Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation

RNA variant assessment using transactivation and transdifferentiation

Understanding the impact of splicing and nonsense variants on RNA is crucial for the resolution of variant classification as well as their suitability for precision medicine interventions. This is primarily enabled through RNA studies involving transcriptomics followed by targeted assays using RNA isolated from clinically accessible tissues (CATs) such as blood or skin of affected individuals. Insufficient disease gene expression in CATs does however pose a major barrier to RNA based investigations, which we show is relevant to 1,436 Mendelian disease genes. We term these "silent" Mendelian genes (SMGs), the largest portion (36%) of which are associated with neurological disorders. We developed two approaches to induce SMG expression in human dermal fibroblasts (HDFs) to overcome this limitation, including CRISPR-activation-based gene transactivation and fibroblast-to-neuron transdifferentiation. Initial transactivation screens involving 40 SMGs stimulated our development of a highly multiplexed transactivation system culminating in the 6- to 90,000-fold induction of expression of 20/20 (100%) SMGs tested in HDFs. Transdifferentiation of HDFs directly to neurons led to expression of 193/516 (37.4%) of SMGs implicated in neurological disease. The magnitude and isoform diversity of SMG expression following either transactivation or transdifferentiation was comparable to clinically relevant tissues. We apply transdifferentiation and/or gene transactivation combined with short- and long-read RNA sequencing to investigate the impact that variants in USH2A, SCN1A, DMD, and PAK3 have on RNA using HDFs derived from affected individuals. Transactivation and transdifferentiation represent rapid, scalable functional genomic solutions to investigate variants impacting SMGs in the patient cell and genomic context.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Biallelic OTUD6B variants associated with a Kabuki syndrome-like disorder in three siblings: A clinical report and literature review

Biallelic variants in the OTUD6B gene have been reported in the literature in association with an intellectual developmental disorder featuring dysmorphic facies, seizures, and distal limb abnormalities. Physical differences described for affected individuals suggest that the disorder may be clinically recognizable, but previous publications have reported an initial clinical suspicion for Kabuki syndrome (KS) in some affected individuals. Here, we report on three siblings with biallelic variants in OTUD6B co-segregating with neurodevelopmental delay, shared physical differences, and other clinical findings similar to those of previously reported individuals. However, clinical manifestations such as long palpebral fissures, prominent and cupped ears, developmental delay, growth deficiency, persistent fetal fingertip pads, vertebral anomaly, and seizures in the proband were initially suggestive of KS. In addition, previously unreported clinical manifestations such as delayed eruption of primary dentition, soft doughy skin with reduced sweating, and mirror movements present in our patients suggest an expansion of the phenotype, and we perform a literature review to update on current information related to OTUD6B and human gene-disease association.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Splice-Switching Antisense Oligonucleotides Correct Phenylalanine Hydroxylase Exon 11 Skipping Defects and Rescue Enzyme Activity in Phenylketonuria

The PAH gene encodes the hepatic enzyme phenylalanine hydroxylase (PAH), and its deficiency, known as phenylketonuria (PKU), leads to neurotoxic high levels of phenylalanine. PAH exon 11 is weakly defined, and several missense and intronic variants identified in patients affect the splicing process. Recently, we identified a novel intron 11 splicing regulatory element where U1snRNP binds, participating in exon 11 definition. In this work, we describe the implementation of an antisense strategy targeting intron 11 sequences to correct the effect of PAH mis-splicing variants. We used an in vitro assay with minigenes and identified splice-switching antisense oligonucleotides (SSOs) that correct the exon skipping defect of PAH variants c.1199+17G>A, c.1199+20G>C, c.1144T>C, and c.1066-3C>T. To examine the functional rescue induced by the SSOs, we generated a hepatoma cell model with variant c.1199+17G>A using CRISPR/Cas9. The edited cell line reproduces the exon 11 skipping pattern observed from minigenes, leading to reduced PAH protein levels and activity. SSO transfection results in an increase in exon 11 inclusion and corrects PAH deficiency. Our results provide proof of concept of the potential therapeutic use of a single SSO for different exonic and intronic splicing variants causing PAH exon 11 skipping in PKU.

CRISPR activation to characterize splice-altering variants in easily accessible cells

Genetic variants that affect mRNA splicing are a major cause of hereditary disorders, but the spliceogenicity of variants is challenging to predict. RNA diagnostics of clinically accessible tissues enable rapid functional characterization of splice-altering variants within their natural genetic context. However, this analysis cannot be offered to all individuals as one in five human disease genes are not expressed in easily accessible cell types. To overcome this problem, we have used CRISPR activation (CRISPRa) based on a dCas9-VPR mRNA-based delivery platform to induce expression of the gene of interest in skin fibroblasts from individuals with suspected monogenic disorders. Using this ex vivo splicing assay, we characterized the splicing patterns associated with germline variants in the myelin protein zero gene (MPZ), which is exclusively expressed in Schwann cells of the peripheral nerves, and the spastin gene (SPAST), which is predominantly expressed in the central nervous system. After overnight incubation, CRISPRa strongly upregulated MPZ and SPAST transcription in skin fibroblasts, which enabled splice variant profiling using reverse transcription polymerase chain reaction, next-generation sequencing, and long-read sequencing. Our investigations show proof of principle of a promising genetic diagnostic tool that involves CRISPRa to activate gene expression in easily accessible cells to study the functional impact of genetic variants. The procedure is easy to perform in a diagnostic laboratory with equipment and reagents all readily available.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

Prioritizing de novo potential non-canonical splicing variants in neurodevelopmental disorders

BACKGROUND

Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown.

METHODS

We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing.

FINDINGS

We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database.

INTERPRETATION

This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD.

FUNDING

The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Functional evaluation of BRCA1/2 variants of unknown significance with homologous recombination assay and integrative in silico prediction model

Numerous variants of unknown significance (VUSs) exist in hereditary breast and ovarian cancers. Although multiple methods have been developed to assess the significance of BRCA1/2 variants, functional discrepancies among these approaches remain. Therefore, a comprehensive functional evaluation system for these variants should be established. We performed conventional homologous recombination (HR) assays for 50 BRCA1 and 108 BRCA2 VUSs and complementarily predicted VUSs using a statistical logistic regression prediction model that integrated six in silico functional prediction tools. BRCA1/2 VUSs were classified according to the results of the integrative in vitro and in silico analyses. Using HR assays, we identified 10 BRCA1 and 4 BRCA2 VUSs as low-functional pathogenic variants. For in silico prediction, the statistical prediction model showed high accuracy for both BRCA1 and BRCA2 compared with each in silico prediction tool individually and predicted nine BRCA1 and seven BRCA2 variants to be pathogenic. Integrative functional evaluation in this study and the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines strongly suggested that seven BRCA1 variants (p.Glu272Gly, p.Lys1095Glu, p.Val1653Leu, p.Thr1681Pro, p.Phe1761Val, p.Thr1773Ile, and p.Gly1803Ser) and four BRCA2 variants (p.Trp31Gly, p.Ser2616Phe, p.Tyr2660Cys, and p.Leu2792Arg) were pathogenic. This study demonstrates that integrative evaluation using conventional HR assays and optimized in silico prediction comprehensively classified the significance of BRCA VUSs for future clinical applications.

ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants

Background

Interpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies.

Methods

We developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs.

Results

ParSE-seq revealed the splicing effect of 224 SCN5A variants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 unique SCN5A missense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing.

Conclusions

ParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.

Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon

The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

Results

We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

Conclusion

SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Novel noncanonical splice site variant causes mild CHD7-related disorder with variable intrafamilial expressivity

The clinical diagnosis criteria for CHARGE syndrome have been revised several times in the last 25 years. Variable expressivity and reduced penetrance are known, particularly in mild and familial cases. Therefore, it has been proposed to include the detection of a pathogenic CHD7 variant as a major diagnostic criterion. However, intronic variants not located at the canonical splice site are still underrepresented in mutation databases, often because functional analysis is not performed in the diagnostic setting. Here, we report a two-generation family that did not meet the criteria for CHARGE syndrome, until the molecular findings were taken into account. By exome sequencing, we detected an intronic variant in a male individual, who presented with unilateral external ear malformation, bilateral semicircular canal aplasia, polydactyly, vertebral body fusion and a heart defect. The variant was inherited by his mother, who also had bilateral semicircular canal aplasia additionally to unilateral sensorineural hearing impairment, unilateral mandibular palpebral synkinesia, orofacial cleft, and dysphagia. Using RNA studies, we were able to demonstrate that aberrant splicing occurs at an upstream cryptic splice acceptor site, resulting in a frameshift and premature stop of translation. Our data show causality of the noncanonical intronic CHD7 variant and end the diagnostic odyssey of this unsolved phenotype of the family.

RNA analysis of intronic variants in the LAMA2 gene detected by whole genome sequencing confirms a rare dual diagnosis of incontinentia pigmenti with limb-girdle muscular dystrophy

We see that a multiple methods approach to diagnosis remains necessary in the era of whole genome sequencing. We also observe that reproductive risk genetic counseling can be a motivating factor for further testing along the diagnostic odyssey.

A Systematic Method for Detecting Abnormal mRNA Splicing and Assessing Its Clinical Impact in Individuals Undergoing Genetic Testing for Hereditary Cancer Syndromes

Nearly 14% of disease-causing germline variants result from the disruption of mRNA splicing. Most (67%) DNA variants predicted in silico to disrupt splicing are classified as variants of uncertain significance. An analytic workflow-splice effect event resolver (SPEER)-was developed and validated to use mRNA sequencing to reveal significant deviations in splicing, pinpoint the DNA variants potentially involved, and measure the deleterious effects of the altered splicing on mRNA transcripts, providing evidence for assessing the pathogenicity of the variant. SPEER was used to analyze leukocyte RNA encoding 63 hereditary cancer syndrome-related genes in 20,317 patients. Among 3563 patients (17.5%) with at least one DNA variant predicted to affect splicing, 971 (4.8%) had altered splicing with a deleterious effect on the transcript, and 40 had altered splicing due to a DNA variant located outside of the reportable range of the test. Integrating SPEER results into the interpretation of variants allowed variants of uncertain significance to be reclassified as pathogenic or likely pathogenic in 0.4%, and as benign or likely benign in 5.9%, of the 20,317 patients. SPEER-based evidence was associated with a significantly greater effect on classifications of pathogenic or likely pathogenic and benign or likely benign in nonwhite versus non-Hispanic white patients, illustrating that evidence derived from mRNA splicing analysis may help to reduce ethnic/ancestral disparities in genetic testing.

Trio RNA sequencing in a cohort of medically complex children

Genome sequencing (GS) is a powerful test for the diagnosis of rare genetic disorders. Although GS can enumerate most non-coding variation, determining which non-coding variants are disease-causing is challenging. RNA sequencing (RNA-seq) has emerged as an important tool to help address this issue, but its diagnostic utility remains understudied, and the added value of a trio design is unknown. We performed GS plus RNA-seq from blood using an automated clinical-grade high-throughput platform on 97 individuals from 39 families where the proband was a child with unexplained medical complexity. RNA-seq was an effective adjunct test when paired with GS. It enabled clarification of putative splice variants in three families, but it did not reveal variants not already identified by GS analysis. Trio RNA-seq decreased the number of candidates requiring manual review when filtering for de novo dominant disease-causing variants, allowing for the exclusion of 16% of gene-expression outliers and 27% of allele-specific-expression outliers. However, clear diagnostic benefit from the trio design was not observed. Blood-based RNA-seq can facilitate genome analysis in children with suspected undiagnosed genetic disease. In contrast to DNA sequencing, the clinical advantages of a trio RNA-seq design may be more limited.

Web-accessible application for identifying pathogenic transcripts with RNA-seq: Increased sensitivity in diagnosis of neurodevelopmental disorders

For neurodevelopmental disorders (NDDs), a molecular diagnosis is key for management, predicting outcome, and counseling. Often, routine DNA-based tests fail to establish a genetic diagnosis in NDDs. Transcriptome analysis (RNA sequencing [RNA-seq]) promises to improve the diagnostic yield but has not been applied to NDDs in routine diagnostics. Here, we explored the diagnostic potential of RNA-seq in 96 individuals including 67 undiagnosed subjects with NDDs. We performed RNA-seq on single individuals' cultured skin fibroblasts, with and without cycloheximide treatment, and used modified OUTRIDER Z scores to detect gene expression outliers and mis-splicing by exonic and intronic outliers. Analysis was performed by a user-friendly web application, and candidate pathogenic transcriptional events were confirmed by secondary assays. We identified intragenic deletions, monoallelic expression, and pseudoexonic insertions but also synonymous and non-synonymous variants with deleterious effects on transcription, increasing the diagnostic yield for NDDs by 13%. We found that cycloheximide treatment and exonic/intronic Z score analysis increased detection and resolution of aberrant splicing. Importantly, in one individual mis-splicing was found in a candidate gene nearly matching the individual's specific phenotype. However, pathogenic splicing occurred in another neuronal-expressed gene and provided a molecular diagnosis, stressing the need to customize RNA-seq. Lastly, our web browser application allowed custom analysis settings that facilitate diagnostic application and ranked pathogenic transcripts as top candidates. Our results demonstrate that RNA-seq is a complementary method in the genomic diagnosis of NDDs and, by providing accessible analysis with improved sensitivity, our transcriptome analysis approach facilitates wider implementation of RNA-seq in routine genome diagnostics.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum

While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.

Mutational and splicing landscape in a cohort of 43,000 patients tested for hereditary cancer

DNA germline genetic testing can identify individuals with cancer susceptibility. However, DNA sequencing alone is limited in its detection and classification of mRNA splicing variants, particularly those located far from coding sequences. Here we address the limitations of splicing variant identification and interpretation by pairing DNA and RNA sequencing and describe the mutational and splicing landscape in a clinical cohort of 43,524 individuals undergoing genetic testing for hereditary cancer predisposition.

Diagnostic yield and clinical relevance of expanded genetic testing for cancer patients

BACKGROUND

Genetic testing (GT) for hereditary cancer predisposition is traditionally performed on selected genes based on established guidelines for each cancer type. Recently, expanded GT (eGT) using large hereditary cancer gene panels uncovered hereditary predisposition in a greater proportion of patients than previously anticipated. We sought to define the diagnostic yield of eGT and its clinical relevance in a broad cancer patient population over a 5-year period.

METHODS

A total of 17,523 cancer patients with a broad range of solid tumors, who received eGT at Memorial Sloan Kettering Cancer Center between July 2015 to April 2020, were included in the study. The patients were unselected for current GT criteria such as cancer type, age of onset, and/or family history of disease. The diagnostic yield of eGT was determined for each cancer type. For 9187 patients with five common cancer types frequently interrogated for hereditary predisposition (breast, colorectal, ovarian, pancreatic, and prostate cancer), the rate of pathogenic/likely pathogenic (P/LP) variants in genes that have been associated with each cancer type was analyzed. The clinical implications of additional findings in genes not known to be associated with a patients' cancer type were investigated.

RESULTS

16.7% of patients in a broad cancer cohort had P/LP variants in hereditary cancer predisposition genes identified by eGT. The diagnostic yield of eGT in patients with breast, colorectal, ovarian, pancreatic, and prostate cancer was 17.5%, 15.3%, 24.2%, 19.4%, and 15.9%, respectively. Additionally, 8% of the patients with five common cancers had P/LP variants in genes not known to be associated with the patient's current cancer type, with 0.8% of them having such a variant that confers a high risk for another cancer type. Analysis of clinical and family histories revealed that 74% of patients with variants in genes not associated with their current cancer type but which conferred a high risk for another cancer did not meet the current GT criteria for the genes harboring these variants. One or more variants of uncertain significance were identified in 57% of the patients.

CONCLUSIONS

Compared to targeted testing approaches, eGT can increase the yield of detection of hereditary cancer predisposition in patients with a range of tumors, allowing opportunities for enhanced surveillance and intervention. The benefits of performing eGT should be weighed against the added number of VUSs identified with this approach.

Alternatively Spliced Isoforms of the P2X7 Receptor: Structure, Function and Disease Associations

The P2X7 receptor (P2X7R) is an ATP-gated membrane ion channel that is expressed by multiple cell types. Following activation by extracellular ATP, the P2X7R mediates a broad range of cellular responses including cytokine and chemokine release, cell survival and differentiation, the activation of transcription factors, and apoptosis. The P2X7R is made up of three P2X7 subunits that contain specific domains essential for the receptor’s varied functions. Alternative splicing produces P2X7 isoforms that exclude one or more of these domains and assemble in combinations that alter P2X7R function. The modification of the structure and function of the P2X7R may adversely affect cellular responses to carcinogens and pathogens, and alternatively spliced (AS) P2X7 isoforms have been associated with several cancers. This review summarizes recent advances in understanding the structure and function of AS P2X7 isoforms and their associations with cancer and potential role in modulating the inflammatory response.

Antisense Oligonucleotide Rescue of Deep-Intronic Variants Activating Pseudoexons in the 6-Pyruvoyl-Tetrahydropterin Synthase Gene

We report two new 6-pyruvoyl-tetrahydropterin synthase splicing variants identified through genomic sequencing and transcript analysis in a patient with tetrahydrobiopterin deficiency, presenting with hyperphenylalaninemia and monoamine neurotransmitter deficiency. Variant c.243 + 3A>G causes exon 4 skipping. The deep-intronic c.164-672C>T variant creates a potential 5' splice site that leads to the inclusion of four overlapping pseudoexons, corresponding to exonizations of an antisense short interspersed nuclear element AluSq repeat sequence. Two of the identified pseudoexons have been reported previously, activated by different deep-intronic variants, and were also detected at residual levels in control cells. Interestingly, the predominant pseudoexon is nearly identical to a disease causing activated pseudoexon in the F8 gene, with the same 3' and 5' splice sites. Splice switching antisense oligonucleotides (SSOs) were designed to hybridize with splice sites and/or predicted binding sites for regulatory splice factors. Different SSOs corrected the aberrant pseudoexon inclusion, both in minigenes and in fibroblasts from patients carrying the new variant c.164-672C>T or the previously described c.164-716A>T. With SSO treatment PTPS protein was recovered, illustrating the therapeutic potential of the approach, for patients with different pseudoexon activating variants in the region. In addition, the natural presence of pseudoexons in the wild type context suggests the possibility of applying the antisense strategy in patients with hypomorphic PTS variants with the purpose of upregulating their expression to increase overall protein and activity.

Minigene Splicing Assays Identify 20 Spliceogenic Variants of the Breast/Ovarian Cancer Susceptibility Gene RAD51C

RAD51C loss-of-function variants are associated with an increased risk of breast and ovarian cancers. Likewise, splicing disruptions are a frequent mechanism of gene inactivation. Taking advantage of a previous splicing-reporter minigene with exons 2-8 (mgR51C_ex2-8), we proceeded to check its impact on the splicing of candidate ClinVar variants. A total of 141 RAD51C variants at the intron/exon boundaries were analyzed with MaxEntScan. Twenty variants were selected and genetically engineered into the wild-type minigene. All the variants disrupted splicing, and 18 induced major splicing anomalies without any trace or minimal amounts (<2.4%) of the minigene full-length (FL) transcript. Twenty-seven transcripts (including the wild-type and r.904A FL transcripts) were identified by fluorescent fragment electrophoresis; of these, 14 were predicted to truncate the RAD51C protein, 3 kept the reading frame, and 8 minor isoforms (1.1−4.7% of the overall expression) could not be characterized. Finally, we performed a tentative interpretation of the variants according to an ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based classification scheme, classifying 16 variants as likely pathogenic. Minigene assays have been proven as valuable tools for the initial characterization of potential spliceogenic variants. Hence, minigene mgR51C_ex2-8 provided useful splicing data for 40 RAD51C variants.

CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites

Background

It is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential to improve prediction of splice disrupting variants. The recently published SpliceAI algorithm utilises deep neural networks and has been reported to have a greater accuracy than other commonly used methods.

Methods and findings

The original SpliceAI was trained on splice sites included in primary isoforms combined with novel junctions observed in GTEx data, which might introduce noise and de-correlate the machine learning input with its output. Limiting the data to only validated and manual annotated primary and alternatively spliced GENCODE sites in training may improve predictive abilities. All of these gene isoforms were collapsed (aggregated into one pseudo-isoform) and the SpliceAI architecture was retrained (CI-SpliceAI). Predictive performance on a newly curated dataset of 1,316 functionally validated variants from the literature was compared with the original SpliceAI, alongside MMSplice, MaxEntScan, and SQUIRLS. Both SpliceAI algorithms outperformed the other methods, with the original SpliceAI achieving an accuracy of ∼91%, and CI-SpliceAI showing an improvement at ∼92% overall. Predictive accuracy increased in the majority of curated variants.

Conclusions

We show that including only manually annotated alternatively spliced sites in training data improves prediction of clinically relevant variants, and highlight avenues for further performance improvements.

Clinical implementation of RNA sequencing for Mendelian disease diagnostics

BACKGROUND

Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics.

METHODS

We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage.

RESULTS

We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions.

CONCLUSION

Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

Empirical prediction of variant-activated cryptic splice donors using population-based RNA-Seq data

Predicting which cryptic-donors may be activated by a splicing variant in patient DNA is notoriously difficult. Through analysis of 5145 cryptic-donors (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 annotated-donor appear important determinants of cryptic-donor activation. However, other factors such as splicing regulatory elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequently recurring natural mis-splicing events at each exon-intron junction, summarised over 40,233 RNA-sequencing samples (40K-RNA), predict with accuracy which cryptic-donor will be activated in rare disease. 40K-RNA 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.

Genetic variants affecting the consensus splicing motifs can alter binding of spliceosomal components and induce mis-splicing. Here, the authors develop a method, showing that ranking the most common recurring mis-splicing events in public RNA-Seq data can predict the activation of cryptic-donors.

Possible Catch-Up Developmental Trajectories for Children with Mild Developmental Delay Caused by NAA15 Pathogenic Variants

Variants in NAA15 are closely related to neurodevelopmental disorders (NDDs). In this study, we investigated the spectrum and clinical features of NAA15 variants in a Chinese NDD cohort of 769 children. Four novel NAA15 pathogenic variants were detected by whole-exome sequencing, including three de novo variants and one maternal variant. The in vitro minigene splicing assay confirmed one noncanonical splicing variant (c.1410+5G>C), which resulted in abnormal mRNA splicing. All affected children presented mild developmental delay, and catch-up trajectories were noted in three patients based on their developmental scores at different ages. Meanwhile, the literature review also showed that half of the reported patients with NAA15 variants presented mild/moderate developmental delay or intellectual disability, and possible catch-up sign was indicated for three affected patients. Taken together, our study expanded the spectrum of NAA15 variants in NDD patients. The affected patients presented mild developmental delay, and possible catch-up developmental trajectories were suggested. Studying the natural neurodevelopmental trajectories of NDD patients with pathogenic variants and their benefits from physical rehabilitations are needed in the future for precise genetic counseling and clinical management.

Diagnostic pitfalls in patients with malformations of cortical development

Malformations of cortical development (MCDs) are a major source of morbidity and mortality in the pediatric patient cohort. Correct diagnosis of the cause is essential for symptom management, disease prognosis and family counselling but is frequently hampered due to numerous potential pitfalls in the diagnostic process. This review highlights potential problems that either prevent the establishment of a diagnosis or are the sources of diagnostic errors. The focus is placed on hereditary causes of MCDs and strategies will be proposed to circumvent potential diagnostic pitfalls. Errors may occur during variant detection, filtering, or interpretation in relation to patient's phenotype. Based on detailed clinical assessment suitable targeted and untargeted methods to identify pathogenic variants with context-dependent filtering and evaluation approaches will be discussed.

RNA splicing analysis contributes to reclassifying variants of uncertain significance and improves the diagnosis of monogenic disorders

BACKGROUND

Numerous variants of uncertain significance (VUSs) have been identified by whole exome sequencing in clinical practice. However, VUSs are not currently considered medically actionable.

OBJECTIVE

To assess the splicing patterns of 49 VUSs in 48 families identified clinically to improve genetic counselling and family planning.

METHODS

Forty-nine participants with 49 VUSs were recruited from the Reproductive and Genetic Hospital of CITIC-Xiangya. Bioinformatic analysis was performed to preliminarily predict the splicing effects of these VUSs. RT-PCR and minigene analysis were used to assess the splicing patterns of the VUSs. According to the results obtained, couples opted for different methods of reproductive interventions to conceive a child, including prenatal diagnosis and preimplantation genetic testing (PGT).

RESULTS

Eleven variants were found to alter pre-mRNA splicing and one variant caused nonsense-mediated mRNA decay, which resulted in the reclassification of these VUSs as likely pathogenic. One couple chose to undergo in vitro fertilisation with PGT treatment; a healthy embryo was transferred and the pregnancy is ongoing. Three couples opted for natural pregnancy with prenatal diagnosis. One couple terminated the pregnancy because the fetus was affected by short-rib thoracic dysplasia and harboured the related variant. The infants of the other two couples were born and were healthy at their last recorded follow-up.

CONCLUSION

RNA splicing analysis is an important method to assess the impact of sequence variants on splicing in clinical practice and can contribute to the reclassification of a significant proportion of VUSs. RNA splicing analysis should be considered for genetic disease diagnostics.

The splicing effect of variants at branchpoint elements in cancer genes

PURPOSE

Branchpoint elements are required for intron removal, and variants at these elements can result in aberrant splicing. We aimed to assess the value of branchpoint annotations generated from recent large-scale studies to select branchpoint-abrogating variants, using hereditary cancer genes as model.

METHODS

We identified branchpoint elements in 119 genes associated with hereditary cancer from 3 genome-wide experimentally-inferred and 2 predicted branchpoint data sets. We then identified variants that occur within branchpoint elements from public databases. We compared conservation, unique variant observations, and population frequencies at different nucleotides within branchpoint motifs. Finally, selected minigene assays were performed to assess the splicing effect of variants at branchpoint elements within mismatch repair genes.

RESULTS

There was poor overlap between predicted and experimentally-inferred branchpoints. Our analysis of cancer genes suggested that variants at -2 nucleotide, -1 nucleotide, and branchpoint positions in experimentally-inferred canonical motifs are more likely to be clinically relevant. Minigene assay data showed the -2 nucleotide to be more important to branchpoint motif integrity but also showed fluidity in branchpoint usage.

CONCLUSION

Data from cancer gene analysis suggest that there are few high-risk alleles that severely impact function via branchpoint abrogation. Results of this study inform a general scheme to prioritize branchpoint motif variants for further study.

RNA Analysis and Clinical Characterization of a Novel Splice Variant in the NSD1 Gene Causing Familial Sotos Syndrome

BACKGROUND

Sotos syndrome is an autosomal dominant disorder characterized by overgrowth, macrocephaly, distinctive facial features and learning disabilities. Haploinsufficiency of the nuclear receptor SET domain-containing protein 1 (NSD1) gene located on chromosome 5q35 is the major cause of the syndrome. This syndrome shares characteristics with other overgrowth syndromes, which can complicate the differential diagnosis.

METHODS

Genomic DNA was extracted from peripheral blood samples of members of the same family and targeted exome analysis was performed. In silico study of the variant found by next-generation sequencing was used to predict disruption/creation of splice sites and the identification of potential cryptic splice sites. RNA was extracted from peripheral blood samples of patients and functional analyses were performed to confirm the pathogenicity.

RESULTS

We found a novel c.6463 + 5G>A heterozygous NSD1 gene pathogenic variant in a son and his father. Molecular analyses revealed that part of the intron 22 of NSD1 is retained due to the destruction of the splicing donor site, causing the appearance of a premature stop codon in the NSD1 protein.

CONCLUSIONS

Our findings underline the importance of performing RNA functional assays in order to determine the clinical significance of intronic variants, and contribute to the genetic counseling and clinical management of patients and their relatives. Our work also highlights the relevance of using in silico prediction tools to detect a potential alteration in the splicing process.

Impact of the Mutational Landscape of the Sodium/Iodide Symporter in Congenital Hypothyroidism

Background: Iodide transport defect is an uncommon cause of dyshormonogenic congenital hypothyroidism due to homozygous or compound heterozygous pathogenic variants in the SLC5A5 gene, which encodes the sodium/iodide symporter (NIS), causing deficient iodide accumulation in thyroid follicular cells, thus impairing thyroid hormonogenesis. Methods:SLC5A5 gene variants were compiled from public databases and research articles exploring the molecular bases of congenital hypothyroidism. Using a dataset of 198 missense NIS variants classified as either benign or pathogenic, we developed and validated a machine learning-based NIS-specific variant classifier to predict the impact of missense NIS variants. Results: We generated a manually curated dataset containing 7793 unique SLC5A5 variants. As most databases compiled exome sequencing data, variant mapping revealed an increased density of variants in SLC5A5 coding exons. Based on allele frequency (AF) analysis, we established an AF threshold of 1:10,000 above which a variant should be considered benign. Most pathogenic NIS variants were located in the protein-coding region, as most patients were genetically diagnosed by using a candidate gene strategy limited to this region. Significantly, we evidenced that 94.5% of missense NIS variants were classified as of uncertain significance. Therefore, we developed an NIS-specific variant classifier to improve the prediction of pathogenicity of missense variants. Our classifier predicted the clinical outcome of missense variants with high accuracy (90%), outperforming state-of-the-art pathogenicity predictors, such as REVEL, PolyPhen-2, and SIFT. Based on the excellent performance of our classifier, we predicted the mutational landscape of NIS. The analysis of the mutational landscape revealed that most missense variants located in transmembrane segments are frequently pathogenic. Moreover, we predicted that ∼28% of all single-nucleotide variants that could cause missense NIS variants are pathogenic, thus putatively leading to congenital hypothyroidism if present in homozygous or compound heterozygous state. Conclusions: We reported the first NIS-specific variant classifier aiming at improving the interpretation of missense NIS variants in clinical practice. Deciphering the mutational landscape for every protein involved in thyroid hormonogenesis is a relevant task for a deep understanding of the molecular mechanisms causing dyshormonogenic congenital hypothyroidism.

Preparing n-of-1 Antisense Oligonucleotide Treatments for Rare Neurological Diseases in Europe: Genetic, Regulatory, and Ethical Perspectives

Antisense oligonucleotide (ASO) therapies present a promising disease-modifying treatment approach for rare neurological diseases (RNDs). However, the current focus is on "more common" RNDs, leaving a large share of RND patients still without prospect of disease-modifying treatments. In response to this gap, n-of-1 ASO treatment approaches are targeting ultrarare or even private variants. While highly attractive, this emerging, academia-driven field of ultimately individualized precision medicine is in need of systematic guidance and standards, which will allow global scaling of this approach. We provide here genetic, regulatory, and ethical perspectives for preparing n-of-1 ASO treatments and research programs, with a specific focus on the European context. By example of splice modulating ASOs, we outline genetic criteria for variant prioritization, chart the regulatory field of n-of-1 ASO treatment development in Europe, and propose an ethically informed classification for n-of-1 ASO treatment strategies and level of outcome assessments. To accommodate the ethical requirements of both individual patient benefit and knowledge gain, we propose a stronger integration of patient care and clinical research when developing novel n-of-1 ASO treatments: each single trial of therapy should inherently be driven to generate generalizable knowledge, be registered in a ASO treatment registry, and include assessment of generic outcomes, which allow aggregated analysis across n-of-1 trials of therapy.

Splicing Outcomes of 5' Splice Site GT>GC Variants That Generate Wild-Type Transcripts Differ Significantly Between Full-Length and Minigene Splicing Assays

Combining data derived from a meta-analysis of human disease-associated 5' splice site GT>GC (i.e., +2T>C) variants and a cell culture-based full-length gene splicing assay (FLGSA) of forward engineered +2T>C substitutions, we recently estimated that ∼15-18% of +2T>C variants can generate up to 84% wild-type transcripts relative to their wild-type counterparts. Herein, we analyzed the splicing outcomes of 20 +2T>C variants that generate some wild-type transcripts in two minigene assays. We found a high discordance rate in terms of the generation of wild-type transcripts, not only between FLGSA and the minigene assays but also between the different minigene assays. In the pET01 context, all 20 wild-type minigene constructs generated the expected wild-type transcripts; of the 20 corresponding variant minigene constructs, 14 (70%) generated wild-type transcripts. In the pSPL3 context, only 18 of the 20 wild-type minigene constructs generated the expected wild-type transcripts whereas 8 of the 18 (44%) corresponding variant minigene constructs generated wild-type transcripts. Thus, in the context of a particular type of variant, we raise awareness of the limitations of minigene splicing assays and emphasize the importance of sequence context in regulating splicing. Whether or not our findings apply to other types of splice-altering variant remains to be investigated.