OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

Abstract

ABSTRACTPurposeAn increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole genome sequences.MethodsInformation that links patient phenotypes to databases of gene–phenotype associations observed in clinical research can provide useful information and improve variant prioritization for Mendelian diseases. Additionally, background knowledge about interactions between genes can be utilized to guide and restrict the selection of candidate disease modules.ResultsWe developed OligoPVP, an algorithm that can be used to identify variants in oligogenic diseases and their interactions, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods.ConclusionsOur results show that OligoPVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.