@article {6084, title = {Novel loci associated with PR interval in a genome-wide association study of 10 African American cohorts.}, journal = {Circ Cardiovasc Genet}, volume = {5}, year = {2012}, month = {2012 Dec}, pages = {639-46}, abstract = {

BACKGROUND: The PR interval, as measured by the resting, standard 12-lead ECG, reflects the duration of atrial/atrioventricular nodal depolarization. Substantial evidence exists for a genetic contribution to PR, including genome-wide association studies that have identified common genetic variants at 9 loci influencing PR in populations of European and Asian descent. However, few studies have examined loci associated with PR in African Americans.

METHODS AND RESULTS: We present results from the largest genome-wide association study to date of PR in 13 415 adults of African descent from 10 cohorts. We tested for association between PR (ms) and ≈2.8 million genotyped and imputed single-nucleotide polymorphisms. Imputation was performed using HapMap 2 YRI and CEU panels. Study-specific results, adjusted for global ancestry and clinical correlates of PR, were meta-analyzed using the inverse variance method. Variation in genome-wide test statistic distributions was noted within studies (λ range: 0.9-1.1), although not after genomic control correction was applied to the overall meta-analysis (λ: 1.008). In addition to generalizing previously reported associations with MEIS1, SCN5A, ARHGAP24, CAV1, and TBX5 to African American populations at the genome-wide significance level (P<5.0 {\texttimes} 10(-8)), we also identified a novel locus: ITGA9, located in a region previously implicated in SCN5A expression. The 3p21 region harboring SCN5A also contained 2 additional independent secondary signals influencing PR (P<5.0 {\texttimes} 10(-8)).

CONCLUSIONS: This study demonstrates the ability to map novel loci in African Americans as well as the generalizability of loci associated with PR across populations of African, European, and Asian descent.

}, keywords = {Adult, African Americans, Cohort Studies, Electrocardiography, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Meta-Analysis as Topic, Middle Aged, Polymorphism, Single Nucleotide}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.112.963991}, author = {Butler, Anne M and Yin, Xiaoyan and Evans, Daniel S and Nalls, Michael A and Smith, Erin N and Tanaka, Toshiko and Li, Guo and Buxbaum, Sarah G and Whitsel, Eric A and Alonso, Alvaro and Arking, Dan E and Benjamin, Emelia J and Berenson, Gerald S and Bis, Josh C and Chen, Wei and Deo, Rajat and Ellinor, Patrick T and Heckbert, Susan R and Heiss, Gerardo and Hsueh, Wen-Chi and Keating, Brendan J and Kerr, Kathleen F and Li, Yun and Limacher, Marian C and Liu, Yongmei and Lubitz, Steven A and Marciante, Kristin D and Mehra, Reena and Meng, Yan A and Newman, Anne B and Newton-Cheh, Christopher and North, Kari E and Palmer, Cameron D and Psaty, Bruce M and Quibrera, P Miguel and Redline, Susan and Reiner, Alex P and Rotter, Jerome I and Schnabel, Renate B and Schork, Nicholas J and Singleton, Andrew B and Smith, J Gustav and Soliman, Elsayed Z and Srinivasan, Sathanur R and Zhang, Zhu-Ming and Zonderman, Alan B and Ferrucci, Luigi and Murray, Sarah S and Evans, Michele K and Sotoodehnia, Nona and Magnani, Jared W and Avery, Christy L} } @article {6602, title = {Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.}, journal = {Stat Med}, volume = {34}, year = {2015}, month = {2015 Jan 15}, pages = {118-30}, abstract = {

Many longitudinal cohort studies have both genome-wide measures of genetic variation and repeated measures of phenotypes and environmental exposures. Genome-wide association study analyses have typically used only cross-sectional data to evaluate quantitative phenotypes and binary traits. Incorporation of repeated measures may increase power to detect associations, but also requires specialized analysis methods. Here, we discuss one such method-generalized estimating equations (GEE)-in the contexts of analysis of main effects of rare genetic variants and analysis of gene-environment interactions. We illustrate the potential for increased power using GEE analyses instead of cross-sectional analyses. We also address challenges that arise, such as the need for small-sample corrections when the minor allele frequency of a genetic variant and/or the prevalence of an environmental exposure is low. To illustrate methods for detection of gene-drug interactions on a genome-wide scale, using repeated measures data, we conduct single-study analyses and meta-analyses across studies in three large cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium-the Atherosclerosis Risk in Communities study, the Cardiovascular Health Study, and the Rotterdam Study.

}, keywords = {Aged, Aging, Cardiovascular Diseases, Cohort Studies, Computer Simulation, Cross-Sectional Studies, Epidemiologic Research Design, Gene-Environment Interaction, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Longitudinal Studies, Meta-Analysis as Topic, Models, Genetic, Pharmacogenetics, Risk Assessment, United States}, issn = {1097-0258}, doi = {10.1002/sim.6323}, author = {Sitlani, Colleen M and Rice, Kenneth M and Lumley, Thomas and McKnight, Barbara and Cupples, L Adrienne and Avery, Christy L and Noordam, Raymond and Stricker, Bruno H C and Whitsel, Eric A and Psaty, Bruce M} }