Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.

ABSTRACT:

Background

Mixed models are used to correct for confounding due to population stratification and hidden relatedness in genome-wide association studies. This class of models includes linear mixed models and generalized linear mixed models. Existing mixed model approaches to correct for population substructure have been previously investigated with both continuous and case-control response variables. However, they have not been investigated in the context of extreme phenotype sampling (EPS), where genetic covariates are only collected on samples having extreme response variable values. In this work, we compare the performance of existing binary trait mixed model approaches (GMMAT, LEAP and CARAT) on EPS data. Since linear mixed models are commonly used even with binary traits, we als

SUBMITTER: Onifade M 

PROVIDER: S-EPMC8815214 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature