A Bayesian linear mixed model for prediction of complex traits.

ABSTRACT:

Motivation

Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance.

Results

We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare va

SUBMITTER: Hai Y 

PROVIDER: S-EPMC8016495 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature