Integrative Genetic Risk Prediction Using Non-Parametric Empirical Bayes Classification

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 parameterfree 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.

Simultaneous estimation of normal means with side information

“…INTRODUCTION rate regression incorporate auxiliary information to improve the power to detect true signals in a primary dataset (Genovese et al, 2006;Ramdas et al, 2017). In the genomic risk prediction literature, Hu et al (2017) and Zhao (2017) showed that summary statistics from previously conducted genome-wide association studies can be used to improve the performance of polygenic risk scores.…”