A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project (original) (raw)
Related papers
Splicing in the Diagnosis of Rare Disease: Advances and Challenges
Frontiers in Genetics, 2021
Mutations which affect splicing are significant contributors to rare disease, but are frequently overlooked by diagnostic sequencing pipelines. Greater ascertainment of pathogenic splicing variants will increase diagnostic yields, ending the diagnostic odyssey for patients and families affected by rare disorders, and improving treatment and care strategies. Advances in sequencing technologies, predictive modeling, and understanding of the mechanisms of splicing in recent years pave the way for improved detection and interpretation of splice affecting variants, yet several limitations still prohibit their routine ascertainment in diagnostic testing. This review explores some of these advances in the context of clinical application and discusses challenges to be overcome before these variants are comprehensively and routinely recognized in diagnostics.
Scientific Reports, 2021
The development of computational methods to assess pathogenicity of pre-messenger RNA splicing variants is critical for diagnosis of human disease. We assessed the capability of eight algorithms, and a consensus approach, to prioritize 249 variants of uncertain significance (VUSs) that underwent splicing functional analyses. The capability of algorithms to differentiate VUSs away from the immediate splice site as being ‘pathogenic’ or ‘benign’ is likely to have substantial impact on diagnostic testing. We show that SpliceAI is the best single strategy in this regard, but that combined usage of tools using a weighted approach can increase accuracy further. We incorporated prioritization strategies alongside diagnostic testing for rare disorders. We show that 15% of 2783 referred individuals carry rare variants expected to impact splicing that were not initially identified as ‘pathogenic’ or ‘likely pathogenic’; one in five of these cases could lead to new or refined diagnoses.
The human splicing code reveals new insights into the genetic determinants of disease
Science, 2014
INTRODUCTION: Advancing whole-genome precision medicine requires understanding how gene expression is altered by genetic variants, especially those that are far outside of protein-coding regions. We developed a computational technique that scores how strongly genetic variants affect RNA splicing, a critical step in gene expression whose disruption contributes to many diseases, including cancers and neurological disorders. A genome-wide analysis reveals tens of thousands of variants that alter splicing and are enriched with a wide range of known diseases. Our results provide insight into the genetic basis of spinal muscular atrophy, hereditary nonpolyposis colorectal cancer, and autism spectrum disorder.
RNA splicing in human disease and in the clinic
Clinical Science
Defects at the level of the pre-mRNA splicing process represent a major cause of human disease. Approximately 15–50% of all human disease mutations have been shown to alter functioning of basic and auxiliary splicing elements. These elements are required to ensure proper processing of pre-mRNA splicing molecules, with their disruption leading to misprocessing of the pre-mRNA molecule and disease. The splicing process is a complex process, with much still to be uncovered before we are able to accurately predict whether a reported genomic sequence variant (GV) represents a splicing-associated disease mutation or a harmless polymorphism. Furthermore, even when a mutation is correctly identified as affecting the splicing process, there still remains the difficulty of providing an exact evaluation of the potential impact on disease onset, severity and duration. In this review, we provide a brief overview of splicing diagnostic methodologies, from in silico bioinformatics approaches to we...
Decoding Abnormal Splicing Code in Human Diseases
Journal of Investigative Genomics, 2015
RNA splicing is an intricate process in humans and higher metazoans. Splicing is regulated through multifaceted coordinated factors, such as cis-acting splicing code and RNAbinding splicing trans-factors that associate or compete with ribonucleoproteins (RNPs). Individual cis-acting splicing code and their functional coordination with cognate splicing trans-factors still remain elusive mostgenes, because these code are comprised of highly degenerative short sequence motifs and multiple splicing trans-factors can recognize an identical motif. In addition, a specific splicing motif functions differentially in different genes, which is determined by additional factors such as neighboring sequence context, cell types and association/competition with other splicing trans-factors. Genetic and cellular alterations compromising the fidelity of splicing processes provoke many human diseases. Analyses of abnormal splicing code in human diseases not only uncover the underlying maladies of splicing regulations in pathological conditions, but also allow us to gain insight into splicing mechanisms in physiological conditions. This review introduces accumulating knowledge of numerous modes of splicing aberrations and provides critical information to understand the underlying patho mechanisms of human diseases, which hopefully leads to development of rational therapies.
Translating RNA Splicing Analysis into Diagnosis and Therapy
OBM Genetics
A large proportion of rare disease patients remain undiagnosed and the vast majority of such conditions remain untreatable whether diagnosed or not. RNA splicing analysis is able to increase the diagnostic rate in rare disease by identifying cryptic splicing mutations and can help in interpreting the pathogenicity of genomic variants. Whilst targeted RT-PCR analysis remains a highly sensitive tool for assessing the splicing effects of known variants, RNA-seq can provide a more comprehensive transcriptome-wide analysis of splicing. Appropriate care should be taken in RNA-seq experimental design since sample quality, processing, choice of library preparation and sequencing parameters all introduce variability. Many bioinformatic tools exist to aid both in the prediction of splicing effects from DNA sequence and in the handling of RNA-seq data for splicing analysis. Once identified, splicing abnormalities may be amenable to correction using antisense oligonucleotide compounds by maskin...
International Journal of Molecular Sciences
For more than three decades, researchers have known that consensus splice sites alone are not sufficient regulatory elements to provide complex splicing regulation. Other regulators, so-called splicing regulatory elements (SREs) are needed. Most importantly, their sequence variants often underlie the development of various human disorders. However, due to their variable location and high degeneracy, these regulatory sequences are also very difficult to recognize and predict. Many different approaches aiming to identify SREs have been tried, often leading to the development of in silico prediction tools. While these tools were initially expected to be helpful to identify splicing-affecting mutations in genetic diagnostics, we are still quite far from meeting this goal. In fact, most of these tools are not able to accurately discern the SRE-affecting pathological variants from those not affecting splicing. Nonetheless, several recent evaluations have given appealing results (namely for EX-SKIP, ESRseq and Hexplorer predictors). In this review, we aim to summarize the history of the different approaches to SRE prediction, and provide additional validation of these tools based on patients' clinical data. Finally, we evaluate their usefulness for diagnostic settings and discuss the challenges that have yet to be met.
Alternative splicing and disease
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 2009
Almost all protein-coding genes are spliced and their majority is alternatively spliced. Alternative splicing is a key element in eukaryotic gene expression that increases the coding capacity of the human genome and an increasing number of examples illustrates that the selection of wrong splice sites causes human disease. A fine-tuned balance of factors regulates splice site selection. Here, we discuss well-studied examples that show how a disturbance of this balance can cause human disease. The rapidly emerging knowledge of splicing regulation now allows the development of treatment options.
2021
Simple Summary RNA splicing defects, caused by genetic variants, are a common molecular mechanism of disease. To detect variants that cause splicing impairment, mRNA-based studies must be performed. Classical mRNA assays are time-consuming, which is why we have validated a new reliable straightforward approach to detect normal alternative splicing events and also splicing aberrations. Using our approach, we were able to reclassify three variants of uncertain significance in NBN and STK11 genes, which is of great importance for a proper clinical management of the patients. Abstract RNA sequencing is a promising technique for detecting normal and aberrant RNA isoforms. Here, we present a new single-gene, straightforward 1-day hands-on protocol for detection of splicing alterations with deep RNA sequencing from blood. We have validated our method’s accuracy by detecting previously published normal splicing isoforms of STK11 gene. Additionally, the same technique was used to provide the...
Genomics, Proteomics & Bioinformatics, 2013
Information theory-based methods have been shown to be sensitive and specific for predicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the software predicts variants affecting mRNA splicing. Individual information contents (in bits) of reference and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines (U2OS, U251 and A431), which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6-17 inactivating mutations, 1-5 leaky mutations and 6-13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.