Polygenic Modeling with Bayesian Sparse Linear Mixed Models

TL;DR

This paper introduces a Bayesian sparse linear mixed model (BSLMM) that hybridizes linear mixed models and sparse regression, improving polygenic trait analysis and phenotype prediction in genetics.

Contribution

The paper develops BSLMM, a novel hybrid model with a new MCMC algorithm, enhancing polygenic modeling and phenotype prediction accuracy.

Findings

BSLMM effectively estimates the proportion of variance explained by genotypes.

BSLMM outperforms existing methods in phenotype prediction tasks.

The method is computationally feasible and publicly available.

Abstract

Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given data set one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a "Bayesian sparse linear mixed model" (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters, and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html