Abstract
For many patients with atrial fibrillation (AF), ventricular rate control with atrioventricular (AV) nodal blockers is considered first-line therapy, though response to treatment is highly variable. Using an extreme phenotype of failure of rate control necessitating AV nodal ablation and pacemaker implantation, we conducted a genome wide association study (GWAS) to identify genomic modulators of rate control therapy. Cases included 95 patients who failed rate control therapy. Controls (N=190) achieved adequate rate control therapy with ≤2 AV nodal blockers using a conventional clinical definition. Genotyping was performed on the Illumina 610-Quad platform, and results were imputed to the 1000 Genomes reference haplotypes. 554,041 single nucleotide polymorphisms (SNPs) met criteria for minor allele frequency (>0.01), call rate (>95%), and quality control, and 6,055,224 SNPs were available after imputation. No SNP reached the canonical threshold for significance for GWAS of P<5 × 10−8. Sixty-three SNPs with P<10−5 at 6 genomic loci were genotyped in a validation cohort of 130 cases and 157 controls. These included 6q24.3 (near SAMD5/SASH1, P=9.36 × 10−8), 4q12 (IGFBP7, P=1.75 × 10−7), 6q22.33 (C6orf174, P=4.86 × 10−7), 3p21.31 (CDCP1, P=1.18 × 10−6), 12p12.1 (SOX5, P=1.62 × 10−6), and 7p11 (LANCL2, P=6.51 × 10−6). However, none of these were significant in the replication cohort or in a meta-analysis of both cohorts. In conclusion, we identified several potentially important genomic modulators of rate control therapy in AF, particularly SOX5, which was previously associated with resting heart rate and PR interval. However these failed to reach genome-wide significance.
Keywords: atrial fibrillation, genomics, rate control
Introduction
Atrial fibrillation (AF) is the most common sustained clinical arrhythmia, affecting an estimated 6 million patients in the United States. Given the increasing incidence of AF observed over the past 2 decades, its prevalence in the United States is projected to increase to 12 to 16 million by the year 2050.1 For many patients with AF, pharmacological therapy with atrioventricular (AV) nodal blockers is a first-line therapeutic strategy to control ventricular rate and prevent symptoms and systolic heart failure (HF) associated with rapid ventricular rates. This is generally accomplished with the administration of calcium channel blockers, betaadrenergic blockers, and/or digitalis, which slow conduction in the AV node. Rate control therapy has been shown to be equivalent at preventing clinical outcomes to a rhythm control strategy, in which antiarrhythmic drugs (AADs) and electrical cardioversions are used to maintain sinus rhythm.2 However, a substantial minority (~30%) of patients fail rate control therapy, either due to persistent symptoms or inability to control the ventricular rate despite therapeutic doses of multiple AV nodal blockers.3 Genetic differences might explain some of the inter-individual variability in the response to rate control therapy. We recently demonstrated that a single nucleotide polymorphism (SNP) in the β1-adrenergic receptor gene (ADRB1) is associated with response to rate control therapy.4 Recent genome wide association studies (GWASs) have identified genomic predictors of resting heart rate in sinus rhythm5–8 and PR interval.9 These findings suggest that response to rate control therapy may also be genotype-dependent. 10,11 The goal of this study was to conduct a GWAS comparing AF patients who responded effectively and ineffectively to ventricular rate control therapy. Given that the efficacy of rate control therapy is highly variable and is a complex clinical phenotype with multiple potential confounders, we chose an extreme phenotype for our analysis: failure of multiple AV nodal blockers necessitating AV node ablation and pacemaker implantation.
Methods
The discovery cohort consisted of patients in the Vanderbilt AF Registry (VAFR). As previously described, patients with AF have been recruited from Vanderbilt arrhythmia and general cardiology clinics, the emergency department, and inpatient services since April, 2000.12 The replication cohort consisted of patients selected from the Vanderbilt University Medical Center DNA Biobank (“BioVU”).13 This resource consists of de-identified medical records of Vanderbilt inpatients and outpatients and DNA extracted from blood that is left over after routine laboratory testing and scheduled to be discarded. The Vanderbilt University Institutional Review Board has approved VAFR and BioVU. All patients in VAFR gave written informed consent. Given their de-identified nature, patient records and biological samples in BioVU fall under the designation of “non-human subjects” under Title 45, Code of Federal Regulations, Part 46, and individual informed consent is not obtained. BioVU includes a mechanism to exclude subjects known to be part of prospective registries like VAFR; thus, samples from patients in VAFR were not included in the BioVU replication set.
Cases included patients in whom rate control therapy failed necessitating AV node ablation and permanent pacemaker implantation. Controls were AF patients who achieved criteria for successful rate control (ventricular rate ≤80 beats per minute at rest and no higher than 110 beats per minute during a 6 minute walk test), as outlined in the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) study,14 with ≤2 AV nodal blockers. The discovery cohort included 285 patients with persistent AF. Of these, 95 failed rate control therapy necessitating AV node ablation and pacemaker implantation (cases) and 190 were successfully rate-controlled with ≤2 AV nodal blockers (controls). The validation cohort consisted of 130 cases and 157 controls defined with the same criteria. Baseline clinical characteristics are presented in Table 1.
Table 1.
Baseline clinical characteristics for cases in whom rate control therapy was ineffective and in controls with adequate rate response.
| Discovery | Validation | |||||
|---|---|---|---|---|---|---|
| Cases (N=95) | Controls (N=190) | P value* | Cases (N=130) | Controls (N=157) | P value* | |
|
|
||||||
| Age (years) | 69.2±9.8 | 65.8±10.3 | 0.22 | 73.4±11.6 | 56.8±11.3 | <0.001 |
| Male | 60% | 78% | 0.001 | 53.1% | 67.5% | 0.001 |
| Hypertension | 64% | 69% | 0.4 | 68% | 66% | 0.45 |
| Diabetes | 31% | 19% | 0.04 | 32% | 18% | 0.01 |
| Coronary Artery Disease | 32% | 30% | 0.77 | 72% | 14% | <0.001 |
| Myocardial Infarction | 30% | 14% | 0.008 | 13% | 5.5% | 0.03 |
| Heart Failure | 30% | 22% | 0.15 | 81% | 9.2% | <0.001 |
Chi-square test for discreet variables, Mann-Whitney U test for continuous variables.
Genomic DNA was extracted from whole blood using standard techniques. Genotyping in the discovery cohort was performed for 620,902 variants with the Illumina 610-Quad platform (San Diego, CA). We performed quality control of these data by excluding samples with >5% missing genotypes, we estimated probability of identity by descent to check for family relationships, and agreement with database sex using X chromosome heterozygosity rates in PLINK.15 We dropped 2 samples for missing data, no samples for relationships above the 1st cousin level, and no samples for sex discordance. For SNPs within the set of high-quality samples, we dropped 43,807 SNPs for missing >5% genotypes, 54,558 monomorphic and rare SNPs with minor allele frequencies (MAF) <1%, and 1,601 SNPs for Hardy-Weinberg equilibrium test P-values smaller than 1×10−6. We also removed 2,718 palindromic SNPs to facilitate genotype imputation, and removed 15,553 non-autosomal SNPs. In the discovery cohort, 502,665 SNPs met the pre-specified criteria for inclusion in the analysis.
We imputed genotypes from the 1000 Genomes cosmopolitan reference panel of haplotypes using IMPUTE.16 A total of 6,055,224 genotyped and imputed SNPs with MAF ≥5% and information scores >0.4 were included in primary discovery analyses. Logistic regression models were fit for each SNP using SNPTEST,16 regressing the response to rate control outcome onto genotype, age, and sex. To evaluate quality control we calculated the genomic control parameter17 for tests at genotyped and imputed autosomal SNPs and λ =1.025, suggesting that there were no strong unaccounted for biases or confounders (Supplementary Figure 1).
We ranked SNPs by P-value, and conducted secondary analyses adjusting for the index, or most significant SNP in each genomic region within 1 megabase. We then selected a set of haplotype tag SNPs for the CEU reference population to tag a 100kb region centered on each index SNP with r2 ≥0.8, and MAF ≥0.05, and genotyped the index and tag SNPs in the replication samples (130 BioVU cases and 157 BioVU controls) using the Sequenom MassArray genotyping platform (Sequenom, Inc., San Diego, CA). A total of 63 SNPs at 6 genomic regions were selected for replication experiments (Table 2, Figure 1a–e).
Table 2.
Association results for index SNPs in six genomic regions from in the discovery cohort.
| Region | SNP | Nearest gene | Effect/referent | Effect Allele Frequency | Information | OR | 95% CI | p-value |
|---|---|---|---|---|---|---|---|---|
| 6q24 | rs1534773 | SAMD5/SASH1 | T/C | 0.82 | 0.99 | 0.28 | (0.18–0.45) | 9.36×10−8 |
| 4q12 | rs4242007 | IGFBP7 | C/G | 0.05 | 0.90 | 7.11 | (3.41–14.84) | 1.75×10−7 |
| 6q22 | rs1512450 | C6orf174 | G/C | 0.68 | 0.99 | 0.36 | (0.24–0.55) | 1.11×10−6 |
| 3p21 | rs55891698 | CDCP1 | T/C | 0.05 | 0.80 | 6.2 | (2.98–13.21) | 1.18×10−6 |
| 12p12 | rs7967643 | SOX5 | T/C | 0.82 | 0.99 | 0.31 | (0.19–0.50) | 1.62×10−6 |
| 7p11 | rs815929 | LANCL2 | A/C | 0.89 | 0.89 | 0.27 | (0.15–0.47) | 6.51×10−6 |
CI: confidence interval. OR: odds ratio. SNP: single nucleotide polymorphism.
Figure 1.
Manhattan plot of results for genome-wide association with failure of rate control therapy in patients with atrial fibrillation. The canonical threshold for genome-wide significance, P <5 × 10−8, is indicated by the red line. The threshold for inclusion of variants in the validation experiment, P<5 × 10−5, is indicated by the blue line.
Quality control procedures in the replication cohort were conducted as in the discovery cohort. No samples failed quality control. Two SNPs failed missing data filters. We then imputed the candidate regions to the 1000 Genomes cosmopolitan reference haplotypes with IMPUTE, and conducted logistic regression analysis in the replication samples using SNPTEST. We then conducted fixed-effects inverse variance weighted meta-analysis with METAL18 to obtain combined evidence of effects and significance from discovery and replication samples.
Results
In the discovery analysis, SNPs in 6 genomic regions were associated with rate control therapy failure (Table 2). No SNP reached the canonical threshold for significance for GWAS of P<5×10−8 in the discovery analysis (Figure 2). Multiple SNPs in the 6q24.3, 6q22.33, 4q12, 12p12.1, 3p21.31, and 7p11 regions were nominally significantly associated (Figures 1a–e).
Figure 2.
Plots of the top five most significant genomic regions associated with failure of rate control therapy in patients with atrial fibrillation: a) Chr6q24.3 (near SAMD5/SASH1); b) 4q12 (IGFBP7); c) 6q22.33 (C6orf174); d) 3p21.31 (CDCP1); and e) 12p12.1 (SOX5).
None of the 63 SNPs genotyped in the replication cohort were significantly associated with failure of rate control therapy (Table 3). Using the genotypes for these 63 SNPs, we imputed 2792 SNPs. The region near the IGFBP7 gene on chromosome 4 was the only region with p-values smaller than 0.05 in the replication cohort.
Table 3.
The 20 most significant SNPs from the replication cohort. All SNPs are in the chromosome 4 IGFBP7 region.
| SNP | Effect/referent | Effect Allele Frequency | Information | OR | 95% CI | p-value |
|---|---|---|---|---|---|---|
| rs4865181 | A/G | 0.22 | 0.96 | 0.57 | (0.38–0.85) | 0.006 |
| rs13119745 | T/C | 0.27 | 0.61 | 0.51 | (0.31–0.83) | 0.007 |
| chr4:57950946 | TA/T | 0.43 | 0.45 | 1.89 | (1.17–3.03) | 0.009 |
| rs6554428 | T/C | 0.55 | 0.90 | 1.55 | (1.11–2.15) | 0.010 |
| rs13108835 | C/A | 0.55 | 0.90 | 1.55 | (1.11–2.15) | 0.010 |
| rs11133483 | A/G | 0.57 | 0.97 | 1.54 | (1.11–2.13) | 0.010 |
| rs11133484 | G/A | 0.57 | 0.97 | 1.54 | (1.11–2.13) | 0.010 |
| rs7661524 | C/G | 0.55 | 0.91 | 1.54 | (1.11–2.14) | 0.010 |
| rs7681360 | A/G | 0.57 | 0.97 | 1.53 | (1.11–2.12) | 0.010 |
| rs11133482 | T/C | 0.57 | 0.97 | 1.53 | (1.11–2.12) | 0.010 |
| rs13129156 | G/A | 0.55 | 0.93 | 1.53 | (1.1–2.12) | 0.011 |
| rs9993153 | A/G | 0.55 | 0.92 | 1.53 | (1.1–2.13) | 0.011 |
| rs13147020 | G/A | 0.57 | 0.98 | 1.52 | (1.1–2.11) | 0.011 |
| rs7670481 | T/C | 0.54 | 0.83 | 1.55 | (1.1–2.19) | 0.013 |
| rs6825609 | A/G | 0.56 | 0.95 | 1.50 | (1.09–2.08) | 0.014 |
| rs4865182 | A/G | 0.24 | 1.00 | 0.62 | (0.43–0.91) | 0.015 |
| rs4075349 | T/C | 0.41 | 0.95 | 0.68 | (0.49–0.94) | 0.020 |
| rs1714007 | G/A | 0.65 | 1.00 | 1.49 | (1.06–2.08) | 0.020 |
| rs1714006 | G/A | 0.65 | 1.00 | 1.49 | (1.06–2.08) | 0.020 |
| rs1718829 | C/A | 0.65 | 0.99 | 1.49 | (1.06–2.08) | 0.020 |
CI: confidence interval. OR: odds ratio. SNP: single nucleotide polymorphism.
A meta-analysis was performed for the association of the imputed SNPs with failure of rate control therapy in the discovery and replication cohort (225 cases and 347 controls). None of the SNPs were significantly associated with failure of rate control therapy, and the most significantly associated SNPs with p-values smaller than 10−4 were all in the 6q24 region between the SAMD5 and SASH1 genes (Table 4).
Table 4.
Results from the meta-analysis of discovery and replication association summary statistics. All of the most significant results come from the region between the SAM5D/SASH1 genes on chromosome 6.
| SNP | effect/referent | OR | 95% CI | P-value | Direction |
|---|---|---|---|---|---|
| rs1534774 | C/G | 0.42 | (0.28–0.62) | 1.67×10−5 | −− |
| rs1534773 | T/C | 0.53 | (0.39–0.71) | 1.69×10−5 | −− |
| rs9386204 | A/G | 0.43 | (0.29–0.63) | 2.10×10−5 | −− |
| rs1358689 | T/C | 0.43 | (0.29–0.63) | 2.19×10−5 | −− |
| rs2328840 | A/C | 0.43 | (0.29–0.64) | 2.58×10−5 | −− |
| rs9390526 | T/C | 0.43 | (0.29–0.64) | 2.83×10−5 | −− |
| rs9322117 | A/C | 0.54 | (0.4–0.72) | 3.01×10−5 | −− |
| rs9322118 | T/C | 0.54 | (0.4–0.72) | 3.02×10−5 | −− |
| rs9377097 | A/G | 0.44 | (0.29–0.64) | 3.03×10−5 | −− |
| rs1881827 | A/G | 0.44 | (0.3–0.64) | 3.11×10−5 | −− |
| rs1997380 | T/C | 1.88 | (1.39–2.53) | 3.30×10−5 | ++ |
| rs1404488 | A/G | 2.30 | (1.55–3.41) | 3.53×10−5 | ++ |
| rs4896956 | T/G | 1.85 | (1.38–2.47) | 3.57×10−5 | ++ |
| rs7765323 | C/G | 1.84 | (1.38–2.46) | 3.67×10−5 | ++ |
| rs4896958 | T/C | 0.54 | (0.41–0.73) | 3.90×10−5 | −− |
| rs1406709 | A/T | 0.54 | (0.4–0.72) | 3.94×10−5 | −− |
| rs9403916 | T/C | 0.54 | (0.41–0.73) | 3.96×10−5 | −− |
| rs4896957 | T/C | 0.54 | (0.41–0.73) | 3.96×10−5 | −− |
| rs1406710 | A/T | 1.85 | (1.38–2.49) | 4.05×10−5 | ++ |
| rs9403915 | T/C | 0.55 | (0.41–0.73) | 4.21×10−5 | −− |
| rs9403913 | T/G | 0.55 | (0.41–0.73) | 4.28×10−5 | −− |
| rs9322115 | A/T | 1.91 | (1.4–2.61) | 4.66×10−5 | ++ |
| rs4896955 | T/C | 0.55 | (0.41–0.74) | 6.04×10−5 | −− |
| rs2876678 | A/G | 1.81 | (1.35–2.42) | 7.15×10−5 | ++ |
CI: confidence interval. OR: odds ratio. SNP: single nucleotide polymorphism.
Discussion
We conducted a GWAS for failure of rate control therapy in patients with AF, with cases defined as patients who required AV nodal ablation and pacemaker implantation, and controls as patients who met AFFIRM criteria for successful rate control with ≤2 AV nodal blockers. Although we found SNPs at 6 genomic loci near statistical significance in the discovery cohort, these failed to replicate in the replication cohort or in the combined meta-analysis of both cohorts.
The clinical response to AV nodal blockers in patients with AF managed with a rate control strategy is highly heterogeneous and multifactorial. Several recent studies have led us to hypothesize that genetic factors contribute to this variability. Eijgelsheim and colleagues conducted a meta-analysis of GWASs of resting heart rate in 38,991 subjects of European descent.5 They discovered 6 novel loci associated with resting heart rate and confirmed 3 previously described loci. Three large studies in 2010 used GWAS to analyze variability in PR interval. In one of these, Pfeufer et al9 performed a meta-analysis of GWASs of PR interval that included 28,517 European subjects. They found 9 loci associated with PR interval (including SOX5), of which 5 were also associated with AF. We previously conducted a separate GWAS of PR interval in 2334 subjects drawn from BioVU, and replicated the finding that SCN10A variants are associated with variability in the PR interval.19 While the vast majority of subjects in these studies had no prior history of AF, the finding that genetic variants play a role in resting heart rate and AV nodal conduction led us to believe that they might also modify the response to rate control therapy in AF. For example, genetic variants in adrenergic receptor genes are associated with response to β-blockers in patients with hypertension and heart failure20 and also modify heart rate response to β-blockade in healthy subjects.21 We previously studied whether common polymorphisms in the ADRB1 gene (encoding the cardiac β1 receptor) are associated with response to rate control therapy in the Vanderbilt AF registry. Carriers of the Arg389Gly variant were more likely than wild-type individuals to achieve AFFIRM criteria for successful rate control.4 These studies led us to hypothesize that additional genetic variants are associated with successful rate control therapy in patients with AF.
In the current study, SNPs at 6 genomic regions were close to our pre-specified criteria for genome-wide statistical significance in the discovery cohort, although we failed to replicate these findings in the replication cohort. The first region was Chr6q24.3 near the sterile alpha motif containing 5 (SAMD5) and sterile alpha motifs and SH3 domain-containing protein 1 (SASH1) genes. SASH1 is ubiquitously expressed with highest tissue level in lung, placenta, spleen, and thymus. It is down-regulated in the majority of breast tumors (74%) in comparison with corresponding normal breast epithelial tissue.22 Reduced expression is also reported in a majority of lung tumors, thyroid tumors and in a few colon cancers. SAMD5 is ubiquitously expressed, particularly in the esophagus, colon, ileum, oral mucosa, and kidney. While little has been published about the protein’s function, SAMD5 expression was significantly decreased in human eye trabecular meshwork cells treated with PITX2 small interfering RNAs.23 PITX2 encodes the paired-like homeodomain 2 transcription factor which is not only important for proper eye development but is also crucial for left-right patterning and development of the pulmonary myocardium during cardiogenesis, and has been robustly associated with AF risk and response to therapy for AF.10,11,24,25
Two SNPs at Chr4q12 (near IGFBP7) were associated with failed rate control in the discovery cohort. IGFBP7 is expressed in many tissues including heart, and a splice site mutation was associated with retinal arterial macroaneurysm and supravalvular pulmonic stenosis in 8 Saudi families.26 Although the function of IGFBP7 is incompletely understood, it has been implicated as a tumor suppressor gene that binds vascular endothelial growth factor (VEGF)-A and is involved in angiogenesis in breast and colorectal cancer, meningioma, and melanoma. IGFBP7 was also found to play a role in physiological angiogenesis during embryogenesis in mice.27 In addition, a morpholino knock-down model of the IGFBP7 ortholog in zebrafish demonstrated a complex phenotype that included global defects in blood vessel formation as well as pericardial edema.27 Therefore, IGFBP7 plays a vital role in angiogenesis in zebrafish and mice, and in humans has been linked to structural cardiac disease.
An intronic SNP in the CUB domain-containing protein (CDCP)-1 gene was associated with failure of rate control therapy in the discovery cohort. This gene, which has not been extensively studied, encodes a protein involved with cell adhesion and has been implicated as an oncogene.28 Its potential role in cardiac pathophysiology has not been determined.
Several SNPs at Chr12p12.1 (near SOX5) fell just short of genome wide significance in the discovery cohort. This gene encodes a transcription factor that is expressed in many tissues, including the heart. Sox5 deficient mice displayed congenital skeletal and spinal cord deformities, and Sox5−/− knock-out mice died in utero of apparent heart failure.29 In humans, polymorphisms near SOX5 were associated with resting heart rate and PR interval in separate GWASs,5,9 although the mechanism by which this gene influences cardiac electrophysiology remains unknown.
As with all retrospective, nonrandomized studies, ours is susceptible to selection bias and the effects of unknown confounders. We found several loci associated with failure of rate control therapy in our discovery cohort but we were unable to replicate these findings in the replication cohort. There are no standardized and validated criteria to define successful ventricular rate control during AF. While the AFFIRM criteria have been widely used, their correlation with outcomes is unknown. In addition, more recent studies have suggested that there is no difference in outcomes between a strict versus lenient target heart rate response during AF.30 Success of rate control therapy is a difficult phenotype to study, as it is inherently subjective and influenced by many factors. Even though we attempted to uniformly apply the AFFIRM criteria to define the success of rate control therapy, it was up to individual practitioners to evaluate the success or failure. Other latent sources of confounding include patient compliance with medications and the ability to tolerate effective doses of AV nodal blockers. We attempted to address some of these confounders by choosing an extreme phenotype, the need for AV nodal ablation and pacemaker implantation, as the definition of failure of rate control in this study. However, even this is subject to bias since individual providers might have different thresholds for AV nodal ablation. Finally, our study was relatively small and susceptible to type 2 error.
Supplementary Material
Quantile-quantile plot of association test results from the discovery cohort analysis.
Acknowledgments
Financial Support: This work was supported by NIH grants U19 HL65962 and R01 HL092217, CTSA award UL1TR000445, and an American Heart Association Established Investigator Award (0940116N).
Footnotes
Conflicts of interest: none.
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References
- 1.Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, Seward JB, Tsang TSM. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114:119–125. doi: 10.1161/CIRCULATIONAHA.105.595140. [DOI] [PubMed] [Google Scholar]
- 2.Testa L, Biondi-Zoccai GGL, Russo Dello A, Bellocci F, Andreotti F, Crea F. Rate-control vs. rhythm-control in patients with atrial fibrillation: a meta-analysis. Eur Heart J. 2005;26:2000–2006. doi: 10.1093/eurheartj/ehi306. [DOI] [PubMed] [Google Scholar]
- 3.Olshansky B, Rosenfeld LE, Warner AL, Solomon AJ, O’Neill G, Sharma A, Platia E, Feld GK, Akiyama T, Brodsky MA, Greene HL. The Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) study. Approaches to control rate in atrial fibrillation. J Am Coll Cardiol. 2004;43:1201–1208. doi: 10.1016/j.jacc.2003.11.032. [DOI] [PubMed] [Google Scholar]
- 4.Parvez B, Chopra N, Rowan S, Vaglio JC, Muhammad R, Roden DM, Darbar D. A common β1-adrenergic receptor polymorphism predicts favorable response to ratecontrol therapy in atrial fibrillation. J Am Coll Cardiol. 2012;59:49–56. doi: 10.1016/j.jacc.2011.08.061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Eijgelsheim M, Newton-Cheh C, Sotoodehnia N, de Bakker PIW, Müller M, Morrison AC, Smith AV, Isaacs A, Sanna S, Dörr M, Navarro P, Fuchsberger C, Nolte IM, de Geus EJC, Estrada K, Hwang S-J, Bis JC, Rückert I-M, Alonso A, Launer LJ, Hottenga JJ, Rivadeneira F, Noseworthy PA, Rice KM, Perz S, Arking DE, Spector TD, Kors JA, Aulchenko YS, Tarasov KV, Homuth G, Wild SH, Marroni F, Gieger C, Licht CM, Prineas RJ, Hofman A, Rotter JI, Hicks AA, Ernst F, Najjar SS, Wright AF, Peters A, Fox ER, Oostra BA, Kroemer HK, Couper D, Völzke H, Campbell H, Meitinger T, Uda M, Witteman JCM, Psaty BM, Wichmann H-E, Harris TB, Kääb S, Siscovick DS, Jamshidi Y, Uitterlinden AG, Folsom AR, Larson MG, Wilson JF, Penninx BW, Snieder H, Pramstaller PP, van Duijn CM, Lakatta EG, Felix SB, Gudnason V, Pfeufer A, Heckbert SR, Stricker BHC, Boerwinkle E, O’Donnell CJ. Genome-wide association analysis identifies multiple loci related to resting heart rate. Hum Mol Genet. 2010;19:3885–3894. doi: 10.1093/hmg/ddq303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban H-J, Yoon D, Lee MH, Kim D-J, Park M, Cha S-H, Kim J-W, Han B-G, Min H, Ahn Y, Park MS, Han HR, Jang H-Y, Cho EY, Lee J-E, Cho NH, Shin C, Park T, Park JW, Lee J-K, Cardon L, Clarke G, McCarthy MI, Lee J-Y, Lee J-K, Oh B, Kim H-L. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet. 2009;41:527–534. doi: 10.1038/ng.357. [DOI] [PubMed] [Google Scholar]
- 7.Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, Gudjonsson SA, Jonasdottir A, Mathiesen EB, Njølstad I, Nyrnes A, Wilsgaard T, Hald EM, Hveem K, Stoltenberg C, Løchen M-L, Kong A, Thorsteinsdottir U, Stefansson K. Several common variants modulate heart rate, PR interval and QRS duration. Nat Genet. 2010;42:117–122. doi: 10.1038/ng.511. [DOI] [PubMed] [Google Scholar]
- 8.den Hoed M, Eijgelsheim M, Esko T, Brundel BJ, Peal DS, Evans DM, Nolte IM, Segrè AV, Holm H, Handsaker RE, Westra HJ, Johnson T, Isaacs A, Yang J, Lundby A, Zhao JH, Kim YJ, Go MJ, Almgren P, Bochud M, Boucher G, Cornelis MC, Gudbjartsson D, Hadley D, van der Harst P, Hayward C, den Heijer M, Igl W, Jackson AU, Kutalik Z, Luan J, Kemp JP, Kristiansson K, Ladenvall C, Lorentzon M, Montasser ME, Njajou OT, O’Reilly PF, Padmanabhan S, St Pourcain B, Rankinen T, Salo P, Tanaka T, Timpson NJ, Vitart V, Waite L, Wheeler W, Zhang W, Draisma HH, Feitosa MF, Kerr KF, Lind PA, Mihailov E, Onland-Moret NC, Song C, Weedon MN, Xie W, Yengo L, Absher D, Albert CM, Alonso A, Arking DE, de Bakker PI, Balkau B, Barlassina C, Benaglio P, Bis JC, Bouatia-Naji N, Brage S, Chanock SJ, Chines PS, Chung M, Darbar D, Dina C, Dörr M, Elliott P, Felix SB, Fischer K, Fuchsberger C, de Geus EJ, Goyette P, Gudnason V, Harris TB, Hartikainen AL, Havulinna AS, Heckbert SR, Hicks AA, Hofman A, Holewijn S, Hoogstra-Berends F, Hottenga JJ, Jensen MK, Johansson A, Junttila J, Kääb S, Kanon B, Ketkar S, Khaw KT, Knowles JW, Kooner AS, Kors JA, Kumari M, Milani L, Laiho P, Lakatta EG, Langenberg C, Leusink M, Liu Y, Luben RN, Lunetta KL, Lynch SN, Markus MR, Marques-Vidal P, Mateo Leach I, McArdle WL, McCarroll SA, Medland SE, Miller KA, Montgomery GW, Morrison AC, Müller-Nurasyid M, Navarro P, Nelis M, O’Connell JR, O’Donnell CJ, Ong KK, Newman AB, Peters A, Polasek O, Pouta A, Pramstaller PP, Psaty BM, Rao DC, Ring SM, Rossin EJ, Rudan D, Sanna S, Scott RA, Sehmi JS, Sharp S, Shin JT, Singleton AB, Smith AV, Soranzo N, Spector TD, Stewart C, Stringham HM, Tarasov KV, Uitterlinden AG, Vandenput L, Hwang SJ, Whitfield JB, Wijmenga C, Wild SH, Willemsen G, Wilson JF, Witteman JC, Wong A, Wong Q, Jamshidi Y, Zitting P, Boer JM, Boomsma DI, Borecki IB, van Duijn CM, Ekelund U, Forouhi NG, Froguel P, Hingorani A, Ingelsson E, Kivimaki M, Kronmal RA, Kuh D, Lind L, Martin NG, Oostra BA, Pedersen NL, Quertermous T, Rotter JI, van der Schouw YT, Verschuren WM, Walker M, Albanes D, Arnar DO, Assimes TL, Bandinelli S, Boehnke M, de Boer RA, Bouchard C, Caulfield WL, Chambers JC, Curhan G, Cusi D, Eriksson J, Ferrucci L, van Gilst WH, Glorioso N, de Graaf J, Groop L, Gyllensten U, Hsueh WC, Hu FB, Huikuri HV, Hunter DJ, Iribarren C, Isomaa B, Jarvelin MR, Jula A, Kähönen M, Kiemeney LA, van der Klauw MM, Kooner JS, Kraft P, Iacoviello L, Lehtimäki T, Lokki ML, Mitchell BD, Navis G, Nieminen MS, Ohlsson C, Poulter NR, Qi L, Raitakari OT, Rimm EB, Rioux JD, Rizzi F, Rudan I, Salomaa V, Sever PS, Shields DC, Shuldiner AR, Sinisalo J, Stanton AV, Stolk RP, Strachan DP, Tardif JC, Thorsteinsdottir U, Tuomilehto J, van Veldhuisen DJ, Virtamo J, Viikari J, Vollenweider P, Waeber G, Widen E, Cho YS, Olsen JV, Visscher PM, Willer C, Franke L, Erdmann J, Thompson JR, Sotoodehnia N, Newton-Cheh C, Ellinor PT, Stricker BH, Metspalu A, Perola M, Beckmann JS, Smith GD, Stefansson K, Wareham NJ, Munroe PB, Sibon OC, Milan DJ, Snieder H, Samani NJ, Loos RJ. Identification of heart rate–associated loci and their effects on cardiac conduction and rhythm disorders. Global BPgen Consortium; CARDIoGRAM Consortium, PR GWAS Consortium, Pfeufer A. QRS GWAS Consortium, QT-IGC Consortium, CHARGE-AF Consortium. Nat Genet. 2013;45:621–631. doi: 10.1038/ng.2610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, Smith AV, Tarasov KV, Müller M, Sotoodehnia N, Sinner MF, Verwoert GC, Li M, Kao WHL, Köttgen A, Coresh J, Bis JC, Psaty BM, Rice K, Rotter JI, Rivadeneira F, Hofman A, Kors JA, Stricker BHC, Uitterlinden AG, van Duijn CM, Beckmann BM, Sauter W, Gieger C, Lubitz SA, Newton-Cheh C, Wang TJ, Magnani JW, Schnabel RB, Chung MK, Barnard J, Smith JD, Van Wagoner DR, Vasan RS, Aspelund T, Eiriksdottir G, Harris TB, Launer LJ, Najjar SS, Lakatta E, Schlessinger D, Uda M, Abecasis GR, Müller-Myhsok B, Ehret GB, Boerwinkle E, Chakravarti A, Soliman EZ, Lunetta KL, Perz S, Wichmann H-E, Meitinger T, Levy D, Gudnason V, Ellinor PT, Sanna S, Kääb S, Witteman JCM, Alonso A, Benjamin EJ, Heckbert SR. Genome-wide association study of PR interval. Nat Genet. 2010;42:153–159. doi: 10.1038/ng.517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Parvez B, Vaglio J, Rowan S, Muhammad R, Kucera G, Stubblefield T, Carter S, Roden D, Darbar D. Symptomatic response to antiarrhythmic drug therapy is modulated by a common single nucleotide polymorphism in atrial fibrillation. J Am Coll Cardiol. 2012;60:539–545. doi: 10.1016/j.jacc.2012.01.070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Parvez B, Shoemaker MB, Muhammad R, Richardson R, Jiang L, Blair MA, Roden DM, Darbar D. Common genetic polymorphism at 4q25 locus predicts atrial fibrillation recurrence after successful cardioversion. Heart Rhythm. 2013;10:849–855. doi: 10.1016/j.hrthm.2013.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Darbar D, Motsinger AA, Ritchie MD, Gainer JV, Roden DM. Polymorphism modulates symptomatic response to antiarrhythmic drug therapy in patients with lone atrial fibrillation. Heart Rhythm. 2007;4:743–749. doi: 10.1016/j.hrthm.2007.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Roden D, Pulley J, Basford M, Bernard G, Clayton E, Balser J, Masys D. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008;84:362–369. doi: 10.1038/clpt.2008.89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wyse DG, Waldo AL, Dimarco JP, Domanski MJ, Rosenberg Y, Schron EB, Kellen JC, Greene HL, Mickel MC, Dalquist JE, Corley SD. A Comparison of Rate Control and Rhythm Control in Patients with Atrial Fibrillation. N Engl J Med. 2002;347:1825–1833. doi: 10.1056/NEJMoa021328. [DOI] [PubMed] [Google Scholar]
- 15.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511. doi: 10.1038/nrg2796. [DOI] [PubMed] [Google Scholar]
- 17.Devlin B, Bacanu SA, Roeder K. Genomic control to the extreme. Nat Genet. 2004;36:1129–1130. doi: 10.1038/ng1104-1129. [DOI] [PubMed] [Google Scholar]
- 18.Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–2191. doi: 10.1093/bioinformatics/btq340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Denny JC, Ritchie MD, Crawford DC, Schildcrout JS, Ramirez AH, Pulley JM, Basford MA, Masys DR, Haines JL, Roden DM. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation. 2010;122:2016–2021. doi: 10.1161/CIRCULATIONAHA.110.948828. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Johnson JA, Liggett SB. Cardiovascular pharmacogenomics of adrenergic receptor signaling: clinical implications and future directions. Clin Pharmacol Ther. 2011;89:366–378. doi: 10.1038/clpt.2010.315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kurnik D, Li C, Sofowora GG, Friedman EA, Muszkat M, Xie H-G, Harris PA, Williams SM, Nair UB, Wood AJJ, Stein CM. Beta-1-adrenoceptor genetic variants and ethnicity independently affect response to beta-blockade. Pharmacogenet Genomics. 2008;18:895–902. doi: 10.1097/FPC.0b013e328309733f. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zeller C, Hinzmann B, Seitz S, Prokoph H, Burkhard-Goettges E, Fischer JOR, Jandrig B, Schwarz L-E, Rosenthal AE, Scherneck S. SASH1: a candidate tumor suppressor gene on chromosome 6q24. 3 is downregulated in breast cancer. Oncogene. 2003;22:2972–2983. doi: 10.1038/sj.onc.1206474. [DOI] [PubMed] [Google Scholar]
- 23.Paylakhi SH, Fan J-B, Mehrabian M, Sadeghizadeh M, Yazdani S, Katanforoush A, Kanavi MR, Ronaghi M, Elahi E. Effect of PITX2 knockdown on transcriptome of primary human trabecular meshwork cell cultures. Molecular Vision. 2011;17:1209. [PMC free article] [PubMed] [Google Scholar]
- 24.Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, Arking DE, Müller-Nurasyid M, Krijthe BP, Lubitz SA, Bis JC, Chung MK, Dörr M, Ozaki K, Roberts JD, Smith JG, Pfeufer A, Sinner MF, Lohman K, Ding J, Smith NL, Smith JD, Rienstra M, Rice KM, Van Wagoner DR, Magnani JW, Wakili R, Clauss S, Rotter JI, Steinbeck G, Launer LJ, Davies RW, Borkovich M, Harris TB, Lin H, Völker U, Völzke H, Milan DJ, Hofman A, Boerwinkle E, Chen LY, Soliman EZ, Voight BF, Li G, Chakravarti A, Kubo M, Tedrow UB, Rose LM, Ridker PM, Conen D, Tsunoda T, Furukawa T, Sotoodehnia N, Xu S, kamatani N, Levy D, Nakamura Y, Parvez B, Mahida S, Furie KL, Rosand J, Muhammad R, Psaty BM, Meitinger T, Perz S, Wichmann H-E, Witteman JCM, Kao WHL, Kathiresan S, Roden DM, Uitterlinden AG, Rivadeneira F, McKnight B, Sjögren M, Newman AB, Liu Y, Gollob MH, Melander O, Tanaka T, Stricker BHC, Felix SB, Alonso A, Darbar D, Barnard J, Chasman DI, Heckbert SR, Benjamin EJ, Gudnason V, Kääb S. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44:670–675. doi: 10.1038/ng.2261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Shoemaker MB, Muhammad R, Parvez B, White BW, Streur M, Song Y, Stubblefield T, Kucera G, Blair M, Rytlewski J, Parvathaneni S, Nagarakanti R, Saavedra P, Ellis C, Whalen SP, Roden DM, Darbar D. Common Atrial Fibrillation Risk Alleles at 4q25 Predict Recurrence after Catheter-based Atrial Fibrillation Ablation. Heart Rhythm. 2013;10:394–400. doi: 10.1016/j.hrthm.2012.11.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Abu-Safieh L, Abboud EB, Alkuraya H, Shamseldin H, Al-Enzi S, Al-Abdi L, Hashem M, Colak D, Jarallah A, Ahmad H, Bobis S, Nemer G, Bitar F, Alkuraya FS. Mutation of IGFBP7 causes upregulation of BRAF/MEK/ERK pathway and familial retinal arterial macroaneurysms. Am J Hum Genet. 2011;89:313–319. doi: 10.1016/j.ajhg.2011.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hooper AT, Shmelkov SV, Gupta S, Milde T, Bambino K, Gillen K, Goetz M, Chavala S, Baljevic M, Murphy AJ, Valenzuela DM, Gale NW, Thurston G, Yancopoulos GD, Vahdat L, Evans T, Rafii S. Angiomodulin Is a Specific Marker of Vasculature and Regulates Vascular Endothelial Growth Factor-A–Dependent Neoangiogenesis. Circ Res. 2009;105:201–208. doi: 10.1161/CIRCRESAHA.109.196790. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Scherl-Mostageer M, Sommergruber W, Abseher R, Hauptmann R, Ambros P, Schweifer N. Identification of a novel gene, CDCP1, overexpressed in human colorectal cancer. Oncogene. 2001;20:4402–4408. doi: 10.1038/sj.onc.1204566. [DOI] [PubMed] [Google Scholar]
- 29.Smits P, Li P, Mandel J, Zhang Z, Deng JM, Behringer RR, de Crombrugghe B, Lefebvre V. The transcription factors L-Sox5 and Sox6 are essential for cartilage formation. Dev Cell. 2001;1:277–290. doi: 10.1016/s1534-5807(01)00003-x. [DOI] [PubMed] [Google Scholar]
- 30.Van Gelder IC, Groenveld HF, Crijns HJGM, Tuininga YS, Tijssen JGP, Alings AM, Hillege HL, Bergsma-Kadijk JA, Cornel JH, Kamp O, Tukkie R, Bosker HA, Van Veldhuisen DJ, Van den Berg MP. Lenient versus strict rate control in patients with atrial fibrillation. N Engl J Med. 2010;362:1363–1373. doi: 10.1056/NEJMoa1001337. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Quantile-quantile plot of association test results from the discovery cohort analysis.