Integrative genetic risk prediction using nonparametric empirical Bayes classification

TL;DR

This paper introduces a nonparametric empirical Bayes method for integrating auxiliary genetic data to improve risk prediction of complex diseases, especially when individual data are scarce or heterogeneous.

Contribution

It presents a novel tuning parameter-free approach that uses only summary statistics from auxiliary studies, enhancing prediction accuracy in complex disease genetics.

Findings

Superior predictive accuracy in simulations

Reduces prediction error in pediatric autoimmune disease study

Effective with only auxiliary summary statistics

Abstract

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This paper proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free nonparametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa.