title: Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.
abstract: Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative Population Architecture using Genomics and Epidemiology (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt University's biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.
authors: Department of Biomedical Informatics, Center for Human Genetics Research, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232, USA. william.s.bush@vanderbilt.edu
authors: William S
authors: WS
authors: Bush
authors: Jonathan
authors: J
authors: Boston
authors: Sarah A
authors: SA
authors: Pendergrass
authors: Logan
authors: L
authors: Dumitrescu
authors: Robert
authors: R
authors: Goodloe
authors: Kristin
authors: K
authors: Brown-Gentry
authors: Sarah
authors: S
authors: Wilson
authors: Bob
authors: B
authors: McClellan
authors: Eric
authors: E
authors: Torstenson
authors: Melissa A
authors: MA
authors: Basford
authors: Kylee L
authors: KL
authors: Spencer
authors: Marylyn D
authors: MD
authors: Ritchie
authors: Dana C
authors: DC
authors: Crawford
mesh_terms: D019295:Computational Biology; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies / Q000706:statistics & numerical data*; D005828:Genetics, Population / Q000706:statistics & numerical data; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D009369:Neoplasms / Q000235:genetics; D009749:Nutrition Surveys / Q000706:statistics & numerical data; D020641:Polymorphism, Single Nucleotide; D012042:Registries / Q000706:statistics & numerical data