The gene, environment association studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions

Marilyn C. Cornelis(Harvard University), Arpana Agrawal(Washington University in St. Louis), John W. Cole(University of Maryland, Baltimore), Nadia N. Hansel(Johns Hopkins University), Kathleen C. Barnes(Johns Hopkins University), Terri H. Beaty(Johns Hopkins University), Siiri Bennett(University of Washington), Laura J. Bierut(Washington University in St. Louis), Eric Boerwinkle(The University of Texas Health Science Center at Houston), Kimberly F. Doheny(Johns Hopkins University), Bjarke Feenstra(Statens Serum Institut), Eleanor Feingold(University of Pittsburgh), Myriam Fornage(The University of Texas Health Science Center at Houston), Christopher A. Haiman(University of Southern California), Emily Harris(National Institutes of Health), M. Geoffrey Hayes(Northwestern University), John A. Heit(Mayo Clinic), Frank B. Hu(Harvard University), Jae H. Kang(Harvard University), Cathy C. Laurie(University of Washington), Hua Ling(Johns Hopkins University), Teri A. Manolio(National Institutes of Health), Mary L. Marazita(University of Pittsburgh), Rasika A. Mathias(Johns Hopkins University), Daniel B. Mirel(Broad Institute), Justin Paschall(National Institutes of Health), Louis R. Pasquale(Harvard University), Elizabeth Pugh(Johns Hopkins University), John P. Rice(Washington University in St. Louis), Jenna Udren(University of Washington), Rob M. van Dam(Harvard University), Xiaojing Wang(University of Pittsburgh), Janey L. Wiggs(Harvard University), Kayleen Williams(University of Washington), Kai Yu(National Institutes of Health)
Genetic Epidemiology
January 20, 2010
10.1002/gepi.20492
Cited by 157

Abstract

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

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