TITLE

Mayo Genome Consortia: A Genotype-Phenotype Resource for Genome-Wide Association Studies With an Application to the Analysis of Circulating Bilirubin Levels

AUTHOR(S)
Bielinski, Suzette J.; High Seng Chai; Pathak, Jyotishman; Talwalkar, Jayant A.; Limburg, Paul J.; Gullerud, Rachel E.; Sicotte, Hugues; Klee, Eiuc W.; Ross, Jason L.; Kocher, Jean-Pierre A.; Kullo, Ivfikhar J.; Heit, John A.; Petersen, Gloria M.; De Anjrade, Mariza; Chute, Christopher G.
PUB. DATE
July 2011
SOURCE
Mayo Clinic Proceedings;Jul2011, Vol. 86 Issue 7, p606
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
OBJECTIVE: To create a cohort for cost-effective genetic research, the Mayo Genome Consortia (MayoGC) has been assembled with participants from research studies across Mayo Clinic with highthroughput genetic data and electronic medical record (EMR) data for phenotype extraction. PARTICIPANTS AND METHODS: Eligible participants include those who gave general research consent in the contributing studies to share high-throughput genotyping data with other investigators. Herein, we describe the design of the MayoGC, including the current participating cohorts, expansion efforts, data processing, and study management and organization. A genome-wide association study to identify genetic variants associated with total bilirubin levels was conducted to test the genetic research capability of the MayoGC. RESULTS: Genome-wide significant results were observed on 2q37 (top single nucleotide polymorphism, rs4148325; P=5.0 x 10-62) and 12p12 (top single nucleotide polymorphism, rs4363657; P=5.1 × 10-8) corresponding to a gene cluster of uridine 5′-diphospho-glucuronosyltransferases (the UGTL4 cluster) and solute carrier organic anion transporter family, member 1B1 (SLCO1B1), respectively. CONCLUSION: Genome-wide association studies have identified genetic variants associated with numerous phenotypes but have been historically limited by inadequate sample size due to costly genotyping and phenotyping. Large consortia with harmonized genotype data have been assembled to attain sufficient statistical power, but phenotyping remains a rate-limiting factor in gene discovery research efforts. The EMR consists of an abundance of phenotype data that can be extracted in a relatively quick and systematic manner. The MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases.
ACCESSION #
62807419

 

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