A computational framework for the prioritization of disease-gene candidates

BACKGROUND: The identification of genes and uncovering the role they play in diseases is an important and complex challenge. Genome-wide linkage and association studies have made advancements in identifying genetic variants that underpin human disease. An important challenge now is to identify meaningful disease-associated genes from a long list of candidate genes implicated by these analyses. The application of gene prioritization can enhance our understanding of disease mechanisms and aid in the discovery of drug targets. The integration of protein-protein interaction networks along with disease datasets and contextual information is an important tool in unraveling the molecular basis of diseases. RESULTS: In this paper we propose a computational pipeline for the prioritization of disease-gene candidates. Diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and topological measures and tissue-specific are integrated. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 32 prioritized genes was generated. This approach correctly identified key AD susceptible genes: PSEN1 and TRAF1. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. Relatively high predictive performance (AUC: 0.70) was observed when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation. CONCLUSIONS: This work provides a foundation for future investigation of diverse heterogeneous data integration for disease-gene prioritization.