Targeted RNAseq Improves Clinical Diagnosis of Very Early-Onset Pediatric Immune Dysregulation

Abstract

Despite increased use of whole exome sequencing (WES) for the clinical analysis of rare disease, overall diagnostic yield for most disorders hovers around 30%. Previous studies of mRNA have succeeded in increasing diagnoses for clearly defined disorders of monogenic inheritance. We asked if targeted RNA sequencing could provide similar benefits for primary immunodeficiencies (PIDs) and very early-onset inflammatory bowel disease (VEOIBD), both of which are difficult to diagnose due to high heterogeneity and variable severity. We performed targeted RNA sequencing of a panel of 260 immune-related genes for a cohort of 13 patients (seven suspected PID cases and six VEOIBD) and analyzed variants, splicing, and exon usage. Exonic variants were identified in seven cases, some of which had been previously prioritized by exome sequencing. For four cases, allele specific expression or lack thereof provided additional insights into possible disease mechanisms. In addition, we identified five instances of aberrant splicing associated with four variants. Three of these variants had been previously classified as benign in ClinVar based on population frequency. Digenic or oligogenic inheritance is suggested for at least two patients. In addition to validating the use of targeted RNA sequencing, our results show that rare disease research will benefit from incorporating contributing genetic factors into the diagnostic approach.

A Personalized Metabolic Modelling Approach through Integrated Analysis of RNA-Seq-Based Genomic Variants and Gene Expression Levels in Alzheimer’s Disease

“…This makes RNAseq data unique since both gene expression levels and pathogenic variants can be identified from the same sample (Zhao, 2019). Different studies in the literature have already reported the application of variant discovery from RNAseq data in different diseases (Berger, et al, 2022; Tushir, et al, 2021). However, the use of genomic and transcriptomic information content of RNAseq data together to generate condition-specific genome-scale metabolic models have remained unexplored to date.…”

“…This makes RNAseq data unique since both gene expression levels and pathogenic variants can be identified from the same sample (Zhao, 2019). Different studies in the literature have already reported the application of variant discovery from RNAseq data in different diseases (Berger, et al, 2022;Tushir, et al, 2021).…”