Robust Prediction-Based Analysis for Genome-Wide Association and Expression Studies

Abstract

Here we describe a prediction-based framework to analyze omic data and generate models for both disease diagnosis and identification of cellular pathways which are significant in complex diseases. Our framework differs from previous analysis in its use of underlying biology (cellular pathways/gene-sets) to produce predictive feature-disease models. In our study of alcoholism, lung cancer, and schizophrenia, we demonstrate the framework’s ability to robustly analyze omic data of multiple types and sources, identify significant features sets, and produce accurate predictive models.