Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals...

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Main Authors:	Widmer, Christian, Lippert, Christoph, Weissbrod, Omer, Fusi, Nicolo, Kadie, Carl, Davidson, Robert, Listgarten, Jennifer, Heckerman, David
Format:	Online
Language:	English
Published:	Nature Publishing Group 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230738/

id	pubmed-4230738
recordtype	oai_dc
spelling	pubmed-42307382014-11-17 Further Improvements to Linear Mixed Models for Genome-Wide Association Studies Widmer, Christian Lippert, Christoph Weissbrod, Omer Fusi, Nicolo Kadie, Carl Davidson, Robert Listgarten, Jennifer Heckerman, David Article We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science. Nature Publishing Group 2014-11-12 /pmc/articles/PMC4230738/ /pubmed/25387525 http://dx.doi.org/10.1038/srep06874 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Widmer, Christian Lippert, Christoph Weissbrod, Omer Fusi, Nicolo Kadie, Carl Davidson, Robert Listgarten, Jennifer Heckerman, David
spellingShingle	Widmer, Christian Lippert, Christoph Weissbrod, Omer Fusi, Nicolo Kadie, Carl Davidson, Robert Listgarten, Jennifer Heckerman, David Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
author_facet	Widmer, Christian Lippert, Christoph Weissbrod, Omer Fusi, Nicolo Kadie, Carl Davidson, Robert Listgarten, Jennifer Heckerman, David
author_sort	Widmer, Christian
title	Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
title_short	Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
title_full	Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
title_fullStr	Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
title_full_unstemmed	Further Improvements to Linear Mixed Models for Genome-Wide Association Studies
title_sort	further improvements to linear mixed models for genome-wide association studies
description	We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.
publisher	Nature Publishing Group
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230738/
_version_	1613156191896076288

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

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