Rare variant effect estimation and polygenic risk prediction

Abstract

AbstractDue to their low frequency, estimating the effect of rare variants is challenging. Here, we propose RareEffect, a method that first estimates gene or region-based heritability and then each variant effect size using an empirical Bayesian approach. Our method uses a variance component model, popular in rare variant tests, and is designed to provide two levels of effect sizes, gene/region-level and variant-level, which can provide better interpretation. To adjust for the case-control imbalance in phenotypes, our approach uses a fast implementation of the Firth bias correction. We demonstrate the accuracy and computational efficiency of our method through extensive simulations and the analysis of UK Biobank whole exome sequencing data for five continuous traits and five binary disease phenotypes. Additionally, we show that the effect sizes obtained from our model can be leveraged to improve the performance of polygenic scores.