Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.
Nat Commun
; 10(1): 2837, 2019 06 28.
Article
in En
| MEDLINE
| ID: mdl-31253775
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Gene Expression Regulation
/
Sequence Analysis, RNA
/
Genetic Predisposition to Disease
/
Transcriptome
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2019
Document type:
Article
Affiliation country:
Netherlands