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.

Additional Metadata
Keywords GEE, Gene-environment interaction, GWAS, Longitudinal data, Rare genetic variants
Persistent URL dx.doi.org/10.1002/sim.6323, hdl.handle.net/1765/82563
Journal Statistics in Medicine
Citation
Sitlani, C.M, Rice, K.M, Lumley, T, McKnight, B, Cupples, L.A, Avery, C.L, … Psaty, B.M. (2015). Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Statistics in Medicine, 34(1), 118–130. doi:10.1002/sim.6323