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

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, Jelkje J. Boer-Bergsma, Pytrik Folkertsma, Tessa Gillett, K. Joeri van der Velde, Roan Kanninga, Peter C. van den Akker, Sabrina Z. Jan, Edgar T. Hoorntje, Wouter P. te Rijdt, Yvonne J. Vos, Jan D. H. Jongbloed, Conny M. A. van Ravenswaaij-Arts, Richard Sinke, Birgit Sikkema-Raddatz, Wilhelmina S. Kerstjens-Frederikse, Morris A. Swertz and Lude Franke ()
Additional contact information
Patrick Deelen: University of Groningen, University Medical Center Groningen, Department of Genetics
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
Roan Kanninga: University of Groningen, University Medical Center Groningen, Department of Genetics
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
Wouter P. te Rijdt: University of Groningen, University Medical Center Groningen, Department of Genetics
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
Lude Franke: University of Groningen, University Medical Center Groningen, Department of Genetics

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-10649-4

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