Effective Genetic Risk Prediction Using Mixed Models

TL;DR

This paper introduces GeRSI, a novel mixed effects model for genetic risk prediction that outperforms existing methods by effectively handling numerous SNP effects as random, especially for highly polygenic diseases.

Contribution

The paper presents GeRSI, a new statistical approach that improves polygenic risk prediction by using random effects, avoiding the need to estimate individual SNP effects.

Findings

GeRSI outperforms current models in simulation studies.

GeRSI improves AUC for hypertension from 54% to 59%.

GeRSI improves AUC for bipolar disorder from 55% to 62%.

Abstract

To date, efforts to produce high-quality polygenic risk scores from genome-wide studies of common disease have focused on estimating and aggregating the effects of multiple SNPs. Here we propose a novel statistical approach for genetic risk prediction, based on random and mixed effects models. Our approach (termed GeRSI) circumvents the need to estimate the effect sizes of numerous SNPs by treating these effects as random, producing predictions which are consistently superior to current state of the art, as we demonstrate in extensive simulation. When applying GeRSI to seven phenotypes from the WTCCC study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The best existing model yields an AUC of 54%, while GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.