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

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.

Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a master regulator of IFNγ signalling in celiac disease

“…2A, B ). Moreover, by using Genenetwork 34 , we found that the lncRNAs RP3-395M20.9 , AC007278.2 and AC104820.2 may be involved in tumour necrosis factor (TNF) signalling, neutrophil degranulation and chemokine receptor signalling, respectively, implying a role for these uncharacterized lncRNAs in immune regulation in CeD.…”

“…We sought to infer biological function using a guilt-by-association co-regulation approach to identify clusters of shared molecular function (see Methods). We identified co-regulated genes by correlating our prioritized gene list in 1,588 principal components that were identified from the co-expression of 31,499 RNA-seq samples across multiple tissues 34 (Fig. 2A).…”

“…This systematic prioritization approach resulted in 118 prioritized causal genes, including 26 that are direct targets of an approved drug or of drugs under development for other complex diseases, including autoimmune diseases. The co-expression pattern within a large RNA-seq dataset from blood 34 suggests these genes are involved in cytokine signalling in innate and adaptive cells as well as in T cell activation pathways. We also identified TRAFD1 to be trans -regulator of 41 genes, with a strong enrichment in IFNγ signalling and MHC I antigen processing/presentation pathways, which are pivotal for the disease pathogenesis.…”

“…The genes that have been prioritized may have some shared function in CeD pathology. To identify possible shared pathways, we performed co-regulation clustering analysis based on 1,588 normalized expression co-regulation principal components identified from RNA-seq information across multiple human tissues by Deelen et al 34 . We performed pairwise Pearson correlation of our prioritized genes with these 1,588 principal components and derived a correlation Z score for each prioritized gene pair.…”

“…This was complemented with a literature search (research and review papers) in Pubmed. For the coding and non-coding genes for which no studies were found, Genecards (www.genecards.org) and Gene Network v2.0 datasets (www.genenetwork.nl) 34 were used, respectively. Information regarding the potential druggability of the prioritized genes was obtained from DrugBank 37 , the pharmacogenetics database 38 and a previous study that catalogued druggable genes 39 .…”