PleioGRiP: Pleiotropic Genetic Risk Prediction via Bayesian model search and classification

Abstract

The program PleioGRiP performs a genome-wide Bayesian model search to identify SNPs associated with a discrete phenotype, and uses SNPs ranked by Bayes factor to produce nested Bayesian classifiers. These classifiers can be used for genetic risk prediction, either selecting the classifier with optimal number of features, or using an ensemble of classifiers. In addition, PleioGRiP implements an extension to the Bayesian search and classification, and can search for pleiotropic relationships in which SNPs are simultaneously associated with two or more distinct phenotypes. These relationships can be used to generate connected Bayesian classifiers to predict the phenotype of interest either using genetic data alone, or in combination with the secondary phenotype(s).