CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses.

Abstract excerpt

As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Computational Biology
• Share to Gene Expression
• Share to Genome