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

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作者
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
机构
[1] Wellcome Genome Campus,European Molecular Biology Laboratory, European Bioinformatics Institute
[2] MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh,South East Scotland Regional Genetics Services
[3] Western General Hospital,Centre for Genomic and Experimental Medicine, Institute of Genetics & Molecular Medicine, Western General Hospital
[4] University of Edinburgh,Usher Institute for Population Health Sciences and Informatics
[5] The University of Edinburgh,Clinical Genetic Department
[6] Nine Edinburgh BioQuarter,Edinburgh Cancer Research Centre, Institute of Genetics & Molecular Medicine, Western General Hospital
[7] Addenbrooke’s Hospital Cambridge University Hospitals,Wellcome Sanger Institute
[8] University of Edinburgh,MRC Human Genetics Unit
[9] Wellcome Genome Campus,undefined
[10] University of Exeter Medical School,undefined
[11] RILD Level 4,undefined
[12] Royal Devon & Exeter Hospital,undefined
[13] MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh,undefined
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摘要
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.
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