Increasing diagnostic yield by RNA-Sequencing in rare disease—bypass hurdles of interpreting intronic or splice-altering variants

Abstract Background Whole-exome sequencing (WES) has become an efficient diagnostic test for patients with likely monogenic conditions such as rare idiopathic diseases or sudden unexplained death. Yet, many cases remain undiagnosed. Here, we report the added diagnostic yield achieved for 101 WES cases re-analyzed 1 to 7 years after initial analysis. Methods Of the 101 WES cases, 51 were rare idiopathic disease cases and 50 were postmortem “molecular autopsy” cases of early sudden unexplained death. Variants considered for reporting were prioritized and classified into three groups: (1) diagnostic variants, pathogenic and likely pathogenic variants in genes known to cause the phenotype of interest; (2) possibly diagnostic variants, possibly pathogenic variants in genes known to cause the phenotype of interest or pathogenic variants in genes possibly causing the phenotype of interest; and (3) variants of uncertain diagnostic significance, potentially deleterious variants in genes possibly causing the phenotype of interest. Results Initial analysis revealed diagnostic variants in 13 rare disease cases (25.4%) and 5 sudden death cases (10%). Re-analysis resulted in the identification of additional diagnostic variants in 3 rare disease cases (5.9%) and 1 sudden unexplained death case (2%), which increased our molecular diagnostic yield to 31.4% and 12%, respectively. Conclusions The basis of new findings ranged from improvement in variant classification tools, updated genetic databases, and updated clinical phenotypes. Our findings highlight the potential for re-analysis to reveal diagnostic variants in cases that remain undiagnosed after initial WES.

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Improvement of diagnostic yield in carbamoylphosphate synthetase 1 ( CPS1 ) molecular genetic investigation by RNA sequencing

JIMD Reports ◽

10.1002/jmd2.12091 ◽

2020 ◽

Vol 52 (1) ◽

pp. 28-34

Author(s):

Jasmine Isler ◽

Véronique Rüfenacht ◽

Corinne Gemperle ◽

Gabriella Allegri ◽

Johannes Häberle

Keyword(s):

Rna Sequencing ◽

Diagnostic Yield ◽

Molecular Genetic ◽

Genetic Investigation ◽

Carbamoylphosphate Synthetase

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The Role of RNA-Sequencing as a New Genetic Diagnosis Tool

Current Genetic Medicine Reports ◽

10.1007/s40142-021-00199-x ◽

2021 ◽

Vol 9 (2) ◽

pp. 13-21

Author(s):

Philippa D. K. Curry ◽

Krystyna L. Broda ◽

Christopher J. Carroll

Keyword(s):

Rna Sequencing ◽

Rare Diseases ◽

Diagnostic Yield ◽

Neuromuscular Disorders ◽

Genetic Diagnosis ◽

Rna Seq ◽

Novel Approach ◽

Whole Exome ◽

Diagnosis Tool

Abstract Purpose of Review Whole exome sequencing (WES) and whole-genome sequencing (WGS) are frontline approaches for the genetic diagnosis of rare diseases. However, WES/WGS fails in up to 75% of cases. Transcriptomics via RNA-sequencing (RNA-Seq) is a novel approach that aims to increase the diagnostic yield in rare diseases. Recent Findings Recent publications focus on the success of RNA-Seq for increasing diagnosis rates in WES/WGS-negative patients in up to 36% of cases, across a range of different diseases, sample sizes, and tissue types. Summary RNA-Seq is beneficial for aiding prioritisation of causative variants currently not detected or often overlooked by WES/WGS alone. An improvement in diagnostic yields has been demonstrated using multiple source tissues, with muscle and fibroblasts being the most representative, but the more accessible blood still demonstrating diagnostic success, particularly in neuromuscular disorders. The introduction of RNA-Seq to the genetic diagnosis toolbox promises to be a useful complementary tool to WES/WGS for improving genetic diagnosis in patients with rare disease.

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Detection of pathogenic splicing events from RNA-sequencing data using dasper

10.1101/2021.03.29.437534 ◽

2021 ◽

Author(s):

David Zhang ◽

Regina H. Reynolds ◽

Sonia Garcia-Ruiz ◽

Emil K Gustavsson ◽

Sid Sethi ◽

...

Keyword(s):

Rna Sequencing ◽

Diagnostic Yield ◽

Genetic Diagnosis ◽

Fibroblast Cell ◽

Sequencing Data ◽

Sequencing Technologies ◽

Pathogenic Variants ◽

Splicing Variants ◽

Gene Filter ◽

Disease Associations

AbstractAlthough next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

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Slinker: Visualising novel splicing events in RNA-Seq data

F1000Research ◽

10.12688/f1000research.74836.1 ◽

2021 ◽

Vol 10 ◽

pp. 1255

Author(s):

Breon Schmidt ◽

Marek Cmero ◽

Paul Ekert ◽

Nadia Davidson ◽

Alicia Oshlack

Keyword(s):

Rare Disease ◽

Human Genome ◽

Rna Sequencing ◽

Reference Genome ◽

Data Driven ◽

Rna Seq ◽

Bioinformatics Pipeline ◽

Link Type ◽

Muscular Disorders ◽

Data Driven Approach

Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.

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Diagnosis and analysis of unexplained cases of childhood encephalitis in Australia using metagenomic next-generation sequencing

10.1101/2021.05.10.443367 ◽

2021 ◽

Author(s):

Ci-Xiu Li ◽

Rebecca Burrell ◽

Russell C Dale ◽

Alison Kesson ◽

Christopher C Blyth ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Next Generation Sequencing ◽

Rna Sequencing ◽

Diagnostic Yield ◽

Human Infection ◽

Clinical Samples ◽

Next Generation ◽

Dna Viruses ◽

Immune Mediated ◽

Generation Sequencing

Encephalitis is most often caused by a variety of infectious agents, the identity of which is commonly determined through diagnostic tests utilising cerebrospinal fluid (CSF). Immune-mediated disorders are also a differential in encephalitis cases. We investigated the clinical characteristics and potential aetiological agents of unexplained encephalitis through metagenomic next-generation sequencing of residual clinical samples of multiple tissue types and independent clinical review. A total of 43 specimens, from both sterile and non-sterile sites, were collected from 18 encephalitis cases with no cause identified by the Australian Childhood Encephalitis study. Samples were subjected to total RNA sequencing to determine the presence and abundance of potential pathogens, to reveal mixed infections, pathogen genotypes, and epidemiological origins, and to describe the possible aetiologies of unexplained encephalitis. From this, we identified five RNA and two DNA viruses associated with human infection from both non-sterile (nasopharyngeal aspirates, nose/throat swabs, urine, stool rectal swab) and sterile (cerebrospinal fluid, blood) sites. These comprised two human rhinoviruses, two human seasonal coronaviruses, two polyomaviruses and one picobirnavirus. With the exception of picobirnavirus all have been previously associated with respiratory disease. Human rhinovirus and seasonal coronaviruses may be responsible for five of the encephalitis cases reported here. Immune-mediated encephalitis was considered clinically likely in six cases and RNA sequencing did not identify a possible pathogen in these cases. The aetiology remained unknown in nine cases. Our study emphasises the importance of respiratory viruses in the aetiology of unexplained child encephalitis and suggests that the routine inclusion of non-CNS sampling in encephalitis clinical guidelines/protocols could improve the diagnostic yield.

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Comprehensive use of extended exome analysis improves diagnostic yield in rare disease: a retrospective survey in 1,059 cases

Genetics in Medicine ◽

10.1038/gim.2017.142 ◽

2017 ◽

Vol 20 (3) ◽

pp. 303-312 ◽

Cited By ~ 22

Author(s):

Gaber Bergant ◽

Ales Maver ◽

Luca Lovrecic ◽

Goran Čuturilo ◽

Alenka Hodzic ◽

...

Keyword(s):

Rare Disease ◽

Diagnostic Yield ◽

Retrospective Survey ◽

Exome Analysis

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Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

10.1101/2021.07.18.452855 ◽

2021 ◽

Author(s):

Ruebena Dawes ◽

Himanshu Joshi ◽

Sandra T Cooper

Keyword(s):

Rare Disease ◽

Rna Sequencing ◽

Genetic Variant ◽

Genetic Disorders ◽

Diagnostic Testing ◽

Splice Junction ◽

Empirical Method ◽

Evidence Based ◽

Empirical Prediction ◽

Testing Strategies

Predicting which cryptic-donors may be activated by a genetic variant is notoriously difficult. Through analysis of 5,145 cryptic-donors activated by 4,811 variants (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the authentic-donor appear important determinants of cryptic-donor activation. However, other factors such as auxiliary splicing elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequent mis-splicing events at each exon-intron junction, mined from 40,233 RNA-sequencing samples, predict with remarkable accuracy which cryptic-donor will be activated in rare disease. Aggregate RNA-Sequencing splice-junction data provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

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Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

10.21203/rs.3.rs-731201/v1 ◽

2021 ◽

Author(s):

Ruebena Dawes ◽

Himanshu Joshi ◽

Sandra Cooper

Keyword(s):

Rare Disease ◽

Rna Sequencing ◽

Genetic Variant ◽

Genetic Disorders ◽

Diagnostic Testing ◽

Splice Junction ◽

Empirical Method ◽

Evidence Based ◽

Empirical Prediction ◽

Testing Strategies

Abstract Predicting which cryptic-donors may be activated by a genetic variant is notoriously difficult. Through analysis of 5,145 cryptic-donors activated by 4,811 variants (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the authentic-donor appear important determinants of cryptic-donor activation. However, other factors such as auxiliary splicing elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequent mis-splicing events at each exon-intron junction, mined from 40,233 RNA-sequencing samples, predict with remarkable accuracy which cryptic-donor will be activated in rare disease. Aggregate RNA-Sequencing splice-junction data provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

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MRSD: a novel quantitative approach for assessing suitability of RNA-seq in the clinical investigation of mis-splicing in Mendelian disease

10.1101/2021.03.19.21253973 ◽

2021 ◽

Author(s):

Charlie F. Rowlands ◽

Algy Taylor ◽

Gillian Rice ◽

Nicola Whiffin ◽

Hildegard Nikki Hall ◽

...

Keyword(s):

Rna Sequencing ◽

Diagnostic Yield ◽

Clinical Investigation ◽

Genetic Disorders ◽

Splice Junction ◽

Mendelian Disease ◽

Gene Transcript ◽

Relative Expression Level ◽

Gene Panels ◽

The Impact

Background: RNA-sequencing of patient biosamples is a promising approach to delineate the impact of genomic variants on splicing, but variable gene expression between tissues complicates selection of appropriate tissues. Relative expression level is often used as a metric to predict RNA-sequencing utility. Here, we describe a gene- and tissue-specific metric to inform the feasibility of RNA-sequencing, overcoming some issues with using expression values alone. Results: We derive a novel metric, Minimum Required Sequencing Depth (MRSD), for all genes across three human biosamples (whole blood, lymphoblastoid cell lines (LCLs) and skeletal muscle). MRSD estimates the depth of sequencing required from RNA-sequencing to achieve user-specified sequencing coverage of a gene, transcript or group of genes of interest. MRSD predicts levels of splice junction coverage with high precision (90.1-98.2%) and overcomes transcript region-specific sequencing biases. Applying MRSD scoring to established disease gene panels shows that LCLs are the optimum source of RNA, of the three investigated biosamples, for 69.3% of gene panels. Our approach demonstrates that up to 59.4% of variants of uncertain significance in ClinVar predicted to impact splicing could be functionally assayed by RNA-sequencing in at least one of the investigated biosamples. Conclusions: We demonstrate the power of MRSD as a metric to inform choice of appropriate biosamples for the functional assessment of splicing aberrations. We apply MRSD in the context of Mendelian genetic disorders and illustrate its benefits over expression-based approaches. We anticipate that the integration of MRSD into clinical pipelines will improve variant interpretation and, ultimately, diagnostic yield.

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