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

Originally published Genomic and Precision Medicine. 2020;13:387–395



The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD.


Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies.


We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci (TTN, CAND2, SCN10A, PITX2, CAV1, SYNPO2L, SOX5, TBX5, MYH6, RPL3L). The top variants at known sarcomere genes (TTN, MYH6) were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, PITX2 and SCN10A) were associated with longer PWD but lower AF risk.


Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.


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 heritable8, 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.


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.


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).

Table 1. Study Participant Characteristics*

StudyAncestryNAge, y, Mean±SDSex, Women, %P-Wave Duration, ms, Mean±SDRR Interval, ms, Mean±SD
WHIEuropean10 76665.8±6.6100107.2±11.9914.3±134.2

ARIC indicates Atherosclerosis Risk in Communities study; BRIGHT, British Genetics of Hypertension; CAMP, MGH Cardiology and Metabolic Patient cohort; CHS, Cardiovascular Health Study; ERF, Erasmus Rucphen Family; FHS, Framingham Heart Study; INTER99,; KORA, Kooperative Gesundheitsforschung in der Region Augsburg; LIFELINES, Lifelines Cohort Study; MESA, Multi-Ethnic Study of Atherosclerosis; NEO, Netherlands Epidemiology of Obesity study; RS, Rotterdam Study; SHIP, Study of Health in Pomerania; UHP, the Utrecht Health Project; and WHI, the Women’s Health Initiative.

* N: sample size.

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).

Table 2. Top Exome-Wide Significant Variants for P-Wave Duration In Multiethnic Meta-Analysis

LocusResidualsInverse Normal Transformed Residuals
Closest GeneLocationrsIDEAFunctionNEAFBetaSEP Valueh2, %I2, %BetaSEP Valueh2, %I2, %
Novel loci
 1PKP11q32.1rs1626370AMissense64 4310.20.390.082×10-6*0.0420.030.012×10-6*0.040
 2TTN2q31.2rs2042995CIntron64 4100.30.410.084×10-7*0.0480.030.015×10-7*0.0412
 3DLEC13p22.2rs116202356GMissense64 3310.981.720.272×10-10*0.06200.140.022×10-10*0.0619
 4PITX24q25rs17042171CIntergenic64 3990.90.640.108×10-11*0.07450.060.012×10-11*0.0750
 5ARHGAP104q31.23rs6845865CIntron64 4370.20.540.092×10-10*0.0600.050.019×10-11*0.070
 6TCF21/TARID6q23.2rs2327429CUpstream64 4340.30.390.072×10-7*0.04130.030.011×10-7*0.049
 7JAZF17p15.1rs864745CIntron64 3880.50.320.072×10-60.0400.030.011×10-6*0.040
 8CDK67q21.2rs2282978CIntron64 4240.40.390.072×10-8*0.0500.030.015×10-8*0.056
 9SYNPO2L10q22.2rs3812629AMissense64 4230.20.470.094×10-7*0.0400.040.017×10-7*0.040
 10SOX512p12.1rs17287293AIntergenic64 4290.90.490.103×10-7*0.0400.040.013×10-7*0.040
 11HMGA212q14.3rs8756C3′-UTR64 4180.50.330.077×10-7*0.0400.030.015×10-7*0.040
 12RPL3L16p13.3rs113956264CMissense64 4030.970.990.201×10-6*0.0400.080.024×10-60.0310
 13GOSR217q21.32rs17608766CIntron64 4350.10.800.109×10-15*0.0900.070.011×10-15*0.100
 14MC4R18q21.32rs12970134AIntergenic64 4300.30.380.081×10-6*0.0400.030.017×10-60.030
Previously reported loci
 15CAND23p25.2rs11718898TMissense52 4720.30.390.089×10-7*0.0500.030.018×10-70.050
CAND23p25.2rs3732675TMissense64 3950.40.340.071×10-60.0400.030.013×10-7*0.040
 16SCN10A3p22.2rs6800541CIntron64 4230.41.180.074×10-63*0.44510.100.012×10-65*0.4545
 17HCN15p12rs6892594TIntron64 4270.40.430.072×10-10*0.0600.040.013×10-10*0.060
 18CAV17q31.2rs3807989AIntron64 4300.40.470.072×10-12*0.0800.040.018×10-13*0.080
 19FADS111q12.2rs174546C3′-UTR64 4300.70.500.072×10-11*0.0790.040.016×10-12*0.079
 20TBX512q24.21rs883079C3′-UTR64 4350.30.800.079×10-28*0.19170.070.016×10-29*0.1911
 21MYH614q11.2rs452036AIntron64 4220.40.680.078×10-23*0.1500.060.011×10-23*0.160

Beta indicates the changes of (inverse normal transformed) P-wave duration residuals per 1 effect allele increment; EA, effect allele; EAF, effect allele frequency; h2, SNP heritability estimate; N, sample size; and UTR, untranslated region.

* P values at exome-wide significance.

† Locus with minor allele frequency <5% is also identified from gene-based analysis.

‡ Locus with minor allele frequency <5% identified from gene-based analysis.

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.

Table 3. Top Gene in Low-Frequency Variant Gene-Based Analyses of P-Wave Duration Stratified by Ancestral Group

Var No.cMACResidualsInverse Normal Transformed ResidualsVar No.ResidualsInverse Normal Transformed ResidualsVar No.ResidualsInverse Normal Transformed Residuals
P ValueP ValuecMACP ValueP ValuecMACP ValueP Value
TTN775276 9865×10-27*5×10-26*70421 58015×10-27*1×10-26*53623 0410.590.71
DLEC15710 4192×10-13*2×10-13*5569372×10-12*3×10-12*3925680.700.73
TTC21A3712 2071×10-55×10-63210 9004×10-61×10-6*2812500.980.98
SCN10A6116 5507×10-8*9×10-9*4712 8042×10-7*4×10-8*345240.840.81
TTN775276 9861×10-14*8×10-14*704215 8011×10-20*4×10-18*53623 0410.260.27
MUC5B6836 4147×10-6*1×10-56325 1103×10-6*6×10-65828460.590.56

cMAC indicates cumulative minor allele count; SKAT, sequence kernel association test; and Var no., number of variants included in the gene set.

* P values that exceed the exome-wide significance threshold (P<3.0×10-6, 3.1×10-6, and 3.5×10-6 for individuals of multiethnic, European, and African ancestries, respectively).

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).

Figure 1.

Figure 1. P-wave duration (PWD) loci and atrial fibrillation (AF) risk. The x axis represents the association between the top PWD loci and PWD in −log10 scale. The y axis represents the association P value between the top PWD loci and AF risk (−log10 scale). Variants above y=0 refer to loci associated with longer PWD and higher AF risk (colored in yellow). Variants below y=0 refer to loci associated with longer PWD but lower AF risk (colored in blue). Displayed results are from the multiethnic meta-analysis of PWD residuals. Associations with AF were derived from a recent AF genome-wide association studies.18 Dashed lines show the significance threshold for the current exome-wide analysis (vertical; P<1.9×10-6) and for prior genome-wide analyses of AF (horizontal; P<5×10-8). The dotted line represents the significance cutoff after Bonferroni correction (horizontal; P<2.4×10-3=0.05/21 PWD loci).


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.

Figure 2.

Figure 2. Identified P-wave duration (PWD) associated genes highlight multiple biological pathways for atrial fibrillation (AF) risk. Genes with increasing risk of AF coupled with prolonged PWD are listed at the right. Genes with decreasing risk of AF coupled with prolonged PWD are listed at the left. Each gene is accompanied by a diagram representing the biological function of the gene, indicating how the gene may affect PWD.

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.

Nonstandard Abbreviations and Acronyms


atrial fibrillation


left ventricle


minor allele frequency


P-wave duration


right atrial appendage


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.


*Drs Weng and Hall contributed equally as first authors.

The Data Supplement is available at

For Sources of Funding and Disclosures, see page 394.

Correspondence to: Steven A. Lubitz, Cardiac Arrhythmia Service & Cardiovascular Research Center, Massachusetts General Hospital, 55 Fruit St, GRB 109, Boston, MA, 02114. Email


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