Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes.

Circ Cardiovasc Genet

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.).

Published: April 2017

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; =0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; =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; =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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434456	PMC
http://dx.doi.org/10.1161/CIRCGENETICS.116.001482	DOI Listing

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