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

Authors :
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
Source :
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Publication Year :
2019
Publisher :
Nature Portfolio, 2019.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.8256eeb26c5746a9a9e8d6dcd1fd68ae
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-019-10649-4