scholarly journals Comparison of in Silico Strategies to Prioritize Rare Genomic Variants Impacting RNA Splicing for the Diagnosis of Genomic Disorders

2021 ◽  
Author(s):  
Charlie Rowlands ◽  
Huw B Thomas ◽  
Jenny Lord ◽  
Htoo A Wai ◽  
Gavin Arno ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Messenger Rna ◽  
Rare Variants ◽  
Diagnostic Testing ◽  
Substantial Impact ◽  
Splicing Variants ◽  
Uncertain Significance ◽  
Single Strategy ◽  
Functional Analyses ◽  
Genomic Disorders

Abstract 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 250 variants of uncertain significance (VUSs) that underwent splicing functional analyses. It is the capability of algorithms to differentiate VUSs away from the immediate splice site as being ‘pathogenic’ or ‘benign’ that is likely to have the most 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’; 1 in 5 of these cases could lead to new or refined diagnoses.

scholarly journals Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Charlie Rowlands ◽  
Huw B. Thomas ◽  
Jenny Lord ◽  
Htoo A. Wai ◽  
Gavin Arno ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Messenger Rna ◽  
Rare Variants ◽  
Diagnostic Testing ◽  
Substantial Impact ◽  
Splicing Variants ◽  
Uncertain Significance ◽  
Single Strategy ◽  
Functional Analyses ◽  
Genomic Disorders

AbstractThe 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.

scholarly journals Functional and in-silico interrogation of rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders

2019 ◽  
Author(s):  
Jamie M Ellingford ◽  
Huw B Thomas ◽  
Charlie Rowlands ◽  
Gavin Arno ◽  
Glenda Beaman ◽  
...  
Keyword(s):  
In Silico ◽  
Rna Splicing ◽  
Messenger Rna ◽  
Diagnostic Testing ◽  
Mrna Splicing ◽  
Analysis Framework ◽  
Variant Prioritization ◽  
Monogenic Disorders ◽  
Splicing Mutations ◽  
Uncertain Significance

AbstractPurposeTo develop a comprehensive analysis framework to identify pre-messenger RNA splicing mutations in the context of rare disease.MethodsWe assessed ‘variants of uncertain significance’ through six in-silico prioritization strategies. Firstly, through comparison to functional analyses, we determined the precise effect on splicing of variants identified through clinical multi-disciplinary meetings. Next, we calculated the sensitivity of in-silico prioritization strategies to distinguish known splicing mutations from common variation (>2% in allele frequency in gnomAD) within relevant disease genes. These approaches defined an accurate in-silico strategy for variant prioritization, which we retrospectively applied to a large cohort of 2783 individuals who had previously received genomic testing for rare genomic disorders. We assessed the clinical impact of such prioritization strategies alongside routine diagnostic testing strategies.ResultsWe identified 21 variants that potentially impacted splicing, and used cell based splicing assays to identify those variants which disrupted normal splicing. These findings underpinned new molecular diagnoses for 14 individuals. This process established that the use of pre-defined thresholds from a machine learning splice prediction algorithm, SpliceAI, was the most efficient method for variant prioritization, with a positive predictive value of 86%. We analysed 1,346,744 variants identified through diagnostic testing for 2783 individuals and observed that splicing variant prioritization strategies would improve clarity in clinical analysis for 15% of the individuals surveyed. Prioritized variants could provide new molecular diagnoses or provide additional support for molecular diagnosis for up to 81 individuals within our cohort.ConclusionWe present an in-silico and functional analysis framework for the assessment of variants impacting pre-messenger RNA splicing which is applicable across monogenic disorders. Incorporation of these strategies improves clarity in diagnostic reporting, increases diagnostic yield and, with the advent of targeted treatment strategies, can directly alter patient clinical management.Key HighlightsWe establish an in-silico and functional analysis framework for the incorporation of splice variant assessment into diagnostic testing that is applicable across monogenic disorders.After assessment of six distinct variant prioritization strategies, we concluded that SpliceAI was the best method to accurately identify genomic variation disrupting normal pre-mRNA splicing. We determined this through (i) functional assessment of novel ‘variants of uncertain significance’ described in this study, and (ii) calculation of sensitivity and specificity for prioritization strategies to distinguish known splicing mutations from common variants in the general population.We describe novel disease-causing variants with support from cell based functional assays which underpin autosomal recessive, autosomal dominant and X-linked Mendelian disorders. This includes variants which are deeply intronic, within the nearby splice region of canonical splice sites and variants which activate cryptic splice sites within the protein-coding regions of genes.We integrated the best performing variant prioritization strategy alongside clinical diagnostic testing for 2783 individuals referred to a well-established targeted gene panel test available through the UK National Health Service. We show that integration of such strategies will increase accuracy and clarity of diagnostic reporting, including the identification of variants which could provide new diagnoses and new carrier findings for referred individuals.Functional assessment is essential for accurate clinical assessment of variants disrupting pre-mRNA splicing. We show through cell based functional assessments that variants impacting splicing may have complex impacts on pre-mRNA splicing, which may cause multiple interpretable consequences according to ACMG guidelines.

scholarly journals Predicting RNA splicing from DNA sequence using Pangolin

2021 ◽  
Author(s):  
Tony Zeng ◽  
Yang I Li
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Rna Splicing ◽  
Rare Variants ◽  
Sequence Data ◽  
Learning Approaches ◽  
Loss Of Function ◽  
Pathogenic Variants ◽  
Uncertain Significance ◽  
The Impact

Recent progress in deep learning approaches have greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues that has been trained on RNA splicing and sequence data from four species. Pangolin outperforms state of the art methods for predicting RNA splicing on a variety of prediction tasks. We use Pangolin to study the impact of genetic variants on RNA splicing, including lineage-specific variants and rare variants of uncertain significance. Pangolin predicts loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense (AUPRC = 0.93), demonstrating remarkable potential for identifying pathogenic variants.

scholarly journals Interpretation of BRCA2 Splicing Variants: A Case Series of Challenging Variant Interpretations and the Importance of Functional RNA Analysis

2021 ◽  
Author(s):  
Paola Nix ◽  
Erin Mundt ◽  
Bradford Coffee ◽  
Elizabeth Goossen ◽  
Bryan M. Warf ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Case Series ◽  
Cancer History ◽  
Rna Analysis ◽  
Pathogenic Variants ◽  
Conflicting Evidence ◽  
Increased Risk ◽  
Splicing Variants ◽  
Uncertain Significance ◽  
In Silico Models

AbstractA substantial proportion of pathogenic variants associated with an increased risk of hereditary cancer are sequence variants affecting RNA splicing. The classification of these variants can be complex when both non-functional and functional transcripts are produced from the variant allele. We present four BRCA2 splice site variants with complex variant interpretations (BRCA2 c.68-3T>G, c.68-2A>G, c.425G>T, c.8331+2T>C). Evidence supporting a pathogenic classification is available for each variant, including in silico models, absence in population databases, and published functional data. However, comprehensive RNA analysis showed that some functional transcript may be produced by each variant. BRCA2 c.68-3T>G results in a partial splice defect. For BRCA2 c.68-2A>G and c.425G>T, aberrant splicing was shown to produce a potentially functional, in-frame transcript. BRCA2 c.8331+2T>C may utilize a functional GC donor in place of the wild-type GT donor. The severity of cancer history for carriers of these variants was also assessed using a history weighting algorithm and was not consistent with pathogenic controls (carriers of known pathogenic variants in BRCA2). Due to the conflicting evidence, our laboratory classifies these BRCA2 variants as variants of uncertain significance. This highlights the importance of evaluating new and existing evidence to ensure accurate variant classification and appropriate patient care.

scholarly journals Functional Analyses of HNF1A-MODY Variants Refine the Interpretation of Identified Sequence Variants

2020 ◽  
Vol 105 (4) ◽  
pp. e1377-e1386
Author(s):  
Jana Malikova ◽  
Alba Kaci ◽  
Petra Dusatkova ◽  
Ingvild Aukrust ◽  
Janniche Torsvik ◽  
...  
Keyword(s):  
Dna Binding ◽  
Rare Variants ◽  
Functional Evaluation ◽  
Functional Protein ◽  
Functional Studies ◽  
Uncertain Significance ◽  
Hnf1a Gene ◽  
Nuclear Factor 1 ◽  
Functional Analyses

Abstract Context While rare variants of the hepatocyte nuclear factor-1 alpha (HNF1A) gene can cause maturity-onset diabetes of the young (HNF1A-MODY), other variants can be risk factors for the development of type 2 diabetes. As has been suggested by the American College of Medical Genetics (ACMG) guidelines for variant interpretation, functional studies provide strong evidence to classify a variant as pathogenic. Objective We hypothesized that a functional evaluation can improve the interpretation of the HNF1A variants in our Czech MODY Registry. Design, Settings, and Participants We studied 17 HNF1A variants that were identified in 48 individuals (33 female/15 male) from 20 Czech families with diabetes, using bioinformatics in silico tools and functional protein analyses (transactivation, protein expression, DNA binding, and nuclear localization). Results Of the 17 variants, 12 variants (p.Lys120Glu, p.Gln130Glu, p.Arg131Pro, p.Leu139Pro, p.Met154Ile, p.Gln170Ter, p.Glu187SerfsTer40, p.Phe215SerfsTer18, p.Gly253Arg, p.Leu383ArgfsTer3, p.Gly437Val, and p.Thr563HisfsTer85) exhibited significantly reduced transcriptional activity or DNA binding (< 40%) and were classified as (likely) pathogenic, 2/17 variants were (likely) benign and 3/17 remained of uncertain significance. Functional analyses allowed for the reclassification of 10/17 variants (59%). Diabetes treatment was improved in 20/29 (69%) carriers of (likely) pathogenic HNF1A variants. Conclusion Functional evaluation of the HNF1A variants is necessary to better predict the pathogenic effects and to improve the diagnostic interpretation and treatment, particularly in cases where the cosegregation or family history data are not available or where the phenotype is more diverse and overlaps with other types of diabetes.

scholarly journals Splicing in the Diagnosis of Rare Disease: Advances and Challenges

2021 ◽  
Vol 12 ◽  
Author(s):  
Jenny Lord ◽  
Diana Baralle
Keyword(s):  
Rare Disease ◽  
Clinical Application ◽  
Predictive Modeling ◽  
Diagnostic Testing ◽  
Rare Disorders ◽  
Diagnostic Odyssey ◽  
Sequencing Technologies ◽  
Splicing Variants ◽  
The Way

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.

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