT, and RR intervals). Consistent with epidemiological studies showing increased risk of SCD with prolonged QRS/QT intervals, the interval-prolonging alleles are in aggregate associated with increased risk for SCD (P = 0. 006).
Despite recent progress in treatment and prevention of coronary heart disease, sudden cardiac death (SCD) remains a major public health problem, with an annual incidence of SCD that ranges from 50 to 100 per 100,000 in the general population [1], [2]. While there has been a great deal of focus on SCD in the setting of Mendelian forms of arrhythmia (e. g. long and short QT syndromes), the vast majority of SCD events occur in the general population, with up to 50% of individuals manifesting SCD as a first sign of disease [3]. An estimated ∼80% of all SCDs are associated with coronary disease, ∼10–15% in the setting of cardiomyopathy and ∼5% occur in persons with myocarditis, coronary anomalies or ion channelopathies (e. g. long QT/Brugada/short QT syndromes) [4]. Despite this clinical heterogeneity, a familial component to SCD risk has been demonstrated even after adjusting for traditional cardiovascular disease risk factors [5], [6], [7], [8], suggesting that genetic factors are likely to play an important role. The critical importance of the genetic contribution for effective prediction and prevention of SCD has been emphasized in a recent consensus document from the US National Heart Lung and Blood Institute [9]. In this study, we perform a meta-analysis of 5 genome-wide association studies (GWAS) with follow-up genotyping in up to 11 additional studies of European ancestry to identify genetic variants that modify susceptibility to community-based SCD. In addition to an unbiased scan of the genome, we also focus on specific SNPs that have been previously associated with electrophysiological traits that, when extreme, are associated with increased risk for SCD (QRS, QT, and RR intervals).
To identify genetic determinants of SCD, genome-wide genotyping and imputation of ∼2. 5 million SNPs was performed in 1,283 SCD cases and ∼20,000 controls drawn from 5 samples of European ancestry: Atherosclerosis Risk in Communities (ARIC), Framingham Heart Study (FHS), FinGesture, Oregon Sudden Unexpected Death Study (Oregon-SUDS), Rotterdam Study (RS) (Table S1). All individual studies showed minimal test statistic inflation after post-imputation quality control (genomic control λ<1. 03) (see Materials and Methods). Meta-analysis was performed using inverse variance weighting, with minimal test statistic inflation observed (λ = 1. 004) and no early departure from the null expectation (Figure S1A), suggesting that overall there was good genotyping quality and minimal population substructure. Two loci exceeded the genome-wide significance threshold of P<5×10−8 (Figure S1B, Table 1). There were 13 independent loci containing at least 1 SNP with P<10−5 (Table S2). We attempted to follow-up all of these loci in additional independent case-control samples from FinGesture and Oregon-SUDS, as well as in seven additional cohorts: Cardiovascular Health Study (CHS), CVPath Institute Sudden Cardiac Death registry (CVPI-SCDr), the Harvard Cohorts (5 combined cohorts, see Materials and Methods). In total, this follow-up genotyping included 1,730 cases and 10,530 controls of European ancestry (Table S3). We were able to design assays and obtain high quality genotype data for 11 SNPs corresponding to 10 of these independent loci (Table 1). Nominally significant replication results consistent with the GWAS were observed for two highly correlated SNPs (r2 = 1 in HapMap CEU samples) in the BAZ2B (bromodomain adjacent zinc finger domain 2B) locus (rs174230, P = 0. 03; rs4665058, P = 0. 01). On the other hand there was no significant evidence of replication for the two loci that were of genome-wide significance in the initial scan (rs2650907 and rs12601622 located on chromosomes 16 and 17, respectively), suggesting that they represent either false positive signals in the initial scan or insufficient power of the replication cohort. In this context, it is important to note that the imputation quality of rs12601622 was poor across all studies (imputation quality (r2) <0. 4). When combining follow-up results with the discovery GWAS results, only rs4665058 approached genome-wide significance (P = 5. 8×10−8). This marker was therefore further genotyped in the ARREST study, which includes 719 SCD cases, and in 4,190 population-based controls, and in the recently published AGNES case-control study [10], which is a study of 670 individuals with first MI and ventricular fibrillation (VF) compared to 654 with MI alone. In the combined follow-up cohorts, including the ARREST and AGNES studies, this SNP was significant after Bonferroni correction for the number of loci tested (Pnominal = 8. 0×10−5; Pcorrected = 8. 0×10−4). In a combined analysis of GWAS and all follow-up genotyping results, rs4665058 well exceeded genome-wide significance (P = 1. 8×10−10) (Figure 1). The risk allele (A allele) of rs4665058 has a study size weighted frequency of 1. 4%, and increases risk for SCD by 1. 92-fold per allele (95% CI 1. 57 to 2. 34) in the combined analysis (Figure S2), and by 1. 65-fold per allele (95% CI 1. 29 to 2. 12) in the follow-up samples alone (Table 1). No significant heterogeneity was observed (Q statistic = 15. 6, P = 0. 11; I2 = 35. 9,95% CI 0 to 68. 5%). No significant interaction was observed for either sex or age (data not shown). It is interesting to note that the risk allele is the ancestral allele (based on non-human primate sequence), and its low frequency in European ancestry populations suggests strong negative selection, as fewer than 0. 8% of ancestral alleles have reached a frequency of 1. 4% or lower (Figure S3). In addition to performing an unbiased GWAS in the 5 discovery cohorts, we also used the results from the initial GWAS meta-analysis to examine the role of SNPs previously reported to be associated with QRS (ventricular depolarization), QT (ventricular depolarization/repolarization), and/or RR (inverse heart rate) intervals [11], [12], [13], [14]. These electrocardiographic measured traits are associated with cardiovascular mortality and SCD in both Mendelian settings (e. g. LQTS), and in the general population [15], [16], [17]. Overall, there were 49 independent loci associated with the ECG traits reported (25 for QRS [14], 16 QT [12], [13], 9 for RR [11], with one locus overlapping between QRS and QT intervals) (Tables S4, S5, S6). While in general, we hypothesize that QRS/QT/heart rate prolonging alleles increase risk for SCD, recent pleiotropy analyses of ECG traits have shown inconsistent directions of effects for some alleles (e. g. a QT-prolonging allele is associated with decreased QRS interval) [14], and thus, a priori we have chosen a two-sided test to assess significance of association for ECG SNPs with SCD. Nominal significance was observed for 3 loci, including PLN (QT/QRS, P = 0. 013), NOS1AP (QT, P = 0. 010), and KCNQ1 (QT, P = 0. 014). A fourth locus, TKT/CACNA1D/PRKCD (QRS, P = 0. 0007), was significant after multiple-test correction for all 49 tested SNPs (corrected P = 0. 034) (Table 2). Interestingly, the direction of effect for TKT/CACNA1D/PRKCD and KCNQ1 is opposite of that expected: the QRS/QT interval prolonging allele is associated with decreased risk for SCD. For the remaining 2 loci, the direction of effect is consistent with a model in which increasing QRS/QT interval increases risk for SCD. Indeed, looking across all 49 loci (Tables S4, S5, S6), we find that in aggregate QRS/QT/heart rate (inverse RR) -prolonging alleles are more often associated with increased risk for SCD (31/49; P = 0. 03). This result is entirely driven by QRS (17/25, P = 0. 04) (Table S4) and QT intervals (12/16, P = 0. 02) (Table S5), with no overrepresentation observed for RR interval (3/9, P = NS) (Table S6). The results for QRS and QT intervals (combined P = 0. 006) are consistent with epidemiological studies, which show increased QRS/QT interval in the general population is associated with increased risk of SCD. While the general trend is significant, the observation that 2/4 of the individual SNPs nominally associated with SCD actually show the opposite direction of effect, including TKT/CACNA1D/PRKCD, suggests the need to further elucidate how each of these variants modifies both the underlying QRS/QT interval trait as well as the potential role in SCD. For example, it is possible that the risk for SCD is not directly (or entirely) mediated though the effect on QRS/QT interval, as has been suggested for NOS1AP [18].
Using meta-analysis of GWAS with follow-up genotyping in independent samples, we demonstrate strong evidence for SCD susceptibility at locus 2q24. 2 in individuals of European ancestry. Somewhat surprisingly, we identified a relatively rare allele (MAF = 0. 014) with a strong effect (OR = 1. 92), which is in contrast to most GWAS for complex traits, which have typically identified alleles with MAF >0. 1 and ORs<1. 5. Given concerns about imputation accuracy for rarer alleles, we note that rs4665058 is almost perfectly in LD with rs174230 (r2 = 1. 0 in CEU), which is directly genotyped in all the GWAS samples, and shows almost identical association results (Table 1). Further, we observe the same effect size for FinGesture GWAS and follow-up genotyping samples (Figure S2), again suggesting that imputation does not affect the results. Indeed, it is quite plausible that rare variants play a large role in risk for SCD given the fatal nature of SCD (∼10% of SCD victims survive) potentially selecting against more common risk alleles. Despite the strong evidence for association with SCD across the entire study, we note that no effect for rs4665058 is observed in the AGNES and CHS studies. Power was 63% and 52%, respectively, under an additive genetic model with OR = 1. 92, requiring a nominal P = 0. 05. We observe no significant heterogeneity in the meta-analysis including all studies (P = 0. 11), suggesting that the lack of association in these two cohorts is likely a function of random chance, magnified by the low frequency of the risk allele. We would also point out that AGNES has a more narrow phenotype than the other studies, and included only those who suffered their first MI and survived the initial event to receive in-hospital care, which could contribute to the lack of association. For CHS, SCD adjudication was performed along with the ARIC samples, and thus represents an identical phenotype. The only difference was the age of the individuals in these two cohorts (ARIC 45–64 years, CHS >65), however in interaction analyses we did not observe age to be a significant modifier of the genetic effect. As with other GWAS, the current study design is not without limitations. First, association approaches depend upon linkage disequilibrium to identify associated SNPs, thus the underlying functional variant at any of the loci is potentially unrecognized. Indeed, using Pilot 1 data from the 1000 Genomes project (November 2010 release) [19], we do not identify any missense mutations highly correlated with the most strongly associated SNP, rs4665058 (r2>0. 8), suggesting that the functional variant is likely to be regulatory. To test whether rs4665058 was an eQTL, we searched the GTEx eQTL database (http: //www. ncbi. nlm. nih. gov/gtex/test/GTEX2/gtex. cgi), which queries lymphoblastoid, liver and various brain regions. We did not observe any eQTLs for rs4665058 or any SNPs from the 1000 Genomes data highly correlated with rs4665058 (r2>0. 8). We recognize that this negative finding is not necessarily informative, given that expression from heart tissue has not been queried. Second, we only implicate loci, as opposed to genes, and additional work is required to definitively identify which gene at a locus is responsible. Indeed, while the strongest association signal maps to an intron in BAZ2B, due to linkage disequilibrium, the signal also extends to the WDSUB1 and TANC1 genes. All three genes are expressed in human heart (http: //www. genecards. org) and the mouse heart during various key stages during cardiogenesis and the formation of the autonomic nervous system from neural crest (Embryonic day, E7. 5–10. 5) (http: //biogps. gnf. org). BAZ2B and WDSUB1 are essentially uncharacterized, however, TANC1 has been shown to regulate dendritic spines and excitatory synapses in both cultured neurons and a mouse knock-out [20]. TANC1 is most highly expressed in heart in humans, however, no cardiac phenotype in the TANC1 knock-out mouse was noted. Third, while we present evidence that in aggregate QRS/QT interval-prolonging alleles are associated with SCD, only TKT/CACNA1D/PRKCD exceeds a Bonferroni corrected P-value threshold, suggesting inadequate power for these analyses. The findings related to individual QRS/QT interval-prolonging alleles should therefore be considered exploratory and require replication in additional populations. Finally, we note that the meta-analysis consisted of both population-based and case-control studies, with some of the case-control studies using CAD controls as opposed to population-based controls (Tables S1 and S3). The inclusion of different control groups allows us to separate out whether the risk conferred by the BAZ2B locus acts through increased risk for CAD, which is present in ∼80% of SCD victims. The consistent results in studies with CAD controls (FinGesture GWAS, Oregon-SUDS GWAS, Oregon-SUDS replication) (Figure S2) provide evidence that the risk associated with rs4665058 may be specific to SCD, rather than a generic risk factor for CAD. Indeed, given the case mix across the cohorts (primary ventricular fibrillation, ischemic CAD, non-ischemic CAD, non-CAD), the study is best powered to identify variants that increase risk of SCD through a mechanism common across the various subtypes of SCD. In summary, we have identified the locus including the bromodomain-containing gene [21], BAZ2B, as a new SCD susceptibility locus. The bromodomain is an exclusive protein domain known to recognize acetyl-lysine residues on proteins and might play an important role in chromatin remodeling and gene transcription regulation [22]. The risk allele, while low frequency in Caucasian populations (MAF = 0. 014) has a relatively large effect, increasing risk for SCD by >1. 9-fold per allele (95% CI 1. 57 to 2. 34). While rs4665058 may not be clinically relevant in the general population in whom the increment in absolute risk attributable to the variant is modest, exploring its role in high-risk populations (e. g. heart failure, SQTS/LQTS) may help to identify those who could benefit from intervention. Beyond the BAZ2B locus, our study also highlights the role of QRS/QT interval associated variants in the risk of SCD, and suggests that larger GWAS of these and other intermediate risk factors may yield additional SCD loci.
Five studies consisting of individuals of European ancestry from Europe and the United States contributed to the GWAS discovery phase of this study: Atherosclerosis Risk in Communities (ARIC), Framingham Heart Study (FHS), FinGesture, Oregon-Sudden Unexpected Death Study (Oregon-SUDS), and Rotterdam Study (RS). For follow-up, additional genotyping was performed in independent samples from FinGesture and Oregon-SUDS, as well as in 8 additional populations of European ancestry: AmsteRdam REsuscitation STudies (ARREST), Cardiovascular Health Study (CHS), CVPath Institute Sudden Cardiac Death registry (CVPI-SCDr), and Harvard Cohorts (consisting of 5 combined populations, see below). We also performed a look-up of our top result in the AGNES study. All studies received approval from the appropriate institutional review committees, and the subjects in each cohort provided written informed consent. For all studies, covariates, measured at baseline for prospective cohorts, included age and gender, with the following exceptions: FinGesture GWAS included the top 10 principal components calculated from pre-imputation genotype data through a multi-dimensional scaling (MDS) method, as implemented in PLINK [34]; CVPI-SCDr did not include covariates, as the controls were population-based controls from CHS. Genotyping was performed using either Affymetrix or Illumina arrays, depending on the cohort (Table S7). Each study performed filtering of both individuals and SNPs to ensure robustness for genetic analysis. SNP genotypes were assessed for quality, and SNPs failing quality control were removed before imputation according to specific criteria (Table S7). Each study utilized the remaining SNP genotypes to impute genotypes for approximately 2. 5 million autosomal SNPs based on linkage disequilibrium patterns observed in the HapMap CEU samples (Utah residents of Northern and Western European descent). Imputed genotypes were calculated as dosages, with fractional values between 0 and 2 reflecting the estimated number of copies of a given allele for a given SNP for each individual. The use of dosages allows for the incorporation of the uncertainty in the imputations into subsequent analysis. All studies used a hidden Markov model as implemented in the MACH software [35]. All results are reported on the forward strand. Post-imputation, quantile-quantile (QQ) plots were generated for each study, stratified by both allele frequency and imputation quality to identify classes of SNPs that show strong early departure from the null. Based on these analyses, for the ARIC and Rotterdam studies, SNPs with minor allele frequency (MAF) <0. 01 were excluded. For FHS, SNPs with MAF <0. 02 and/or imputation quality score <0. 40 were excluded. Genotyping was performed using iPlex single base primer extension with MALDI-TOF mass spectrometry according to manufacturer protocols (Sequenom Inc. , San Diego, CA). SNPs were excluded from each study if call rate was ≤90% or Hardy-Weinberg equilibrium P<0. 001. SNPs were excluded from final meta-analysis if fewer than 4 out of 5 studies reported back results for that SNP. PCR and extension primer sequences are available upon request. In ARREST, rs4665058 was genotyped using TaqMan (Applied Biosystems), and primer and probe sequences are available upon request. In AGNES, genotypes at rs4665058 were determined by direct genotyping (N = 360) using Taqman assay or imputation from HapMap reference panel (N = 969) using the Markov-chain Monte Carlo method implemented in MACH1. 0 (Rsq = 0. 97). Details on genotype imputation have been described elsewhere [10]. For prospective community-based samples, associations between SCD and SNPs were tested using Cox proportional hazards regression models under the assumption of an additive model of genotypic effect. For case-control samples, a logistic regression framework was employed. For the Harvard cohorts, risk set analysis was used to match cases and controls holding time at risk stable; conditional logistic regression in risk set sampling avoids differences in time at risk that otherwise result. These models were adjusted for age and sex. In family-based cohorts (FHS), linear mixed modeling was implemented to additionally control for relatedness [36]. A genomic control correction factor (λ), calculated from all imputed SNPs, was applied on a per-study basis to account for cryptic population sub-structure and other potential biases [37]. Regression results were meta-analyzed using inverse variance weighted fixed-effects models as implemented in the METAL software package (http: //www. sph. umich. edu/csg/abecasis/metal/). Results were considered statistically significant at a P-value of 5×10−8, a figure that reflects the estimated testing burden of one million independent SNPs in samples of European ancestry [38]. For age by SNP and sex by SNP interactions, we performed regression analysis as described above separately within each study, including both main effects and an interaction term in the model. Meta-analysis of discovery and replication results was performed using inverse-variance weighting as implemented in the R package ‘meta’ (R version 2. 81, http: //www. r-project. org/). To account for the age range differences across the cohorts, we used a random-effects model for the age by SNP interaction.
Family studies have clearly demonstrated a role for genes in modifying risk for sudden cardiac death (SCD), however genetic studies have been limited by available samples. Here we have assembled over 4,400 SCD cases with >30,000 controls, all of European ancestry, and utilize a two-stage study design. In the first stage, we conducted an unbiased genome-wide scan in 1,283 SCD cases and >20,000 controls, and then performed follow-up genotyping in the remainder of the samples. We demonstrate strong association to a region of the genome not previously implicated in SCD, the BAZ2B locus, which contains 3 genes not previously known to play a role in cardiac biology. In addition, we used the genome-wide scan data to test a focused hypothesis that genetic variants that modulate ECG traits associated with SCD (QT, QRS, and RR intervals) also modify risk for SCD, and we demonstrate that QT- and QRS-prolonging alleles are, as a group, associated with increased risk of SCD. Taken together, these findings begin to elucidate the genetic contribution to SCD susceptibility and provide important targets for functional studies to investigate the etiology and pathogenesis of SCD.
acute cardiovascular problems
genome-wide association studies
medicine
coronary artery disease
arrhythmias
genetics
biology
human genetics
genetics of disease
genetics and genomics
cardiovascular
Identification of a Sudden Cardiac Death Susceptibility Locus at 2q24.2 through Genome-Wide Association in European Ancestry Individuals