Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation

Introduction

P-wave duration (PWD) is an electrocardiographic measurement that reflects cardiac conduction through the atria. PWD variability may implicate intrinsic or acquired properties in the function and structure of atrial conductivity.¹ Shortened and prolonged PWD have been repeatedly associated with atrial fibrillation (AF),^2,3 a common and heritable⁴ arrhythmia that predisposes to stroke, heart failure, and increased mortality.^5–7

Although PWD is heritable^{8, 9} only 2 genome-wide association studies have been conducted.^10,11 Given the relationship between PWD and AF, examining the genetic determinants of PWD may provide insights into the pathophysiology of AF. Moreover, assessment of coding variation may facilitate identification of AF-specific genes. Therefore, we conducted an exome-chip based analysis focused on rare and common genetic determinants of PWD.

Methods

Each study was reviewed and approved by the local or institutional IRB, and each participant provided consent. Study-specific details are provided in Data Supplement, under Description of participating studies and in Table I in the Data Supplement. In our primary analysis, we considered loci/genes significantly associated with PWD if a common variant (minor allele frequency [MAF] ≥5%) or a gene-based test, including burden or sequence kernel association test¹² comprising low-frequency variants (MAF <5% or MAF <1%) exceeded exome-wide significance in meta-analyses, after Bonferroni correction. We reported low-frequency variants that exceeded exome-wide significance at significant loci identified in gene-based analyses. The full Methods section is available in the Data Supplement (under Methods). Data supporting the findings of this study can be made available, following reasonable request to the corresponding author.

Results

A total of 64 440 individuals from 4 ethnic groups (56 943 European, 5681 African, 630 Asian, 1186 Hispanic), and 15 studies were included in our meta-analysis. The per-study mean age ranged from 46.2 to 72.6 years; roughly 60% of participants were women (Table 1). For the multiethnic single variant analyses, we tested ≈26 000 common variants (see Table III in the Data Supplement for the exact number of variants included in each analysis). The Quantile-Quantile plots show a small degree of inflation for both PWD residuals (λ=1.10) and inverse normal transformed PWD residuals (λ=1.13; Figure IA and IB in the Data Supplement). We performed meta-analyses in ethnicity-specific groups (European: λ=1.10–1.13; African: λ=1.03; Figure IC through IF in the Data Supplement). Linkage disequilibrium score regression intercepts were 1 (multiethnic analyses) and 0.95 (European-specific analyses), suggesting the inflation was mainly due to polygenicity. Meta-analysis results from PWD residuals and inverse normal transformed PWD residuals were highly correlated across analyses (Pearson ρ≥0.99, P<2.2×10^-16; Figure II in the Data Supplement).

Common Variant Analyses

We identified 41 exome-wide significant variants at 18 loci (P<1.9×10^-6; Figure III in the Data Supplement) in our multiethnic meta-analysis of PWD residuals (Table 2). Eleven of the 18 PWD loci are novel, representing the following nearest genes: PKP1 (rs1626370, P=2×10^-6), TTN (rs2042995, P=4×10^-7), PITX2 (rs17042171, P=8×10^-11), ARHGAP10 (rs6845865, P=2×10^-10), TCF21 (rs2327429, P=2×10^-7), CDK6 (rs2282978, P=2×10^-8), SYNPO2L (rs3812629, P=4×10^-7), SOX5 (rs17287293, P=3×10^-7), HMGA2 (rs8756, P=7×10^-7), GORS4 (rs17608766, P=9×10^-15), and MC4R (rs12970134, P=1×10^-6). Another novel locus was associated only with the inverse normal transformed PWD (JAZF1, P=1×10^-6; Table 2; Table IV in the Data Supplement). The PWD variance explained by each of the top variants ranged from 0.04% to 0.44%; the top variants in aggregate explained ≈1.6% of the phenotypic variance. Associations for SCN10A and PITX2 regions were moderately heterogeneous across individual studies (I² ≥45%; Table 2). Of these 19 multiethnic significantly associated loci, 13 were significantly associated with PWD residuals in the European ancestry subset, and one (SCN10A) was observed in individuals of African ancestry (Table IV in the Data Supplement). No additional loci were observed in analyses restricted to either European or African ancestry (Figure IV in the Data Supplement for Manhattan plots).

In conditional analyses, we identified additional signals from SCN5A and SCN10A (Table V in the Data Supplement). For inverse normal transformed PWD residuals, an additional signal (rs10033464, P=2×10^-7) was observed in the PITX2 region. In addition to the 7 previously known loci that exceeded exome-wide significance, we observed 2 nominally significant associations with PWD at SSBP3 and EPAS1 (P<0.001; Table VI in the Data Supplement).¹⁰

Gene-Based Analyses

We performed burden and sequence kernel association tests for associations with PWD for 16 949 genes with a cumulative minor allele count ≥10, including 192 455 low-frequency and rare variants, in the multiethnic sample. We identified 4 genes associated with PWD using sequence kernel association tests aggregating functional variants with MAF <5% (TTN, P=6×10^-27; DLEC1, P=2×10^-13; SCN10A, P=7×10^-8; and RPL3L, P=9×10^-7; Table 3). We identified an additional association (TTC21A, P=1×10^-6) using inverse normal transformed PWD residuals in the European-specific analysis. Using burden tests, we identified TTN and MUC5B as PWD-associated genes in the multiethnic and European-specific analyses. We did not observe any significant associations for variants with MAF <1%, suggesting that identified associations were mainly driven by low-frequency, not rare, variants. Among these significant genes, we identified 2 additional low-frequency missense variants exceeding exome-wide significance for association (DLEC1, rs116202356, Glu264Lys, P=2×10^-10; RPL3L, rs113956264, Val262Met, P=1×10^-6; Table 2), which were not reported in our single variant tests.

Expression Quantitative Trait Locus Analyses Between Genes at PWD Loci and Gene Expression

We assessed expression quantitative trait locus associations for top variants and proxies (linkage disequilibrium: r²>0.8; 1000 Genomes: phase 3 version 5, all individuals from LDlink¹³) in 2 heart tissues from GTEx version 7 (right atrial appendage [RAA] and left ventricle [LV]; Table VII in the Data Supplement).¹⁴ Six loci were associated with significant changes in gene expression, especially in the RAA, including 2 known PWD loci (HCN1, FADS1) and 4 novel loci (TTN, TCF21, JAZF1, SYNPO2L; Table VII in the Data Supplement). The alleles associated with longer PWD at HCN1 and SYNPO2L had lower expression of these genes in RAA tissues. In contrast, alleles at the JAZF1 and FADS1 loci were associated with higher gene expression in the RAA and LV, respectively. Gene expression directionality was consistent across RAA and LV tissues. Expression level changes of JAZF1 and MYOZ1 per allele in RAA tissue were significantly higher than in the LV. We observed more significant expressions quantitative trait locus in the RAA than the LV, as expected, because PWD reflects atrial conduction.

Relation of the PWD With ECG Traits Identifies 4 Novel and 5 Known Loci

We examined associations between PWD loci and other ECG measurements from large-scale association studies (Table VIII in the Data Supplement). We identified 8 novel (TTN, DLEC1, ARHGAP10, JAZF1, SYNPO2L, SOX5, HMGA2, GOSR2) and 5 known (SCN10A, CAV1, FADS1, TBX5, MYH6) PWD loci, all previously reported to be associated with PR interval, PR segment, QRS duration, QT interval, or RR interval. Variants at TCF21, SYNPO2L, and MYH6 were associated with PR interval in recent large-scale genetic association studies,^15–17 but the top variants in our PWD analysis were in low to moderate linkage disequilibrium with top variants from these earlier analyses (linkage disequilibrium, r²<0.8; 1000 Genomes: phase 3 version 5, all individuals).

Overlap Between PWD Loci and AF

Fourteen PWD loci were associated with AF risk in a recent AF genome-wide association studies¹⁸ (P<0.0024=0.05/21 loci; Figure 1 and Table VIII in the Data Supplement). Two loci in well-known AF gene regions, PITX2 and TTN, were novel PWD loci. Among these 14 loci, 6 were associated with longer PWD and higher AF risk (TTN, TCF21, SOX5, GOSR2, MC4R, MYH6), whereas 8 were associated with longer PWD but lower AF risk (DLEC1, PITX2, CDK6, SYNPO2L, CAND2, SCN10A, CAV1, TBX5).

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Discussion

In a multiancestry study comprising ≈65 000 individuals, we identified 12 novel and 7 previously reported loci related to PWD in a meta-analysis of common exome-chip variants. After aggregating rare and low-frequency exonic variants, we identified 6 genes, including 2 additional low-frequency variants potentially related to PWD, and loci with specific patterns of association for PWD and AF risk. These findings suggest that AF may result from multiple genetic mechanisms, and PWD may be an endophenotype for these mechanisms.

Our study extends the literature on the genetic components underlying atrial conduction, and the relationship between PWD and AF risk. In comparison to earlier genetic association studies of PWD,^10,11 we predominantly focused on genetic variants in coding regions (Table 2). In total, we identified 21 common variant loci related to PWD. The top common variants explain ≈1.6% of the phenotypic variance in PWD. Our gene-based analyses also highlight the importance of low-frequency variants contributing to PWD in genes, such as TTN, SCN10A, and RPL3L.

Our findings have 2 major implications. First, associated loci span genes involved in the development and maintenance of adult cardiac tissue (PITX2, TCF21, HMGA2, NKX2-5, TBX5, CAND2, CDK6), muscle and sarcomere structure (TTN, SYNPO2L, SOX5, MYH6, RPL3L), ion channel function (HCN1, SCN10A), and cell-cell contact (PKP1, ARHGAP10, CAV1). We additionally noted several genes with a role in metabolism (JAZF1, CDK6, HMGA2, MC4R) though the connection to AF is less clear.^19–22 The transcription factor PITX2 is the top susceptibility locus for AF. Decreased Pitx2 expression in the adult left atrium is associated with AF in humans,²³ and abnormal cardiac conduction, and low-voltage P-waves in knockout mice.²⁴PITX2 is activated by TBX5 to coregulate many membrane effector genes (such as SCN5A, GJA5, and RYR2). Reduction of Tbx5 expression in a mouse model decreased myocardial automaticity.²⁵TCF21 is a transcription factor required during embryogenesis for formation of heart tissue and is involved in fibroblast generation after injury in adults.²⁶ The nuclear scaffolding protein HMGA2 trans-activates the heart-specific transcription factor NKX2-5.²⁷HMGA overexpression in mice mediates the response to pressure-overload induced cardiac remodeling.²⁸CAND2 suppresses myogenin degradation and directs cardiac progenitor cells towards a myocyte fate.²⁹

Titin (TTN) is a major structural component of the sarcomere, required for contractile function in cardiomyocytes. Loss of function mutations in TTN are associated with early-onset AF³⁰ and dilated cardiomyopathy.³¹ cytoskeletal heart-enriched actin-associated protein; aka SYNPO2L is a Z-disc protein; zebrafish knockdown models display hypertrophy and delayed conduction,³² and the locus has been associated with AF in genome-wide association studies.¹⁸SOX5 is a master regulator of cell fate in embryonic development.³³ In drosophila, SOX5 knockdown results in decreased heart rate and increased cardiac wall thickness.³⁴MYH6, specifically expressed in the atria, forms the thick filament in cardiac smooth muscle; mutations are associated with cardiomyopathies,³⁵ sinus node dysfunction,³⁶ and congenital heart disease.³⁷ Some identified genes are important for atrial conduction, including HCN1³⁸ and SCN10A³⁹ which govern potassium, and late sodium channel currents, respectively. The proteins ARHGAP10,⁴⁰PKP1,⁴¹ and CAV1,⁴² are involved in cell-cell contact and are necessary for efficient signal conduction. The ribosomal protein RPL3L is specifically expressed in skeletal muscle and heart; coding variants in this gene are associated with AF.⁴³

Second, our study implicates PWD as a powerful endophenotype for understanding the biological mechanisms of AF. Fifteen loci identified in our study were associated with AF risk in a recent AF genome-wide association studies,¹⁸ underscoring the genetic correlation between atrial conduction and AF risk. Epidemiological data indicate that PWD variability is associated with AF risk,^2,3 AF recurrence after cardioversion,⁴⁴ and ablation,⁴⁵ as well as ischemic stroke.⁴⁶ Generally, we observed that top variants at known sarcomere genes (eg, TTN, MYH6) were associated with increased PWD and increased AF risk, implicating atrial myopathic pathways in AF susceptibility. We speculate that myopathic pathways predispose individuals to AF via delayed conduction velocity, increased propensity for reentry, and susceptibility to ectopic atrial activity. Similarly, TCF21 and SOX5 are 2 transcription factors associated with increased PWD and increased AF risk.

In contrast, top variants at SCN10A were associated with increased PWD but reduced AF risk. Other PWD-associated genes, such as PITX2, CAND2, TBX5, and CDK6, contained variants associated with longer PWD and reduced AF risk. The directionality of gene associations observed for PWD and AF risk underscore the complexity of AF susceptibility while highlighting the potential to leverage PWD to elucidate AF-specific pathways (Figure 2). Whether studying PWD can lead to insights relevant to therapeutic targeting remains unclear.

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Our results should be interpreted within the context of our study design. First, the majority of our sample consisted of individuals of European ancestry and may have limited generalizability to non-European ancestries. Studies with broader ethnic/racial diversity are warranted. Second, top variants identified in our study may not directly modulate PWD, a limitation of most genetic association studies. Biological characterization of loci is needed to conclusively link variants to function. Third, ascertainment of rare variation is limited using the exome-chip, and future analyses of sequence data are warranted. Fourth, despite a relatively large sample, our findings explained a small proportion of phenotypic variance. Because the additive SNP-based heritability of PWD has been estimated to be as high as 19%,⁸ our results highlight the fact that much of the genetic susceptibility to PWD remains unexplained. Larger samples, genome-wide assessments, and examination of rare variation may be necessary to identify additional loci for PWD.

In conclusion, we identified 14 novel loci in common and low-frequency variant analyses and 6 gene regions in a low-frequency variant analysis for PWD. Our findings highlight the shared genetic components of atrial conduction and AF risk and illustrate the diverse biological pathways affecting atrial conduction and mechanisms leading to AF.

Acknowledgments

Complete acknowledgments by study are available in the Data Supplement. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. The data used for the analyses described in this article were obtained from the GTEx Portal on October 05, 2018 and January 25, 2020.

Footnotes