Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data

Abstract

We consider the application of Efron's empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by this distribution. Here, we generalize Efron's method to allow for some of the peculiarities of the GAW17 data. In particular, we introduce two ways to extend Efron's model: a weighted empirical Bayes model and a joint covariance model that allows the model to properly incorporate the annotation information of single-nucleotide polymorphisms (SNPs). In the course of our analysis, we examine several aspects of the possible simulation model, including the identity of the most important genes, the differing effects of synonymous and nonsynonymous SNPs, and the relative roles of covariates and genes in conferring disease risk. Finally, we compare the three methods to each other and to other classifiers (random forest and neural network).

Figure 1

ROC curves for the EB, WEB, and JC methods for the prediction model using genes and environmental covariates. The black dotted line is the ROC curve generated from gene and environmental covariates in the prediction model based on the empirical Bayes (EB) method. The blue solid line is the ROC curve from the weighted empirical Bayes (WEB) model. The purple dot-dashed line is the ROC curve from the joint covariance (JM) model. The red dashed line is the diagonal.

Figure 2

ROC curves for the EB, WEB and JC methods for the prediction model using genes only. The black dotted line is the ROC curve generated from the prediction model using genes only, based on the empirical Bayes (EB) method. The blue solid line is the ROC curve from the weighted empirical Bayes (WEB) model. The purple dot-dashed line is the ROC curve from the joint covariance (JC) model. The red dashed line is the diagonal.