Dataset Information

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics.

ABSTRACT:

Background

At least 50% of patients with suspected Mendelian disorders remain undiagnosed after whole-exome sequencing (WES), and the extent to which non-coding variants that are not captured by WES contribute to this fraction is unclear. Whole transcriptome sequencing is a promising supplement to WES, although empirical data on the contribution of RNA analysis to the diagnosis of Mendelian diseases on a large scale are scarce.

Results

Here, we describe our experience with transcript-deleterious variants (TDVs) based on a cohort of 5647 families with suspected Mendelian diseases. We first interrogate all families for which the respective Mendelian phenotype could be mapped to a single locus to obtain an unbiased estimate of the contribution of TDVs at 18.9%. We examine the entire cohort and find that TDVs account for 15% of all "solved" cases. We compare the results of RT-PCR to in silico prediction. Definitive results from RT-PCR are obtained from blood-derived RNA for the overwhelming majority of variants (84.1%), and only a small minority (2.6%) fail analysis on all available RNA sources (blood-, skin fibroblast-, and urine renal epithelial cells-derived), which has important implications for the clinical application of RNA-seq. We also show that RNA analysis can establish the diagnosis in 13.5% of 155 patients who had received "negative" clinical WES reports. Finally, our data suggest a role for TDVs in modulating penetrance even in otherwise highly penetrant Mendelian disorders.

Conclusions

Our results provide much needed empirical data for the impending implementation of diagnostic RNA-seq in conjunction with genome sequencing.

SUBMITTER: Maddirevula S

PROVIDER: S-EPMC7298854 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics.

Maddirevula Sateesh S Kuwahara Hiroyuki H Ewida Nour N Shamseldin Hanan E HE Patel Nisha N Alzahrani Fatema F AlSheddi Tarfa T AlObeid Eman E Alenazi Mona M Alsaif Hessa S HS Alqahtani Maha M AlAli Maha M Al Ali Hatoon H Helaby Rana R Ibrahim Niema N Abdulwahab Firdous F Hashem Mais M Hanna Nadine N Monies Dorota D Derar Nada N Alsagheir Afaf A Alhashem Amal A Alsaleem Badr B Alhebbi Hamoud H Wali Sami S Umarov Ramzan R Gao Xin X Alkuraya Fowzan S FS

Genome biology 20200617 1

<h4>Background</h4>At least 50% of patients with suspected Mendelian disorders remain undiagnosed after whole-exome sequencing (WES), and the extent to which non-coding variants that are not captured by WES contribute to this fraction is unclear. Whole transcriptome sequencing is a promising supplement to WES, although empirical data on the contribution of RNA analysis to the diagnosis of Mendelian diseases on a large scale are scarce.<h4>Results</h4>Here, we describe our experience with transcr ...[more]

PMID: 32552793

Similar Datasets

Project description:To achieve the ambitious targets for tuberculosis (TB) prevention, care, and control stated by the End TB Strategy, new health care strategies, diagnostic tools are warranted. Host-derived biosignatures are explored for their TB diagnostic potential in accordance with the WHO target product profiles (TPPs) for point-of-care (POC) testing. We aimed to identify sputum-independent TB diagnostic signatures in newly diagnosed adult pulmonary-TB (PTB) patients recruited in the context of a prospective household contact cohort study conducted in Andhra Pradesh, India. Whole-blood mRNA samples from 158 subjects (PTB, n = 109; age-matched household controls, n = 49) were examined by dual-color Reverse-Transcriptase Multiplex Ligation-dependent Probe-Amplification (dcRT-MLPA) for the expression of 198 pre-defined genes and a Mesoscale discovery assay for the concentration of 18 cytokines/chemokines in TB-antigen stimulated QuantiFERON supernatants. To identify signatures, we applied a two-step approach; in the first step, univariate filtering was used to identify and shortlist potentially predictive biomarkers; this step may be seen as removing redundant biomarkers. In the second step, a logistic regression approach was used such that group membership (PTB vs. household controls) became the binary response in a Lasso regression model. We identified an 11-gene signature that distinguished PTB from household controls with AUCs of ≥0.98 (95% CIs: 0.94-1.00), and a 4-protein signature (IFNγ, GMCSF, IL7 and IL15) that differentiated PTB from household controls with AUCs of ≥0.87 (95% CIs: 0.75-1.00), in our discovery cohort. Subsequently, we evaluated the performance of the 11-gene signature in two external validation data sets viz, an independent cohort at the Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, UK (GSE107994 data set), and the Catalysis treatment response cohort (GSE89403 data set) from South Africa. The 11-gene signature validated and distinguished PTB from healthy and asymptomatic M. tuberculosis infected household controls in the GSE107994 data set, with an AUC of 0.95 (95% CI: 0.91-0.98) and 0.94 (95% CI: 0.89-0.98). More interestingly in the GSE89403 data set, the 11-gene signature distinguished PTB from household controls and patients with other lung diseases with an AUC of 0.93 (95% CI: 0.87-0.99) and 0.73 (95% CI: 0.56-0.89). These criteria meet the WHO TTP benchmarks for a non-sputum-based triage test for TB diagnosis. We suggest that further validation is required before clinical implementation of the 11-gene signature we have identified markers will be possible.

Project description:BACKGROUND:With the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. To calculate the significance of finding a rare protein-altering variant in a given gene, one must know the frequency of seeing a variant in the general population that is at least as damaging as the variant in question. METHODS:We developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. Based on data from 2504 individuals in the 1000 Genomes Project dataset, we calculated the number of individuals who have a rare variant in a given gene for numerous filtering threshold scenarios, which may be used for calculating the significance of an observed rare variant being causal for disease. Additionally, we calculated mutational burden data on the number of individuals with rare variants in genic regions mapping to protein domains. RESULTS:We describe methods to use the mutational burden data for calculating the significance of observing rare variants in a given proportion of sequenced individuals. We present SORVA, an implementation of these methods as a web tool, and we demonstrate application to 20 relevant but diverse next-gen sequencing studies. Specifically, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and a large-scale study of a complex disorder, autism spectrum disorder. If we use the frequency counts to rank genes based on intolerance for variation, the ranking correlates well with pLI scores derived from the Exome Aggregation Consortium (ExAC) dataset (??=?0.515), with the benefit that the scores are directly interpretable. CONCLUSIONS:We have presented a strategy that is useful for vetting candidate genes from NGS studies and allows researchers to calculate the significance of seeing a variant in a given gene or protein domain. This approach is an important step towards developing a quantitative, statistics-based approach for presenting clinical findings.

Dataset Information

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics.

Background

Results

Conclusions

Publications

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets