A global survey of System Biology-based predictions of gene-rare disease associations to enhance new diagnoses

Abstract

In rare disease diagnosis, described genotype-phenotype associations are evaluated first. In the absence of strong evidence, WES and WGS provide hundred to million other genetic variants, most poorly annotated, that need to be prioritized. While several in silico approaches leverage existing gene-disease knowledge to predict novel associations, doing so in isolation can hide how different genes are represented across other predictions. We hypothesize that a global perspective, accounting for differences in the knowledge accumulated in the gene collections, can refine predictions. Using a network-based algorithm, we explored functional neighborhoods of known disease-associated genes to predict novel candidates for over 200 rare diseases. A global analysis of gene and protein family behavior across predictions identified genes and functions broadly associated with multiple conditions, 192 genes linked to a single disease and 251 genes functionally associated with specific classes of rare diseases. These findings are integrated into a gene-disease specificity score, aimed at enhancing variant prioritization and guiding geneticists in advancing candidate genes toward functional validation.