TY - JOUR
T1 - Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis
AU - Deelen, Patrick
AU - van Dam, Sipko
AU - Herkert, Johanna C
AU - Karjalainen, Juha M
AU - Brugge, Harm
AU - Abbott, Kristin M
AU - van Diemen, Cleo C
AU - van der Zwaag, Paul A
AU - Gerkes, Erica H
AU - Zonneveld-Huijssoon, Evelien
AU - Boer-Bergsma, Jelkje J
AU - Folkertsma, Pytrik
AU - Gillett, Tessa
AU - van der Velde, K Joeri
AU - Kanninga, Roan
AU - van den Akker, Peter C
AU - Jan, Sabrina Z
AU - Hoorntje, Edgar T
AU - Te Rijdt, Wouter P
AU - Vos, Yvonne J
AU - Jongbloed, Jan D H
AU - van Ravenswaaij-Arts, Conny M A
AU - Sinke, Richard
AU - Sikkema-Raddatz, Birgit
AU - Kerstjens-Frederikse, Wilhelmina S
AU - Swertz, Morris A
AU - Franke, Lude
PY - 2019/6/28
Y1 - 2019/6/28
N2 - 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.
AB - 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.
KW - DISEASE
KW - IDENTIFICATION
KW - DISCOVERY
KW - PRIORITIZATION
KW - MUTATIONS
KW - GENOTYPES
KW - VARIANTS
U2 - 10.1038/s41467-019-10649-4
DO - 10.1038/s41467-019-10649-4
M3 - Article
C2 - 31253775
VL - 10
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 2837
ER -