Phenotype-driven discovery of digenic variants in personal genome sequences

Identification of variants associated with inherited diseases is a major challenge, in particular in the analysis of clinical sequence data from individual patients. An increasing number of Mendelian diseases have been identified in which two or more variants in multiple genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. Information that links patient phenotypes to databases of gene--phenotype associations observed in clinical and basic research can provide useful information and improve variant prioritization for Mendelian diseases. PhenomeNET is a computational framework that utilized pan-phenomic data from human and non-human model organisms to prioritize candidate genes in genetically based diseases, and we have recently combined PhenomeNET with genome-wide pathogenicity prediction methods into the PhenomeNET Variant Predictor (PVP) that can be used to prioritize variants in inherited diseases. Here, we illustrate extensions to PVP that can be used to identify variants in oligogenic diseases and their interactions. We inserted multiple variants known to be associated with digenic disease into synthetic genomes and find that PVP can identify sets of causative variants in a hypothesis-neutral manner. Our results show that PVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.