Efficient and accurate multiple-phenotypes regression method for high dimensional data considering population structure

Jong Wha J. Joo, Eun Yong Kang, Elin Org, Nick Furlotte, Brian Parks, Aldons J. Lusis, Eleazar Eskin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 19th Annual International Conference, RECOMB 2015, Proceedings
EditorsTeresa M. Przytycka
PublisherSpringer Verlag
Pages136-153
Number of pages18
ISBN (Electronic)9783319167053
DOIs
StatePublished - 2015
Event19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015 - Warsaw, Poland
Duration: 12 Apr 201515 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9029
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015
Country/TerritoryPoland
CityWarsaw
Period12/04/1515/04/15

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