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Identifying genes associated with complex human diseases is one of the mainchallenges of human genetics and computational medicine. To answer thisquestion, millions of genetic variants get screened to identify a few ofimportance. To increase the power of identifying genes associated with diseasesand to account for other potential sources of protein function aberrations, wepropose a novel factor-graph based model, where much of the biologicalknowledge is incorporated through factors and priors. Our extensive simulationsshow that our method has superior sensitivity and precision compared tovariant-aggregating and differential expression methods. Our integrativeapproach was able to identify important genes in breast cancer, identifyinggenes that had coding aberrations in some patients and regulatory abnormalitiesin others, emphasizing the importance of data integration to explain thedisease in a larger number of patients.

Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms