Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.

Pubmed ID: 25297442

Pubmed Central ID: PMC4321952

Journal: Statistics in medicine

Publication Date: Jan. 15, 2015

Affiliation: Department of Medicine, University of Washington, Seattle, WA, U.S.A.

MeSH Terms: Humans, Aged, Cardiovascular Diseases, United States, Cohort Studies, Aging, Longitudinal Studies, Risk Assessment, Genome-Wide Association Study, Computer Simulation, Cross-Sectional Studies, Models, Genetic, Genome, Human, Genetic Variation, Meta-Analysis as Topic, Epidemiologic Research Design, Gene-Environment Interaction, Pharmacogenetics

Grants: HL080295, HHSN268200800007C, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, HHSN268201200036C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, R01 AG023629, R01 HL080295, U01 HL080295, R56 AG023629, UL1 TR001079, UL1 TR000124, UL1TR000124, AG023629, U01 HG004402, R01 HL059367, UL1RR025005, R01HL59367, UL1 RR025005, R01 HL086694, HHSN268200625226C, R01HL087641, R01 HL087641, R01HL086694, P30 DK063491, R01 HL087652, R01 HL105756, HL105756, R01 HL103612, DK063491, UL1 RR033176, UL1RR033176, HL086752, HHSN268201100005G, HHSN268201100008I, HHSN268201100011I, HHSN268201100005I, HHSN268201100007I

Authors: Stricker BH, Psaty BM, Avery CL, Cupples LA, Lumley T, McKnight B, Sitlani CM, Rice KM, Noordam R, Whitsel EA

Cite As: Sitlani CM, Rice KM, Lumley T, McKnight B, Cupples LA, Avery CL, Noordam R, Stricker BH, Whitsel EA, Psaty BM. Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Stat Med 2015 Jan 15;34(1):118-30. Epub 2014 Oct 9.

Studies:

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.