Improved Estimation Of SNP Heritability Using Bayesian Multiple-Phenotype Models

Abstract

Linear mixed models (LMM) are widely used to estimate narrow sense heritability explained by tagged single-nucleotide polymorphisms (SNPs). However, those estimates are valid only if large sample sizes are used. We propose a Bayesian matrix-variate model that takes into account the genetic correlation among phenotypes and genetic correlation among individuals. The use of multivariate Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP-based heritability estimates over univariate and likelihood-based methods, thus explaining why recent progress in eQTL identification has been limited.