Journal article
MultiPhen: Joint model of multiple phenotypes can increase discovery in GWAS
PF O'Reilly, CJ Hoggart, Y Pomyen, FCF Calboli, P Elliott, MR Jarvelin, LJM Coin
Plos One | PUBLIC LIBRARY SCIENCE | Published : 2012
Open access
Abstract
The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures..
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Awarded by European Commission
Funding Acknowledgements
This work was supported by the European Network for Genetic and Genomic Epidemiology Consortium (grant number P12892 DFHM to PFO), the National Institute for Health Research (NIHR) Imperial College Health Care NHS Trust Biomedical Research Centre (to PFO and PE), the European Union (grant HEALTH-2007-201550 to CJH), the Academy of Finland (project grants 104781, 120315, 129269, 1114194, Center of Excellence in Complex Disease Genetics and SALVE to MRJ), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617 to MRJ), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01 to MRJ), NIH/NIMH (5R01MH63706:02 to MRJ), ENGAGE project and grant agreement HEALTH-F4-2007-201413 (to MRJ), the Medical Research Council, UK (PrevMetSyn/SALVE to MRJ) and the European Community's Seventh Framework Programme (grant number 223367, MultiMod, to LJMC). PE is a NIHR Senior Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.