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Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure

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Research in Computational Molecular Biology (RECOMB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9029))

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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Author information

Authors and Affiliations

  1. Bioinformatics IDP, University of California, Los Angeles, USA

    Jong Wha J. Joo & Eleazar Eskin

  2. Computer Science Department, University of California, Los Angeles, USA

    Eun Yong Kang, Nick Furlotte & Eleazar Eskin

  3. Department of Medicine, University of California, Los Angeles, USA

    Elin Org, Brian Parks & Aldons J. Lusis

  4. Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, USA

    Aldons J. Lusis

  5. Department of Human Genetics, University of California, Los Angeles, USA

    Aldons J. Lusis & Eleazar Eskin

Authors
  1. Jong Wha J. Joo
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  2. Eun Yong Kang
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  3. Elin Org
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  4. Nick Furlotte
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  5. Brian Parks
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  6. Aldons J. Lusis
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  7. Eleazar Eskin
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Correspondence to Eleazar Eskin .

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Editors and Affiliations

  1. National Center of Biotechnology Information, Bethesda, Maryland, USA

    Teresa M. Przytycka

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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. https://doi.org/10.1007/978-3-319-16706-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-16706-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16705-3

  • Online ISBN: 978-3-319-16706-0

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