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Further improvements to linear mixed models for genome-wide association studies

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Item Type:Article
Title:Further improvements to linear mixed models for genome-wide association studies
Creators Name:Widmer, C., Lippert, C., Weissbrod, O., Fusi, N., Kadie, C., Davidson, R., Listgarten, J. and Heckerman, D.
Abstract: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.
Keywords:Algorithms, Genetic Models, Genome-Wide Association Study, Genotype, Linear Models, Phenotype, Single Nucleotide Polymorphism, Software, Animals, Mice
Source:Scientific Reports
ISSN:2045-2322
Publisher:Nature Publishing Group
Volume:4
Page Range:6874
Date:12 November 2014
Official Publication:https://doi.org/10.1038/srep06874
PubMed:View item in PubMed

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