Christy L Avery1, Christina L Wassel2, Melissa A Richard3, Heather M Highland4, Stephanie Bien5, Niha Zubair5, Elsayed Z Soliman6, Myriam Fornage3, Suzette J Bielinski7, Ran Tao8, Amanda A Seyerle4, Sanjiv J Shah9, Donald M Lloyd-Jones9, Steven Buyske10, Jerome I Rotter11, Wendy S Post12, Stephen S Rich13, Lucia A Hindorff14, Janina M Jeff15, Ralph V Shohet16, Nona Sotoodehnia17, Dan Yu Lin8, Eric A Whitsel18, Ulrike Peters5, Christopher A Haiman19, Dana C Crawford20, Charles Kooperberg5, Kari E North21. 1. Department of Epidemiology; Carolina Population Center. Electronic address: christy_avery@unc.edu. 2. Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Burlington, Vermont. 3. Institute of Molecular Medicine and; Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas. 4. Department of Epidemiology. 5. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. 6. Department of Epidemiology and Prevention, Epidemiological Cardiology Research Center and; Department of Internal Medicine, Section on Cardiology, Wake Forest School of Medicine, Winston Salem, North Carolina. 7. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota. 8. Department of Biostatistics. 9. Department of Preventive Medicine and; Department of Medicine, Northwestern University Feinberg School of Medicine and. 10. Department of Statistics and Biostatistics and; Department of Genetics, Rutgers University, Piscataway, New Jersey. 11. Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California. 12. Department of Medicine and; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland. 13. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia. 14. National Institutes of Health, National Human Genome Research Institute, Office of Population Genomics, Bethesda, Maryland. 15. Genetics and Genomic Sciences, The Charles Bronfman Institute for Personalized Medicine, The Center for Statistical Genetics, and The Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York. 16. Center for Cardiovascular Research, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii. 17. Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, Washington. 18. Department of Epidemiology; Department of Medicine. 19. Department of Preventive Medicine, Keck School of Medicine and; Norris Comprehensive Cancer Center, University of Southern California, Pasadena, California. 20. Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio. 21. Department of Epidemiology; Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Abstract
BACKGROUND: The electrocardiographically measured QT interval (QT) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most QT genetic studies have been performed in European ancestral populations, possibly reducing their global relevance. OBJECTIVE: To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified QT loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry. METHODS: Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis. RESULTS: The 16 fine-mapped QT loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10-5) associated with QT. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with QT independent of previous identified GWAS lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs. CONCLUSION: Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in QT prolongation, that the same loci influence variation in QT across global populations, and that additional, novel, population-specific QT signals exist.
BACKGROUND: The electrocardiographically measured QT interval (QT) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most QT genetic studies have been performed in European ancestral populations, possibly reducing their global relevance. OBJECTIVE: To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified QT loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry. METHODS: Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis. RESULTS: The 16 fine-mapped QT loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10-5) associated with QT. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with QT independent of previous identified GWAS lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs. CONCLUSION: Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in QT prolongation, that the same loci influence variation in QT across global populations, and that additional, novel, population-specific QT signals exist.
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