Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation
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).
|Study||Ancestry||N||Age, y, Mean±SD||Sex, Women, %||P-Wave Duration, ms, Mean±SD||RR Interval, ms, Mean±SD|
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).
|Locus||Residuals||Inverse Normal Transformed Residuals|
|Closest Gene||Location||rsID||EA||Function||N||EAF||Beta||SE||P Value||h2, %||I2, %||Beta||SE||P Value||h2, %||I2, %|
|Previously reported loci|
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
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.
|Var No.||cMAC||Residuals||Inverse Normal Transformed Residuals||Var No.||Residuals||Inverse Normal Transformed Residuals||Var No.||Residuals||Inverse Normal Transformed Residuals|
|P Value||P Value||cMAC||P Value||P Value||cMAC||P Value||P Value|
|TTN||775||276 986||5×10-27*||5×10-26*||704||21 5801||5×10-27*||1×10-26*||536||23 041||0.59||0.71|
|TTC21A||37||12 207||1×10-5||5×10-6||32||10 900||4×10-6||1×10-6*||28||1250||0.98||0.98|
|SCN10A||61||16 550||7×10-8*||9×10-9*||47||12 804||2×10-7*||4×10-8*||34||524||0.84||0.81|
|TTN||775||276 986||1×10-14*||8×10-14*||704||215 801||1×10-20*||4×10-18*||536||23 041||0.26||0.27|
|MUC5B||68||36 414||7×10-6*||1×10-5||63||25 110||3×10-6*||6×10-6||58||2846||0.59||0.56|
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).
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.
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.
minor allele frequency
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.
Sources of Funding
Dr Weng is supported by an American Heart Association (AHA) Postdoctoral Fellowship Award (17POST33660226). This work was supported by an AHA Strategically Focused Research Networks (SFRN) postdoctoral fellowship to Drs. Weng and Hall (18SFRN34110082). Funded in part by training grant (National Institute of General Medical Sciences) 5T32GM07814 (Dr Bihlmeyer) and R01HL116747 (Drs Arking and Bihlmeyer), and R01 HL111089 (Dr Arking). This material is based on work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1232825 (Dr Bihlmeyer). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. Additional support was provided by AHA grant 16EIA26410001 (Dr Alonso) and National, Heart, Lung and Blood Institute grant K24HL148521 (Dr Alonso). Dr Ramírez was supported by Medical Research Council grant MR/N025083/1, by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 608765 and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 786833. Dr Sotoodehnia is supported by the following grants from the National Institutes of Health (NIH): R01HL141989, HL116747, and R01 HL111089, and by the Laughlin Family Fund. Dr Kornej was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838259. Dr Benjamin is supported by NIH grants HHSN26818HV00006R; 75N92019D00031; R01HL092577; 1R01HL128914; and American Heart Association 18SFRN34110082. Dr Lunetta is supported by R01 HL092577, AHA 18SFRN34230127, and 18SFRN34150007. Dr Ellinor is supported by the Fondation Leducq (14CVD01), by grants from the NIH (1RO1HL092577, R01HL128914, K24HL105780), and by a grant from the AHA (18SFRN34110082). Dr Lubitz is supported by NIH grant 1R01HL139731 and AHA 18SFRN34250007. Additional funding and acknowledgments for each participating study are provided in the Data Supplement.
Dr Lubitz receives sponsored research support from Bristol-Myers Squibb / Pfizer, Bayer AG, and Boehringer Ingelheim, and has consulted for Bristol Myers Squibb / Pfizer and Bayer AG. Dr Ellinor is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. Dr Ellinor has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, Novartis, and MyoKardia. Dr Mook-Kanamori is a part-time clinical research consultant for Metabolon, Inc. The UMCG, which employs Dr de Boer, has received research grants or fees from AstraZeneca, Abbott, Bristol-Myers Squibb, Novartis, Novo Nordisk, and Roche. Dr de Boer received personal fees from Abbott, AstraZeneca, MandalMed, Inc, Novartis, and Roche. Psaty serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson.
Magnani JW, Williamson MA, Ellinor PT, Monahan KM, Benjamin EJ. P wave indices: current status and future directions in epidemiology, clinical, and research applications.Circ Arrhythm Electrophysiol. 2009; 2:72–79. doi: 10.1161/CIRCEP.108.806828LinkGoogle Scholar
Nielsen JB, Kühl JT, Pietersen A, Graff C, Lind B, Struijk JJ, Olesen MS, Sinner MF, Bachmann TN, Haunsø S,. P-wave duration and the risk of atrial fibrillation: Results from the Copenhagen ECG Study.Heart Rhythm. 2015; 12:1887–1895. doi: 10.1016/j.hrthm.2015.04.026CrossrefMedlineGoogle Scholar
Magnani JW, Zhu L, Lopez F, Pencina MJ, Agarwal SK, Soliman EZ, Benjamin EJ, Alonso A. P-wave indices and atrial fibrillation: cross-cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study.Am Heart J. 2015; 169:53–61.e1. doi: 10.1016/j.ahj.2014.10.009CrossrefMedlineGoogle Scholar
Christophersen IE, Ellinor PT. Genetics of atrial fibrillation: from families to genomes.J Hum Genet. 2016; 61:61–70. doi: 10.1038/jhg.2015.44CrossrefMedlineGoogle Scholar
Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study.Circulation. 1998; 98:946–952. doi: 10.1161/01.cir.98.10.946LinkGoogle Scholar
Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study.Stroke. 1991; 22:983–988. doi: 10.1161/01.str.22.8.983LinkGoogle Scholar
Wang TJ, Larson MG, Levy D, Vasan RS, Leip EP, Wolf PA, D’Agostino RB, Murabito JM, Kannel WB, Benjamin EJ. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study.Circulation. 2003; 107:2920–2925. doi: 10.1161/01.CIR.0000072767.89944.6ELinkGoogle Scholar
Mosley JD, Shoemaker MB, Wells QS, Darbar D, Shaffer CM, Edwards TL, Bastarache L, McCarty CA, Thompson W, Chute CG,. Investigating the genetic architecture of the PR interval using clinical phenotypes.Circ Cardiovasc Genet. 2017; 10:e001482.LinkGoogle Scholar
Smith JG, Lowe JK, Kovvali S, Maller JB, Salit J, Daly MJ, Stoffel M, Altshuler DM, Friedman JM, Breslow JL,. Genome-wide association study of electrocardiographic conduction measures in an isolated founder population: Kosrae.Heart Rhythm. 2009; 6:634–641. doi: 10.1016/j.hrthm.2009.02.022CrossrefMedlineGoogle Scholar
Christophersen IE, Magnani JW, Yin X, Barnard J, Weng LC, Arking DE, Niemeijer MN, Lubitz SA, Avery CL, Duan Q,. Fifteen genetic loci associated with the electrocardiographic P wave.Circ Cardiovasc Genet. 2017; 10:e001667.LinkGoogle Scholar
Verweij N, Mateo Leach I, van den Boogaard M, van Veldhuisen DJ, Christoffels VM, Hillege HL, van Gilst WH, Barnett P, de Boer RA, van der Harst P; LifeLines Cohort Study. Genetic determinants of P wave duration and PR segment.Circ Cardiovasc Genet. 2014; 7:475–481. doi: 10.1161/CIRCGENETICS.113.000373LinkGoogle Scholar
Liu DJ, Peloso GM, Zhan X, Holmen OL, Zawistowski M, Feng S, Nikpay M, Auer PL, Goel A, Zhang H,. Meta-analysis of gene-level tests for rare variant association.Nat Genet. 2014; 46:200–204. doi: 10.1038/ng.2852CrossrefMedlineGoogle Scholar
Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants.Bioinformatics. 2015; 31:3555–3557. doi: 10.1093/bioinformatics/btv402CrossrefMedlineGoogle Scholar
Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, Mohammadi P, Park Y, Parsana P, Segrè AV,. Genetic effects on gene expression across human tissues.Nature. 2017; 550:204–213.CrossrefMedlineGoogle Scholar
Lin H, van Setten J, Smith AV, Bihlmeyer NA, Warren HR, Brody JA, Radmanesh F, Hall L, Grarup N, Müller-Nurasyid M,. Common and rare coding genetic variation underlying the electrocardiographic PR interval.Circ Genom Precis Med. 2018; 11:e002037. doi: 10.1161/CIRCGEN.117.002037LinkGoogle Scholar
Ntalla I, Weng LC, Cartwright JH, Hall AW, Sveinbjornsson G, Tucker NR, Choi SH, Chaffin MD, Roselli C, Barnes MR,. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.Nat Commun. 2020; 11:2542. doi: 10.1038/s41467-020-15706-xCrossrefMedlineGoogle Scholar
van Setten J, Brody JA, Jamshidi Y, Swenson BR, Butler AM, Campbell H, Del Greco FM, Evans DS, Gibson Q, Gudbjartsson DF,. PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.Nat Commun. 2018; 9:2904. doi: 10.1038/s41467-018-04766-9CrossrefMedlineGoogle Scholar
Roselli C, Chaffin MD, Weng LC, Aeschbacher S, Ahlberg G, Albert CM, Almgren P, Alonso A, Anderson CD, Aragam KG,. Multi-ethnic genome-wide association study for atrial fibrillation.Nat Genet. 2018; 50:1225–1233. doi: 10.1038/s41588-018-0133-9CrossrefMedlineGoogle Scholar
Xi Y, Shen W, Ma L, Zhao M, Zheng J, Bu S, Hino S, Nakao M. HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARγ.Biochem Biophys Res Commun. 2016; 472:617–623. doi: 10.1016/j.bbrc.2016.03.015CrossrefMedlineGoogle Scholar
Meng F, Lin Y, Yang M, Li M, Yang G, Hao P, Li L. JAZF1 inhibits adipose tissue macrophages and adipose tissue inflammation in diet-induced diabetic mice.Biomed Res Int. 2018; 2018:4507659. doi: 10.1155/2018/4507659CrossrefMedlineGoogle Scholar
Hou X, Zhang Y, Li W, Hu AJ, Luo C, Zhou W, Hu JK, Daniele SG, Wang J, Sheng J,. CDK6 inhibits white to beige fat transition by suppressing RUNX1.Nat Commun. 2018; 9:1023. doi: 10.1038/s41467-018-03451-1CrossrefMedlineGoogle Scholar
Harrold JA, Widdowson PS, Williams G. beta-MSH: a functional ligand that regulated energy homeostasis via hypothalamic MC4-R?Peptides. 2003; 24:397–405. doi: 10.1016/s0196-9781(03)00054-8CrossrefMedlineGoogle Scholar
Brugger F, Wicki U, Nassenstein-Elton D, Fagg GE, Olpe HR, Pozza MF. Modulation of the NMDA receptor by D-serine in the cortex and the spinal cord, in vitro.Eur J Pharmacol. 1990; 191:29–38. doi: 10.1016/0014-2999(90)94093-dCrossrefMedlineGoogle Scholar
Tao Y, Zhang M, Li L, Bai Y, Zhou Y, Moon AM, Kaminski HJ, Martin JF. Pitx2, an atrial fibrillation predisposition gene, directly regulates ion transport and intercalated disc genes.Circ Cardiovasc Genet. 2014; 7:23–32. doi: 10.1161/CIRCGENETICS.113.000259LinkGoogle Scholar
Nadadur RD, Broman MT, Boukens B, Mazurek SR, Yang X, van den Boogaard M, Bekeny J, Gadek M, Ward T, Zhang M,. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm.Sci Transl Med. 2016; 8:354ra115. doi: 10.1126/scitranslmed.aaf4891CrossrefMedlineGoogle Scholar
Lighthouse JK, Small EM. Transcriptional control of cardiac fibroblast plasticity.J Mol Cell Cardiol. 2016; 91:52–60. doi: 10.1016/j.yjmcc.2015.12.016CrossrefMedlineGoogle Scholar
Monzen K, Ito Y, Naito AT, Kasai H, Hiroi Y, Hayashi D, Shiojima I, Yamazaki T, Miyazono K, Asashima M,. A crucial role of a high mobility group protein HMGA2 in cardiogenesis.Nat Cell Biol. 2008; 10:567–574. doi: 10.1038/ncb1719CrossrefMedlineGoogle Scholar
Wu QQ, Xiao Y, Liu C, Duan M, Cai Z, Xie S, Yuan Y, Wu H, Deng W, Tang Q. The protective effect of high mobility group protein HMGA2 in pressure overload-induced cardiac remodeling.J Mol Cell Cardiol. 2019; 128:160–178. doi: 10.1016/j.yjmcc.2019.01.027CrossrefMedlineGoogle Scholar
Shiraishi S, Zhou C, Aoki T, Sato N, Chiba T, Tanaka K, Yoshida S, Nabeshima Y, Nabeshima Y, Tamura TA. TBP-interacting protein 120B (TIP120B)/cullin-associated and neddylation-dissociated 2 (CAND2) inhibits SCF-dependent ubiquitination of myogenin and accelerates myogenic differentiation.J Biol Chem. 2007; 282:9017–9028. doi: 10.1074/jbc.M611513200CrossrefMedlineGoogle Scholar
Choi SH, Weng LC, Roselli C, Lin H, Haggerty CM, Shoemaker MB, Barnard J, Arking DE, Chasman DI, Albert CM,; DiscovEHR study and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. Association between titin loss-of-function variants and early-onset atrial fibrillation.JAMA. 2018; 320:2354–2364. doi: 10.1001/jama.2018.18179CrossrefMedlineGoogle Scholar
Herman DS, Lam L, Taylor MR, Wang L, Teekakirikul P, Christodoulou D, Conner L, DePalma SR, McDonough B, Sparks E,. Truncations of titin causing dilated cardiomyopathy.N Engl J Med. 2012; 366:619–628. doi: 10.1056/NEJMoa1110186CrossrefMedlineGoogle Scholar
van Eldik W, den Adel B, Monshouwer-Kloots J, Salvatori D, Maas S, van der Made I, Creemers EE, Frank D, Frey N, Boontje N,. Z-disc protein CHAPb induces cardiomyopathy and contractile dysfunction in the postnatal heart.PLoS One. 2017; 12:e0189139. doi: 10.1371/journal.pone.0189139CrossrefMedlineGoogle Scholar
Lefebvre V. The SoxD transcription factors–Sox5, Sox6, and Sox13–are key cell fate modulators.Int J Biochem Cell Biol. 2010; 42:429–432. doi: 10.1016/j.biocel.2009.07.016CrossrefMedlineGoogle Scholar
Li A, Ahsen OO, Liu JJ, Du C, McKee ML, Yang Y, Wasco W, Newton-Cheh CH, O’Donnell CJ, Fujimoto JG,. Silencing of the Drosophila ortholog of SOX5 in heart leads to cardiac dysfunction as detected by optical coherence tomography.Hum Mol Genet. 2013; 22:3798–3806. doi: 10.1093/hmg/ddt230CrossrefMedlineGoogle Scholar
Carniel E, Taylor MR, Sinagra G, Di Lenarda A, Ku L, Fain PR, Boucek MM, Cavanaugh J, Miocic S, Slavov D,. Alpha-myosin heavy chain: a sarcomeric gene associated with dilated and hypertrophic phenotypes of cardiomyopathy.Circulation. 2005; 112:54–59. doi: 10.1161/CIRCULATIONAHA.104.507699LinkGoogle Scholar
Holm H, Gudbjartsson DF, Sulem P, Masson G, Helgadottir HT, Zanon C, Magnusson OT, Helgason A, Saemundsdottir J, Gylfason A,. A rare variant in MYH6 is associated with high risk of sick sinus syndrome.Nat Genet. 2011; 43:316–320. doi: 10.1038/ng.781CrossrefMedlineGoogle Scholar
Granados-Riveron JT, Ghosh TK, Pope M, Bu’Lock F, Thornborough C, Eason J, Kirk EP, Fatkin D, Feneley MP, Harvey RP,. Alpha-cardiac myosin heavy chain (MYH6) mutations affecting myofibril formation are associated with congenital heart defects.Hum Mol Genet. 2010; 19:4007–4016. doi: 10.1093/hmg/ddq315CrossrefMedlineGoogle Scholar
Li N, Csepe TA, Hansen BJ, Dobrzynski H, Higgins RS, Kilic A, Mohler PJ, Janssen PM, Rosen MR, Biesiadecki BJ,. Molecular mapping of sinoatrial node HCN channel expression in the human heart.Circ Arrhythm Electrophysiol. 2015; 8:1219–1227. doi: 10.1161/CIRCEP.115.003070LinkGoogle Scholar
Yang T, Atack TC, Stroud DM, Zhang W, Hall L, Roden DM. Blocking Scn10a channels in heart reduces late sodium current and is antiarrhythmic.Circ Res. 2012; 111:322–332. doi: 10.1161/CIRCRESAHA.112.265173LinkGoogle Scholar
Barcellos KS, Bigarella CL, Wagner MV, Vieira KP, Lazarini M, Langford PR, Machado-Neto JA, Call SG, Staley DM, Chung JY,. ARHGAP21 protein, a new partner of α-tubulin involved in cell-cell adhesion formation and essential for epithelial-mesenchymal transition.J Biol Chem. 2013; 288:2179–2189. doi: 10.1074/jbc.M112.432716CrossrefMedlineGoogle Scholar
Fischer-Kešo R, Breuninger S, Hofmann S, Henn M, Röhrig T, Ströbel P, Stoecklin G, Hofmann I. Plakophilins 1 and 3 bind to FXR1 and thereby influence the mRNA stability of desmosomal proteins.Mol Cell Biol. 2014; 34:4244–4256. doi: 10.1128/MCB.00766-14CrossrefMedlineGoogle Scholar
Yi SL, Liu XJ, Zhong JQ, Zhang Y. Role of caveolin-1 in atrial fibrillation as an anti-fibrotic signaling molecule in human atrial fibroblasts.PLoS One. 2014; 9:e85144. doi: 10.1371/journal.pone.0085144CrossrefMedlineGoogle Scholar
Thorolfsdottir RB, Sveinbjornsson G, Sulem P, Nielsen JB, Jonsson S, Halldorsson GH, Melsted P, Ivarsdottir EV, Davidsson OB, Kristjansson RP,. Coding variants in RPL3L and MYZAP increase risk of atrial fibrillation.Commun Biol. 2018; 1:68. doi: 10.1038/s42003-018-0068-9CrossrefMedlineGoogle Scholar
Gonna H, Gallagher MM, Guo XH, Yap YG, Hnatkova K, Camm AJ. P-wave abnormality predicts recurrence of atrial fibrillation after electrical cardioversion: a prospective study.Ann Noninvasive Electrocardiol. 2014; 19:57–62. doi: 10.1111/anec.12087CrossrefMedlineGoogle Scholar
Caldwell J, Koppikar S, Barake W, Redfearn D, Michael K, Simpson C, Hopman W, Baranchuk A. Prolonged P-wave duration is associated with atrial fibrillation recurrence after successful pulmonary vein isolation for paroxysmal atrial fibrillation.J Interv Card Electrophysiol. 2014; 39:131–138. doi: 10.1007/s10840-013-9851-1CrossrefMedlineGoogle Scholar
He J, Tse G, Korantzopoulos P, Letsas KP, Ali-Hasan-Al-Saegh S, Kamel H, Li G, Lip GYH, Liu T. P-wave indices and risk of ischemic stroke: a systematic review and meta-analysis.Stroke. 2017; 48:2066–2072. doi: 10.1161/STROKEAHA.117.017293LinkGoogle Scholar