Semantic prioritization of novel causative genomic variants

Research output: Contribution to journalArticle

Authors

  • Imane Boudellioua
  • Rozaimi B Mahamad Razali
  • Maxat Kulmanov
  • Yasmeen Hashish
  • Vladimir B Bajic
  • Eva Goncalves-Serra
  • Nadia Schoenmakers
  • Paul N Schofield
  • Robert Hoehndorf

Colleges, School and Institutes

External organisations

  • King Abdullah University of Science and Technology, Computer, Electrical & Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, Thuwal, Saudi Arabia.
  • Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom.
  • University of Cambridge
  • College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, United Kingdom.

Abstract

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

Details

Original languageEnglish
Article numbere1005500
JournalPLoS Computational Biology
Volume13
Issue number4
Publication statusPublished - 17 Apr 2017

Keywords

  • Journal Article