Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.
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ABSTRACT:
SUBMITTER: Deelen P
PROVIDER: S-EPMC6599066 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.
Deelen Patrick P van Dam Sipko S Herkert Johanna C JC Karjalainen Juha M JM Brugge Harm H Abbott Kristin M KM van Diemen Cleo C CC van der Zwaag Paul A PA Gerkes Erica H EH Zonneveld-Huijssoon Evelien E Boer-Bergsma Jelkje J JJ Folkertsma Pytrik P Gillett Tessa T van der Velde K Joeri KJ Kanninga Roan R van den Akker Peter C PC Jan Sabrina Z SZ Hoorntje Edgar T ET Te Rijdt Wouter P WP Vos Yvonne J YJ Jongbloed Jan D H JDH van Ravenswaaij-Arts Conny M A CMA Sinke Richard R Sikkema-Raddatz Birgit B Kerstjens-Frederikse Wilhelmina S WS Swertz Morris A MA Franke Lude L
Nature communications 20190628 1
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 fla ...[more]
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