Nature Communications (Jun 2019)
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,
- Lude Franke
Affiliations
- 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
- DOI
- https://doi.org/10.1038/s41467-019-10649-4
- Journal volume & issue
-
Vol. 10,
no. 1
pp. 1 – 13
Abstract
A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.
