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

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

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 prioritizatio...

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Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Science
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Acceso en línea:https://doaj.org/article/8256eeb26c5746a9a9e8d6dcd1fd68ae
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Sumario: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.

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