OligoPVP: phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants
Research output: Contribution to journal › Article
Colleges, School and Institutes
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia.
- University of Cambridge
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, B15 2TT, Birmingham, United Kingdom.
- NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK.
- NIHR Surgical Reconstruction and Microbiology Research Centre, B15 2TT, Birmingham, UK.
- Centre for Liver Research, NIHR Birmingham Liver Biomedical Research Unit, University of Birmingham, Birmingham B15 2TT, UK.
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia. email@example.com.
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.
|Number of pages||8|
|Publication status||Published - 2 Oct 2018|