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

Author

Listed:
  • Anja Thormann

    (Wellcome Genome Campus)

  • Mihail Halachev

    (MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh
    Western General Hospital)

  • 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
    The University of Edinburgh, Nine Edinburgh BioQuarter)

  • 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
    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
    Wellcome Genome Campus)

  • Fiona Cunningham

    (Wellcome Genome Campus)

  • David R. FitzPatrick

    (MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh)

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.

Suggested Citation

  • 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 & Caro, 2019. "Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10016-3
    DOI: 10.1038/s41467-019-10016-3
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    Cited by:

    1. G. Ball & S. Oldham & V. Kyriakopoulou & L. Z. J. Williams & V. Karolis & A. Price & J. Hutter & M. L. Seal & A. Alexander-Bloch & J. V. Hajnal & A. D. Edwards & E. C. Robinson & J. Seidlitz, 2024. "Molecular signatures of cortical expansion in the human foetal brain," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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