Richard J. Sinke, Lude Franke, Patrick Deelen, Sipko van Dam, Edgar T. Hoorntje, Jan D. H. Jongbloed, Roan Kanninga, Juha Karjalainen, Kristin M. Abbott, Wouter P. te Rijdt, Evelien Zonneveld-Huijssoon, Sabrina Z. Jan, Wilhelmina S. Kerstjens-Frederikse, Erica H. Gerkes, Pytrik Folkertsma, Morris A. Swertz, Harm Brugge, Yvonne J. Vos, Johanna C. Herkert, Jelkje J Boer-Bergsma, Peter C. van den Akker, Tessa Gillett, Birgit Sikkema-Raddatz, Conny M. A. van Ravenswaaij-Arts, Cleo C. van Diemen, Paul A. van der Zwaag, K. Joeri van der Velde, Translational Immunology Groningen (TRIGR), Cardiovascular Centre (CVC), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Stem Cell Aging Leukemia and Lymphoma (SALL)
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., 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.