An expression-directed linear mixed model (edLMM) discovering low-effect genetic variants

Abstract

Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.