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OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants
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 purpos...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168481/ https://www.ncbi.nlm.nih.gov/pubmed/30279426 http://dx.doi.org/10.1038/s41598-018-32876-3 |
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author | Boudellioua, Imane Kulmanov, Maxat Schofield, Paul N. Gkoutos, Georgios V. Hoehndorf, Robert |
author_facet | Boudellioua, Imane Kulmanov, Maxat Schofield, Paul N. Gkoutos, Georgios V. Hoehndorf, Robert |
author_sort | Boudellioua, Imane |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6168481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61684812018-10-05 OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants Boudellioua, Imane Kulmanov, Maxat Schofield, Paul N. Gkoutos, Georgios V. Hoehndorf, Robert Sci Rep Article 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. Nature Publishing Group UK 2018-10-02 /pmc/articles/PMC6168481/ /pubmed/30279426 http://dx.doi.org/10.1038/s41598-018-32876-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Boudellioua, Imane Kulmanov, Maxat Schofield, Paul N. Gkoutos, Georgios V. Hoehndorf, Robert OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title | OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title_full | OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title_fullStr | OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title_full_unstemmed | OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title_short | OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
title_sort | oligopvp: phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168481/ https://www.ncbi.nlm.nih.gov/pubmed/30279426 http://dx.doi.org/10.1038/s41598-018-32876-3 |
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