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Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.
Wednesday,2015-03-04 14:12 America/Los_Angeles — zdrager
| Title | Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. |
| Publication Type | Journal Article |
| Year of Publication | 2015 |
| Authors | Sitlani, CM, Rice, KM, Lumley, T, McKnight, B, Cupples, AL, Avery, CL, Noordam, R, Stricker, BHC, Whitsel, EA, Psaty, BM |
| Journal | Stat Med |
| Volume | 34 |
| Issue | 1 |
| Pagination | 118-30 |
| Date Published | 2015 Jan 15 |
| ISSN | 1097-0258 |
| 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 |
| Abstract | <p>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.</p> |
| DOI | 10.1002/sim.6323 |
| Alternate Journal | Stat Med |
| PubMed ID | 25297442 |
| PubMed Central ID | PMC4321952 |
| Grant List | AG023629 / AG / NIA NIH HHS / United States DK063491 / DK / NIDDK NIH HHS / United States HHSN268200625226C / / PHS HHS / United States HHSN268200800007C / HL / NHLBI NIH HHS / United States HHSN268200800007C / / PHS HHS / United States HHSN268201100005C / HL / NHLBI NIH HHS / United States HHSN268201100005C / / PHS HHS / United States HHSN268201100006C / HL / NHLBI NIH HHS / United States HHSN268201100006C / / PHS HHS / United States HHSN268201100007C / HL / NHLBI NIH HHS / United States HHSN268201100007C / / PHS HHS / United States HHSN268201100008C / HL / NHLBI NIH HHS / United States HHSN268201100008C / / PHS HHS / United States HHSN268201100009C / HL / NHLBI NIH HHS / United States HHSN268201100009C / / PHS HHS / United States HHSN268201100010C / HL / NHLBI NIH HHS / United States HHSN268201100010C / / PHS HHS / United States HHSN268201100011C / HL / NHLBI NIH HHS / United States HHSN268201100011C / / PHS HHS / United States HHSN268201100012C / HL / NHLBI NIH HHS / United States HHSN268201100012C / / PHS HHS / United States HHSN268201200036C / HL / NHLBI NIH HHS / United States HHSN268201200036C / / PHS HHS / United States HL080295 / HL / NHLBI NIH HHS / United States HL086752 / HL / NHLBI NIH HHS / United States HL105756 / HL / NHLBI NIH HHS / United States N01HC55222 / HC / NHLBI NIH HHS / United States N01HC55222 / HL / NHLBI NIH HHS / United States N01HC85079 / HC / NHLBI NIH HHS / United States N01HC85079 / HL / NHLBI NIH HHS / United States N01HC85080 / HC / NHLBI NIH HHS / United States N01HC85080 / HL / NHLBI NIH HHS / United States N01HC85081 / HC / NHLBI NIH HHS / United States N01HC85081 / HL / NHLBI NIH HHS / United States N01HC85082 / HC / NHLBI NIH HHS / United States N01HC85082 / HL / NHLBI NIH HHS / United States N01HC85083 / HC / NHLBI NIH HHS / United States N01HC85083 / HL / NHLBI NIH HHS / United States N01HC85086 / HC / NHLBI NIH HHS / United States N01HC85086 / HL / NHLBI NIH HHS / United States P30 DK063491 / DK / NIDDK NIH HHS / United States R01 HL059367 / HL / NHLBI NIH HHS / United States R01 HL080295 / HL / NHLBI NIH HHS / United States R01 HL086694 / HL / NHLBI NIH HHS / United States R01 HL087641 / HL / NHLBI NIH HHS / United States R01 HL087652 / HL / NHLBI NIH HHS / United States R01 HL103612 / HL / NHLBI NIH HHS / United States R01 HL103612 / HL / NHLBI NIH HHS / United States R01 HL105756 / HL / NHLBI NIH HHS / United States R01HL086694 / HL / NHLBI NIH HHS / United States R01HL087641 / HL / NHLBI NIH HHS / United States R01HL59367 / HL / NHLBI NIH HHS / United States U01 HG004402 / HG / NHGRI NIH HHS / United States UL1 RR025005 / RR / NCRR NIH HHS / United States UL1 RR033176 / RR / NCRR NIH HHS / United States UL1 TR000124 / TR / NCATS NIH HHS / United States UL1 TR001079 / TR / NCATS NIH HHS / United States UL1RR025005 / RR / NCRR NIH HHS / United States UL1RR033176 / RR / NCRR NIH HHS / United States UL1TR000124 / TR / NCATS NIH HHS / United States |