Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.

ABSTRACT:

Motivation

Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (PCs) adjustment to account for population structure and relatedness in high-dimensional penalized models. However, their use in binary trait GWAS rely on the invalid assumption that the residual variance does not depend on the estimated regression coefficients. Moreover, LMMs use a single spectral decomposition of the covariance matrix of the responses, which is no longer possible in generalized linear mixed models (GLMMs).

Results

We introduce a new method called pglmm, a penalize

SUBMITTER: St-Pierre J 

PROVIDER: S-EPMC9907224 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature