TY - JOUR T1 - The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study. JF - Am J Epidemiol Y1 - 2011 A1 - Matise, Tara C A1 - Ambite, Jose Luis A1 - Buyske, Steven A1 - Carlson, Christopher S A1 - Cole, Shelley A A1 - Crawford, Dana C A1 - Haiman, Christopher A A1 - Heiss, Gerardo A1 - Kooperberg, Charles A1 - Marchand, Loic Le A1 - Manolio, Teri A A1 - North, Kari E A1 - Peters, Ulrike A1 - Ritchie, Marylyn D A1 - Hindorff, Lucia A A1 - Haines, Jonathan L KW - Epidemiologic Methods KW - Epidemiologic Research Design KW - Ethnic Groups KW - Genetic Association Studies KW - Genetics, Population KW - Genome-Wide Association Study KW - Humans KW - Interinstitutional Relations KW - Multifactorial Inheritance KW - National Human Genome Research Institute (U.S.) KW - Phenotype KW - Pilot Projects KW - Research Design KW - Risk Factors KW - United States AB -

Genetic studies have identified thousands of variants associated with complex traits. However, most association studies are limited to populations of European descent and a single phenotype. The Population Architecture using Genomics and Epidemiology (PAGE) Study was initiated in 2008 by the National Human Genome Research Institute to investigate the epidemiologic architecture of well-replicated genetic variants associated with complex diseases in several large, ethnically diverse population-based studies. Combining DNA samples and hundreds of phenotypes from multiple cohorts, PAGE is well-suited to address generalization of associations and variability of effects in diverse populations; identify genetic and environmental modifiers; evaluate disease subtypes, intermediate phenotypes, and biomarkers; and investigate associations with novel phenotypes. PAGE investigators harmonize phenotypes across studies where possible and perform coordinated cohort-specific analyses and meta-analyses. PAGE researchers are genotyping thousands of genetic variants in up to 121,000 DNA samples from African-American, white, Hispanic/Latino, Asian/Pacific Islander, and American Indian participants. Initial analyses will focus on single nucleotide polymorphisms (SNPs) associated with obesity, lipids, cardiovascular disease, type 2 diabetes, inflammation, various cancers, and related biomarkers. PAGE SNPs are also assessed for pleiotropy using the "phenome-wide association study" approach, testing each SNP for associations with hundreds of phenotypes. PAGE data will be deposited into the National Center for Biotechnology Information's Database of Genotypes and Phenotypes and made available via a custom browser.

VL - 174 IS - 7 U1 - http://www.ncbi.nlm.nih.gov/pubmed/21836165?dopt=Abstract ER - TY - JOUR T1 - Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. JF - Stat Med Y1 - 2015 A1 - Sitlani, Colleen M A1 - Rice, Kenneth M A1 - Lumley, Thomas A1 - McKnight, Barbara A1 - Cupples, L Adrienne A1 - Avery, Christy L A1 - Noordam, Raymond A1 - Stricker, Bruno H C A1 - Whitsel, Eric A A1 - Psaty, Bruce M KW - Aged KW - Aging KW - Cardiovascular Diseases KW - Cohort Studies KW - Computer Simulation KW - Cross-Sectional Studies KW - Epidemiologic Research Design KW - Gene-Environment Interaction KW - Genetic Variation KW - Genome, Human KW - Genome-Wide Association Study KW - Humans KW - Longitudinal Studies KW - Meta-Analysis as Topic KW - Models, Genetic KW - Pharmacogenetics KW - Risk Assessment KW - United States AB -

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.

VL - 34 IS - 1 U1 - http://www.ncbi.nlm.nih.gov/pubmed/25297442?dopt=Abstract ER -