| Summary: | 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.
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