Evidence of heterogeneity by race/ethnicity in genetic determinants of QT interval.

Epidemiology 2014 Nov;25(6):790-8

From the aDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; bDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA; cCharles Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY; dCentre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia; eDepartment of Biostatistics, University of Washington, Seattle, WA; fInformation Sciences Institute and Computer Science Department, University of Southern California, Marina Del Rey, CA; gDepartment of Epidemiology, University of Washington, Seattle, WA; hCardiovascular Health Research Unit, University of Washington, Seattle, WA; iGroup Health Research Institute, Group Health Cooperative, Seattle, WA; jOffice of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; kDepartment of Internal Medicine, Ohio State Medical Center, Columbus, OH; lDivision of Cardiology, George Washington University, Washington, DC; mDepartment of Medicine, Weill Cornell Medical College, New York, NY; nDivision of Cardiovascular Medicine, Stanford University, Stanford, CA; oDivision of Medicine, University of Washington, Seattle, WA; pDivision of Health Services, University of Washington, Seattle, WA; qEpidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC; rDepartment of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC; and sDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, TX.

Background: QT interval (QT) prolongation is an established risk factor for ventricular tachyarrhythmia and sudden cardiac death. Previous genome-wide association studies in populations of the European descent have identified multiple genetic loci that influence QT, but few have examined these loci in ethnically diverse populations.

Methods: Here, we examine the direction, magnitude, and precision of effect sizes for 21 previously reported SNPs from 12 QT loci, in populations of European (n = 16,398), African (n = 5,437), American Indian (n = 5,032), Hispanic (n = 1,143), and Asian (n = 932) descent as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. Estimates obtained from linear regression models stratified by race/ethnicity were combined using inverse-variance weighted meta-analysis. Heterogeneity was evaluated using Cochran's Q test.

Results: Of 21 SNPs, 7 showed consistent direction of effect across all 5 populations, and an additional 9 had estimated effects that were consistent across 4 populations. Despite consistent direction of effect, 9 of 16 SNPs had evidence (P < 0.05) of heterogeneity by race/ethnicity. For these 9 SNPs, linkage disequilibrium plots often indicated substantial variation in linkage disequilibrium patterns among the various racial/ethnic groups, as well as possible allelic heterogeneity.

Conclusions: These results emphasize the importance of analyzing racial/ethnic groups separately in genetic studies. Furthermore, they underscore the possible utility of trans-ethnic studies to pinpoint underlying casual variants influencing heritable traits such as QT.

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Source
http://dx.doi.org/10.1097/EDE.0000000000000168DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380285PMC
November 2014
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