Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes

Jonathan D Mosley; M Benjamin Shoemaker; Quinn S Wells; Dawood Darbar; Christian M Shaffer; Todd L Edwards; Lisa Bastarache; Catherine A McCarty; Will Thompson; Christopher G Chute; Gail P Jarvik; David R Crosslin; Eric B Larson; Iftikhar J Kullo; Jennifer A Pacheco; Peggy L Peissig; Murray H Brilliant; James G Linneman; John S Witte; Josh C Denny; Dan M Roden

doi:10.1161/CIRCGENETICS.116.001482

Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes

Circ Cardiovasc Genet. 2017 Apr;10(2):e001482. doi: 10.1161/CIRCGENETICS.116.001482.

Authors

Jonathan D Mosley¹, M Benjamin Shoemaker², Quinn S Wells², Dawood Darbar², Christian M Shaffer², Todd L Edwards², Lisa Bastarache², Catherine A McCarty², Will Thompson², Christopher G Chute², Gail P Jarvik², David R Crosslin², Eric B Larson², Iftikhar J Kullo², Jennifer A Pacheco², Peggy L Peissig², Murray H Brilliant², James G Linneman², John S Witte², Josh C Denny², Dan M Roden²

Affiliations

¹ From the Department of Medicine (J.D.M., M.B.S., Q.S.W., C.M.S., J.C.D., D.M.R.), Vanderbilt Epidemiology Center (T.L.E.), Department of Biomedical Informatics (L.B., J.C.D., D.M.R.), Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Division of Cardiology, University of Illinois at Chicago (D.D.); Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, NorthShore University Health System, Evanston, IL (W.T.); School of Medicine (C.G.C.), School of Public Health (C.G.C.), and School of Nursing (C.G.C.), Johns Hopkins University, Baltimore, MD; Division of Medical Genetics, Department of Medicine (G.P.J.), Department of Genome Sciences (G.P.J.), Department of Biomedical Informatics (D.R.C.), Department of Medical Education (D.R.C.), University of Washington; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); Biomedical Informatics Research Center (P.L.P.), Center for Human Genetics (M.H.B., J.G.L.), Marshfield Clinic Research Foundation, WI; and Department of Epidemiology and Biostatistics, University of California, San Francisco (J.S.W.). [email protected].
² From the Department of Medicine (J.D.M., M.B.S., Q.S.W., C.M.S., J.C.D., D.M.R.), Vanderbilt Epidemiology Center (T.L.E.), Department of Biomedical Informatics (L.B., J.C.D., D.M.R.), Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Division of Cardiology, University of Illinois at Chicago (D.D.); Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, NorthShore University Health System, Evanston, IL (W.T.); School of Medicine (C.G.C.), School of Public Health (C.G.C.), and School of Nursing (C.G.C.), Johns Hopkins University, Baltimore, MD; Division of Medical Genetics, Department of Medicine (G.P.J.), Department of Genome Sciences (G.P.J.), Department of Biomedical Informatics (D.R.C.), Department of Medical Education (D.R.C.), University of Washington; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); Biomedical Informatics Research Center (P.L.P.), Center for Human Genetics (M.H.B., J.G.L.), Marshfield Clinic Research Foundation, WI; and Department of Epidemiology and Biostatistics, University of California, San Francisco (J.S.W.).

Abstract

Background: One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability.

Methods and results: We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P=0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P=0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P=0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]).

Conclusions: The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity.

Keywords: PR interval; atrial fibrillation; biomarker; cardiac electrophysiology; molecular epidemiology; risk factors.

MeSH terms

Adolescent
Adult
Aged
Atrial Fibrillation / diagnostic imaging
Atrial Fibrillation / genetics
Atrial Fibrillation / physiopathology*
Body Mass Index
Case-Control Studies
Electrocardiography*
Female
Genotype
Humans
Male
Metabolic Syndrome / complications
Middle Aged
Odds Ratio
Phenotype
Polymorphism, Single Nucleotide
Risk Factors
Waist Circumference
Young Adult

Abstract

MeSH terms

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