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, David R. FitzPatrick

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number2373
Number of pages10
JournalNature Communications
Volume10
Issue number2373
DOIs
Publication statusPublished - 30 May 2019

Fingerprint Dive into the research topics of 'Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP'. Together they form a unique fingerprint.

Cite this