• Crawford DC, Goodloe R, Brown-Gentry K, Wilson S, Roberson J, Gillani NB, Ritchie MD, Dilks HH, Bush WS. Characterization of the Metabochip in diverse populations from the International HapMap Project in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 188-99. PMID: 23424124 [PubMed]. PMCID: PMC3584704. NIHMSID: NIHMS433093.

Abstract 

Genome-wide association studies (GWAS) have identified hundreds of genomic regions associated with common human disease and quantitative traits. A major research avenue for mature genotype-phenotype associations is the identification of the true risk or functional variant for downstream molecular studies or personalized medicine applications. As part of the Population Architecture using Genomics and Epidemiology (PAGE) study, we as Epidemiologic Architecture for Genes Linked to Environment (EAGLE) are fine-mapping GWAS-identified genomic regions for common diseases and quantitative traits. We are currently genotyping the Metabochip, a custom content BeadChip designed for fine-mapping metabolic diseases and traits, in∼15,000 DNA samples from patients of African, Hispanic, and Asian ancestry linked to de-identified electronic medical records from the Vanderbilt University biorepository (BioVU). As an initial study of quality control, we report here the genotyping data for 360 samples of European, African, Asian, and Mexican descent from the International HapMap Project. In addition to quality control metrics, we report the overall allele frequency distribution, overall population differentiation (as measured by FST), and linkage disequilibrium patterns for a select GWAS-identified region associated with low-density lipoprotein cholesterol levels to illustrate the utility of the Metabochip for fine-mapping studies in the diverse populations expected in EAGLE, the PAGE study, and other efforts underway designed to characterize the complex genetic architecture underlying common human disease and quantitative traits.