Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP

Anja Thormann, Mihail Halachev, William McLaren, David J. Moore, Victoria Svinti, Archie Campbell, Shona M. Kerr, Marc Tischkowitz, Sarah E. Hunt, Malcolm G. Dunlop, Matthew E. Hurles, Caroline F. Wright, Helen V. Firth, Fiona Cunningham () and David R. FitzPatrick ()
Additional contact information
Anja Thormann: Wellcome Genome Campus
Mihail Halachev: MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh
William McLaren: Wellcome Genome Campus
David J. Moore: Western General Hospital
Victoria Svinti: MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh
Archie Campbell: University of Edinburgh
Shona M. Kerr: University of Edinburgh
Marc Tischkowitz: Addenbrooke’s Hospital Cambridge University Hospitals
Sarah E. Hunt: Wellcome Genome Campus
Malcolm G. Dunlop: MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh
Matthew E. Hurles: Wellcome Genome Campus
Caroline F. Wright: University of Exeter Medical School, RILD Level 4, Royal Devon & Exeter Hospital
Helen V. Firth: Addenbrooke’s Hospital Cambridge University Hospitals
Fiona Cunningham: Wellcome Genome Campus
David R. FitzPatrick: MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract We aimed to develop an efficient, flexible and scalable approach to diagnostic genome-wide sequence analysis of genetically heterogeneous clinical presentations. Here we present G2P ( www.ebi.ac.uk/gene2phenotype ) as an online system to establish, curate and distribute datasets for diagnostic variant filtering via association of allelic requirement and mutational consequence at a defined locus with phenotypic terms, confidence level and evidence links. An extension to Ensembl Variant Effect Predictor (VEP), VEP-G2P was used to filter both disease-associated and control whole exome sequence (WES) with Developmental Disorders G2P (G2PDD; 2044 entries). VEP-G2PDD shows a sensitivity/precision of 97.3%/33% for de novo and 81.6%/22.7% for inherited pathogenic genotypes respectively. Many of the missing genotypes are likely false-positive pathogenic assignments. The expected number and discriminative features of background genotypes are defined using control WES. Using only human genetic data VEP-G2P performs well compared to other freely-available diagnostic systems and future phenotypic matching capabilities should further enhance performance.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-10016-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10016-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-10016-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().