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.