Prioritization of new candidate genes for rare genetic diseases by a disease-aware evaluation of heterogeneous molecular networks

Abstract

The screening for pathogenic variants in the diagnosis of rare genetic diseases can be now performed in all genes due to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the capacity of every type of information to extract relevant insights dependent on the disease. We compiled 33 functional networks classified in 13 knowledge categories (KCs) and observed a high variability in their ability to recover genes associated with 91 genetic diseases, measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs ability to recover genes on a given list associated to any phenotype, and modulates the prediction of new candidates accordingly. A comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in phenotypes of patients with sydromic retinal dystrophies.