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VEP-G2P: A Tool for Efficient, Flexible and Scalable Diagnostic Filtering of Genomic Variants

Anja Thormann, Mihail Halachev, William McLaren, David J Moore, Victoria Svinti, Archie Campbell, Shona M Kerr, Sarah Hunt, Malcolm G Dunlop, Matthew E Hurles, Caroline F Wright, Helen V Firth, Fiona Cunningham, David R FitzPatrick
doi: https://doi.org/10.1101/416552
Anja Thormann
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Mihail Halachev
2MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
3South East Scotland Regional Genetics Services, Western General Hospital, Edinburgh, UK
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William McLaren
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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David J Moore
3South East Scotland Regional Genetics Services, Western General Hospital, Edinburgh, UK
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Victoria Svinti
2MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
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Archie Campbell
8Centre for Genomic and Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
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Shona M Kerr
6MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
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Sarah Hunt
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Malcolm G Dunlop
2MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
9Edinburgh Cancer Research Centre, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
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Matthew E Hurles
7Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge UK
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Caroline F Wright
4University of Exeter Medical School, RILD Level 4, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
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Helen V Firth
5Clinical Genetic Department, Addenbrooke’s Hospital Cambridge University Hospitals, Cambridge, UK
7Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge UK
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Fiona Cunningham
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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David R FitzPatrick
6MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
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Abstract

Purpose We aimed to develop an efficient, flexible, scalable and evidence-based approach to sequence-based diagnostic analysis/re-analysis of conditions with very large numbers of different causative genes. We then wished to define the expected rate of plausibly causative variants coming through strict filtering in control in comparison to disease populations to quantify background diagnostic “noise”.

Methods We developed G2P (www.ebi.ac.uk/gene2phenotype) as an online system to facilitate the development, validation, curation and distribution of large-scale, evidence-based datasets for use in diagnostic variant filtering. Each locus-genotype-mechanism-disease-evidence thread (LGMDET) associates an allelic requirement and a mutational consequence at a defined locus with a disease entity and a confidence level and evidence links. We then developed an extension to Ensembl Variant Effect Predictor (VEP), VEP-G2P, which can filter based on G2P other widely used gene panel curation systems. We compared the output of disease-associated and control whole exome sequence (WES) using Developmental Disorders G2P (G2PDD; 2044 LGMDETs) and constitutional cancer predisposition G2P (G2PCancer; 128 LGMDETs).

Results We have shown a sensitivity/precision of 97.3%/33% and 81.6%/22.7% for causative de novo and inherited variants respectively using VEP-G2PDD in DDD study probands WES. Many of the apparently diagnostic genotypes “missed” are likely false-positive reports with lower minor allele frequencies and more severe predicted consequences being diagnostically-discriminative features.

Conclusion Case:control comparisons using VEP-G2PDD established an observed:expected ratio of 1:30,000 plausibly causative variants in proband WES to ~1:40,000 reportable but presumed-benign variants in controls. At least half the filtered variants in probands represent background “noise”. Supporting phenotypic evidence is, therefore, necessary in genetically-heterogeneous disorders. G2P and VEP-G2P provides a practical approach to optimize disease-specific filtering parameters in diagnostic genetic research.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 13, 2018.
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VEP-G2P: A Tool for Efficient, Flexible and Scalable Diagnostic Filtering of Genomic Variants
Anja Thormann, Mihail Halachev, William McLaren, David J Moore, Victoria Svinti, Archie Campbell, Shona M Kerr, Sarah Hunt, Malcolm G Dunlop, Matthew E Hurles, Caroline F Wright, Helen V Firth, Fiona Cunningham, David R FitzPatrick
bioRxiv 416552; doi: https://doi.org/10.1101/416552
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VEP-G2P: A Tool for Efficient, Flexible and Scalable Diagnostic Filtering of Genomic Variants
Anja Thormann, Mihail Halachev, William McLaren, David J Moore, Victoria Svinti, Archie Campbell, Shona M Kerr, Sarah Hunt, Malcolm G Dunlop, Matthew E Hurles, Caroline F Wright, Helen V Firth, Fiona Cunningham, David R FitzPatrick
bioRxiv 416552; doi: https://doi.org/10.1101/416552

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