Correcting for population structure and kinship using the linear mixed model: theory and extensions.

ABSTRACT: Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serve

SUBMITTER: Hoffman GE 

PROVIDER: S-EPMC3810480 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature