@article{widmer2014further,
author = {Widmer, Christian and Lippert, Christoph and Weissbrod, Omer and Fusi, Nicolo and Kadie, Carl and Davidson, Robert and Listgarten, Jennifer and Heckerman, David},
title = {Further Improvements to Linear Mixed Models for Genome-Wide Association Studies},
year = {2014},
month = {October},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/improvements-linear-mixed-models-genome-wide-association-studies/},
}