OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants.
Springer Science and Business Media LLC
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Boudellioua, I., Kulmanov, M., Schofield, P., Gkoutos, G. V., & Hoehndorf, R. (2018). OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants.. Sci Rep, 8 (1), 14681. https://doi.org/10.1038/s41598-018-32876-3
An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, 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 alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, 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 in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.
Humans, Genetic Predisposition to Disease, Computational Biology, Genomics, Multifactorial Inheritance, Genome-Wide Association Study, Genetic Association Studies
King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, FCC/1/1976-08-01, and FCS/1/3657-02-01. H2020-EINFRA (731075) National Science Foundation (IOS:1340112)
External DOI: https://doi.org/10.1038/s41598-018-32876-3
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285619
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/