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Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysis

Author

Listed:
  • Patrick Deelen

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    University of Groningen, University Medical Center Groningen, Genomics Coordination Center)

  • Sipko van Dam

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Johanna C. Herkert

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Juha M. Karjalainen

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Harm Brugge

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Kristin M. Abbott

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Cleo C. van Diemen

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Paul A. van der Zwaag

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Erica H. Gerkes

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Evelien Zonneveld-Huijssoon

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Jelkje J. Boer-Bergsma

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Pytrik Folkertsma

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Tessa Gillett

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • K. Joeri van der Velde

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    University of Groningen, University Medical Center Groningen, Genomics Coordination Center)

  • Roan Kanninga

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    University of Groningen, University Medical Center Groningen, Genomics Coordination Center)

  • Peter C. van den Akker

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Sabrina Z. Jan

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Edgar T. Hoorntje

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    Netherlands Heart Institute)

  • Wouter P. te Rijdt

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    Netherlands Heart Institute)

  • Yvonne J. Vos

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Jan D. H. Jongbloed

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Conny M. A. van Ravenswaaij-Arts

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Richard Sinke

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Birgit Sikkema-Raddatz

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Wilhelmina S. Kerstjens-Frederikse

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

  • Morris A. Swertz

    (University of Groningen, University Medical Center Groningen, Department of Genetics
    University of Groningen, University Medical Center Groningen, Genomics Coordination Center)

  • Lude Franke

    (University of Groningen, University Medical Center Groningen, Department of Genetics)

Abstract

The diagnostic yield of exome and genome sequencing remains low (8–70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.

Suggested Citation

  • Patrick Deelen & Sipko van Dam & Johanna C. Herkert & Juha M. Karjalainen & Harm Brugge & Kristin M. Abbott & Cleo C. van Diemen & Paul A. van der Zwaag & Erica H. Gerkes & Evelien Zonneveld-Huijssoon, 2019. "Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysis," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10649-4
    DOI: 10.1038/s41467-019-10649-4
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    Cited by:

    1. Maik Pietzner & Robert Lorenz Chua & Eleanor Wheeler & Katharina Jechow & Julian D. S. Willett & Helena Radbruch & Saskia Trump & Bettina Heidecker & Hugo Zeberg & Frank L. Heppner & Roland Eils & Mar, 2022. "ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Matt C. Danzi & Maike F. Dohrn & Sarah Fazal & Danique Beijer & Adriana P. Rebelo & Vivian Cintra & Stephan Züchner, 2023. "Deep structured learning for variant prioritization in Mendelian diseases," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Andrew D. Grotzinger & Javier de la Fuente & Gail Davies & Michel G. Nivard & Elliot M. Tucker-Drob, 2022. "Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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