TY - GEN
T1 - Efficient and accurate multiple-phenotypes regression method for high dimensional data considering population structure
AU - Joo, Jong Wha J.
AU - Kang, Eun Yong
AU - Org, Elin
AU - Furlotte, Nick
AU - Parks, Brian
AU - Lusis, Aldons J.
AU - Eskin, Eleazar
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - A typical GWAS tests correlation between a single phenotype and each genotype one at a time. However, it is often very useful to analyze many phenotypes simultaneously. For example, this may increase the power to detect variants by capturing unmeasured aspects of complex biological networks that a single phenotype might miss. There are several multivariate approaches that try to detect variants related to many phenotypes, but none of them consider population structure and each may result in a significant number of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA, that could both simultaneously analyze many phenotypes as well as correct for population structure. In a simulated study, GAMMA accurately identifies true genetic effects without false positive identifications, while other methods either fail to detect true effects or result in many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mouse and show that GAMMA identifies several variants that are likely to have a true biological mechanism.
AB - A typical GWAS tests correlation between a single phenotype and each genotype one at a time. However, it is often very useful to analyze many phenotypes simultaneously. For example, this may increase the power to detect variants by capturing unmeasured aspects of complex biological networks that a single phenotype might miss. There are several multivariate approaches that try to detect variants related to many phenotypes, but none of them consider population structure and each may result in a significant number of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA, that could both simultaneously analyze many phenotypes as well as correct for population structure. In a simulated study, GAMMA accurately identifies true genetic effects without false positive identifications, while other methods either fail to detect true effects or result in many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mouse and show that GAMMA identifies several variants that are likely to have a true biological mechanism.
UR - http://www.scopus.com/inward/record.url?scp=84926376463&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16706-0_15
DO - 10.1007/978-3-319-16706-0_15
M3 - Conference contribution
AN - SCOPUS:84926376463
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 153
BT - Research in Computational Molecular Biology - 19th Annual International Conference, RECOMB 2015, Proceedings
A2 - Przytycka, Teresa M.
PB - Springer Verlag
T2 - 19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015
Y2 - 12 April 2015 through 15 April 2015
ER -