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

2019; Nature Portfolio; Volume: 10; Issue: 1 Linguagem: Inglês

10.1038/s41467-019-10649-4

2041-1723

Patrick Deelen, Sipko van Dam, Johanna C. Herkert, Juha Karjalainen, Harm Brugge, Kristin M. Abbott, Cleo C. van Diemen, Paul A. van der Zwaag, Erica H. Gerkes, Evelien Zonneveld‐Huijssoon, Jelkje J. de Boer-Bergsma, Pytrik Folkertsma, Tessa E. 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 J. Sinke, Birgit Sikkema‐Raddatz, Wilhelmina S. Kerstjens‐Frederikse, Morris A. Swertz, Lude Franke,

RNA modifications and cancer

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.