Please use this identifier to cite or link to this item: https://doi.org/10.1002/gepi.20564
Title: Phenotype harmonization and cross-study collaboration in GWAS consortia: The GENEVA experience
Authors: Bennett, S.N.
Caporaso, N.
Fitzpatrick, A.L.
Agrawal, A.
Barnes, K.
Boyd, H.A.
Cornelis, M.C.
Hansel, N.N.
Heiss, G.
Heit, J.A.
Kang, J.H.
Kittner, S.J.
Kraft, P.
Lowe, W.
Marazita, M.L.
Monroe, K.R.
Pasquale, L.R.
Ramos, E.M.
van Dam, R.M. 
Udren, J.
Williams, K.
Keywords: Consortia
GENEVA
Genome-wide association studies
Harmonization
Phenotype
Issue Date: Apr-2011
Citation: Bennett, S.N., Caporaso, N., Fitzpatrick, A.L., Agrawal, A., Barnes, K., Boyd, H.A., Cornelis, M.C., Hansel, N.N., Heiss, G., Heit, J.A., Kang, J.H., Kittner, S.J., Kraft, P., Lowe, W., Marazita, M.L., Monroe, K.R., Pasquale, L.R., Ramos, E.M., van Dam, R.M., Udren, J., Williams, K. (2011-04). Phenotype harmonization and cross-study collaboration in GWAS consortia: The GENEVA experience. Genetic Epidemiology 35 (3) : 159-173. ScholarBank@NUS Repository. https://doi.org/10.1002/gepi.20564
Abstract: Genome-wide association study (GWAS) consortia and collaborations formed to detect genetic loci for common phenotypes or investigate gene-environment (GE) interactions are increasingly common. While these consortia effectively increase sample size, phenotype heterogeneity across studies represents a major obstacle that limits successful identification of these associations. Investigators are faced with the challenge of how to harmonize previously collected phenotype data obtained using different data collection instruments which cover topics in varying degrees of detail and over diverse time frames. This process has not been described in detail. We describe here some of the strategies and pitfalls associated with combining phenotype data from varying studies. Using the Gene Environment Association Studies (GENEVA) multi-site GWAS consortium as an example, this paper provides an illustration to guide GWAS consortia through the process of phenotype harmonization and describes key issues that arise when sharing data across disparate studies. GENEVA is unusual in the diversity of disease endpoints and so the issues it faces as its participating studies share data will be informative for many collaborations. Phenotype harmonization requires identifying common phenotypes, determining the feasibility of cross-study analysis for each, preparing common definitions, and applying appropriate algorithms. Other issues to be considered include genotyping timeframes, coordination of parallel efforts by other collaborative groups, analytic approaches, and imputation of genotype data. GENEVA's harmonization efforts and policy of promoting data sharing and collaboration, not only within GENEVA but also with outside collaborations, can provide important guidance to ongoing and new consortia. © 2011 Wiley-Liss, Inc.
Source Title: Genetic Epidemiology
URI: http://scholarbank.nus.edu.sg/handle/10635/109515
ISSN: 07410395
DOI: 10.1002/gepi.20564
Appears in Collections:Staff Publications

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