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

TL;DR

This paper introduces a novel factor-graph model that integrates exome and gene expression data to improve the identification of disease-associated genes, demonstrating superior sensitivity and precision over existing methods.

Contribution

The study presents a new integrative graphical model that combines genetic variants and expression data, enhancing disease gene discovery capabilities.

Findings

Outperforms variant-aggregating and differential expression methods in simulations.

Successfully identified key breast cancer genes with diverse molecular aberrations.

Highlights the importance of data integration for comprehensive disease understanding.

Abstract

Identifying genes associated with complex human diseases is one of the main challenges of human genetics and computational medicine. To answer this question, millions of genetic variants get screened to identify a few of importance. To increase the power of identifying genes associated with diseases and to account for other potential sources of protein function aberrations, we propose a novel factor-graph based model, where much of the biological knowledge is incorporated through factors and priors. Our extensive simulations show that our method has superior sensitivity and precision compared to variant-aggregating and differential expression methods. Our integrative approach was able to identify important genes in breast cancer, identifying genes that had coding aberrations in some patients and regulatory abnormalities in others, emphasizing the importance of data integration to explain the disease in a larger number of patients.