Generalized estimating equations for genome‐wide association studies using longitudinal phenotype data
Year: 2014
Type: article
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.
Source: Statistics in Medicine
DOI: 10.1002/sim.6323
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: Cedars-Sinai Medical Center, European Commission, ZonMw, National Institutes of Health, U.S. Public Health Service, National Institute on Aging, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, National Center for Research Resources, National Center for Advancing Translational Sciences
Awards: G1023702118, G1052397333, G1220223050, G1243299691, G125972088, G1332411685, G1345547317, G1462415530, G1527150935, G1691174148, G173303992, G2193588257, G2194386449, G2391884931, G247538147, Exceptional Survival: Trajectories to Functional Aging (CHS All Stars), G2555954497, G2663518188, G270907595, G2800149121, G2818913090, G297113894, G3045517347, G3114902838, G3220743633, UCLA Clinical and Translational Science Institute, G34347772, G3462919067, G3697490566, G3897654783, G395403786, G4032347089, G423252844, G4429312988, G5177458509, G536099884, Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood, G5479317095, G5520316217, Epidemiology of Venous Thrombosis and Pulmonary Embolism, G567847597, G573134505, G581887176, G5870443402, CHS Events Follow-up Study, G6226795708, Institute for Clinical and Translational Research, G6902273418, G716312657, G7176672723, G777822606, G8011864243, G8703710232, G8937114048, G9000090438, G9000325083, From GWAS loci to blood pressure genes, variants & mechanisms - Renewal, G905887895, G931818222
OpenAlex ID: W1963595227