title: Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.
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.
authors.affiliation: Department of Medicine, University of Washington, Seattle, WA, U.S.A.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Sitlani
authors.forename: Kenneth M
authors.initials: KM
authors.lastname: Rice
authors.forename: Thomas
authors.initials: T
authors.lastname: Lumley
authors.forename: Barbara
authors.initials: B
authors.lastname: McKnight
authors.forename: L Adrienne
authors.initials: LA
authors.lastname: Cupples
authors.forename: Christy L
authors.initials: CL
authors.lastname: Avery
authors.forename: Raymond
authors.initials: R
authors.lastname: Noordam
authors.forename: Bruno H C
authors.initials: BH
authors.lastname: Stricker
authors.forename: Eric A
authors.initials: EA
authors.lastname: Whitsel
authors.forename: Bruce M
authors.initials: BM
authors.lastname: Psaty
mesh_terms: D000368:Aged; D000375:Aging / Q000235:genetics*; D002318:Cardiovascular Diseases / Q000235:genetics*; D015331:Cohort Studies; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D015340:Epidemiologic Research Design; D059647:Gene-Environment Interaction; D014644:Genetic Variation; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D008137:Longitudinal Studies; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D010597:Pharmacogenetics / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000379:methods; D014481:United States
keywords: GEE; GWAS; gene-environment interaction; longitudinal data; rare genetic variants