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
Conference paper

DOI: 10.1007/978-3-319-16706-0_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)
Cite this paper as:
Joo J.W.J. et al. (2015) Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure. In: Przytycka T. (eds) Research in Computational Molecular Biology. RECOMB 2015. Lecture Notes in Computer Science, vol 9029. Springer, Cham

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jong Wha J. Joo
    • 1
  • Eun Yong Kang
    • 2
  • Elin Org
    • 3
  • Nick Furlotte
    • 2
  • Brian Parks
    • 3
  • Aldons J. Lusis
    • 3
    • 4
    • 5
  • Eleazar Eskin
    • 1
    • 2
    • 5
  1. 1.Bioinformatics IDPUniversity of CaliforniaLos AngelesUSA
  2. 2.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of MedicineUniversity of CaliforniaLos AngelesUSA
  4. 4.Department of Microbiology, Immunology and Molecular GeneticsUniversity of CaliforniaLos AngelesUSA
  5. 5.Department of Human GeneticsUniversity of CaliforniaLos AngelesUSA

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