In silico tools for splicing defect prediction: a survey from the viewpoint of end users

Identification of Synonymous Pathogenic Variants in Monogenic Disorders by Integrating Exome with Transcriptome Sequencing

Exome sequencing is becoming a first-tier clinical diagnostic test for Mendelian diseases, drastically reducing the time and cost of diagnostic odyssey and improving the diagnosis rate. Despite its success, exome sequencing faces practical challenges in assessing the pathogenicity of numerous intronic and synonymous variants, leaving a significant proportion of patients undiagnosed. In this study, a whole-blood transcriptome database was constructed that showed the expression profile of 2981 Online Mendelian Inheritance in Man disease genes in blood samples. Meanwhile, a workflow integrating exome sequencing, blood transcriptome sequencing, and in silico prediction tools to identify and validate splicing-altering intronic or synonymous variants was proposed. Following this pipeline, seven synonymous variants in eight patients were discovered. Of these, the functional evidence of c.981G>A (PIGN), c.1161A>G (ALPL), c.858G>A (ATP6AP2), and c.1011G>T (MTHFR) have not been reported previously. RNA sequencing validation confirmed that these variants induced aberrant splicing, expanding the disease-causing variant spectrum of these genes. Overall, this study shows the feasibility of combining multi-omics data to identify splicing-altering variants, especially the power of RNA sequencing. It also reveals that synonymous variants, which often are overlooked in standard diagnostic approaches, comprise an important portion of unresolved genetic diseases.

Genetic diagnosis of Alport syndrome in 16 Chinese families

BACKGROUND

Alport syndrome (AS) is a genetically heterogeneous disorder resulting from mutations in the collagen IV genes COL4A3, COL4A4, and COL4A5. The genetic diagnosis of AS is very important to make precise diagnosis and achieve optimal outcomes.

METHODS

In this study, 16 Chinese families with suspected AS were recruited after pedigree analysis, and the clinical presentations were analyzed by a nephrologist. The genetic diagnosis was performed by whole-exome sequencing (WES) and the disease-causing variants were confirmed by Sanger sequencing.

RESULTS

The cohort of probands included seven men and nine women, with a mean age of 19.9 years. Pathological analysis showed slight-to-moderate mesangial proliferation, and thin basement membrane was the main findings. Pathogenic variants were revealed by WES in each family, and the co-segregation with renal presentation was confirmed by PCR. In addition, RT-PCR analysis showed that the intronic variant led to aberrant splicing.

CONCLUSION

Our findings expand the spectrum of AS gene variation, which will inform genetic diagnosis and add to the theoretical basis for the prevention of AS.

Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning

Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting estimated 164 million people worldwide. Early detection and intervention strategies are essential to reduce the burden of COPD, but current screening approaches are limited in their ability to accurately predict risk. Machine learning (ML) models offer promise for improved accuracy of COPD risk prediction by combining genetic and electronic medical record data. In this study, we developed and evaluated eight ML models for primary screening of COPD utilizing routine screening data, polygenic risk scores (PRS), additional clinical data, or a combination of all three. To assess our models, we conducted a retrospective analysis of approximately 329,396 patients in the UK Biobank database. Incorporating personal information and blood biochemical test results significantly improved the model's accuracy for predicting COPD risk, achieving a best performance of 0.8505 AUC, a specificity of 0.8539 and a sensitivity of 0.7584. These results indicate that ML models can be effectively utilized for accurate prediction of COPD risk in individuals aged 20 to 50 years, providing a valuable tool for early detection and intervention.

Pathogenicity evaluation of variants of uncertain significance at exon-intron junction by splicing assay in patients with Mowat-Wilson syndrome

High-throughput sequencing has identified vast numbers of variants in genetic disorders. However, the significance of variants at the exon-intron junction remains controversial. Even though most cases of Mowat-Wilson syndrome (MOWS) are caused by heterozygous loss-of-function variants in ZEB2, the pathogenicity of variants at exon-intron junction is often indeterminable. We identified four intronic variants in 5/173 patients with clinical suspicion for MOWS, and evaluated their pathogenicity by in vitro analyses. The minigene analysis showed that c.73+2T>G caused most of the transcripts skipping exon 2, while c.916+6T>G led to partial skipping of exon 7. No splicing abnormalities were detected in both c.917-21T>C and c.3067+6A>T. The minigene analysis reproduced the splicing observed in the blood cells of the patient with c.73+2T>G. The degree of the exon skipping was concordant with the severity of MOWS; while the patient with c.73+2T>G was typical MOWS, the patient with c.916+6T>G showed milder phenotype which has been seldom reported. Our results demonstrate that mRNA splicing assays using the minigenes are valuable for determining the clinical significance of intronic variants in patients with not only MOWS but also other genetic diseases with splicing aberrations and may explain atypical or milder cases, such as the current patient.

Population-based prevalence and mutational landscape of von Willebrand disease using large-scale genetic databases

Von Willebrand disease (VWD) is a common bleeding disorder caused by mutations in the von Willebrand factor gene (VWF). The true global prevalence of VWD has not been accurately established. We estimated the worldwide and within-population prevalence of inherited VWD by analyzing exome and genome data of 141,456 individuals gathered by the genome Aggregation Database (gnomAD). We also extended our data deepening by mining the main databases containing VWF variants i.e., the Leiden Open Variation Database (LOVD) and the Human Gene Mutation Database (HGMD) with the goal to explore the global mutational spectrum of VWD. A total of 4,313 VWF variants were identified in the gnomAD population, of which 505 were predicted to be pathogenic or already reported to be associated with VWD. Among the 282,912 alleles analyzed, 31,785 were affected by the aforementioned variants. The global prevalence of dominant VWD in 1000 individuals was established to be 74 for type 1, 3 for 2A, 3 for 2B and 6 for 2M. The global prevalences for recessive VWD forms (type 2N and type 3) were 0.31 and 0.7 in 1000 individuals, respectively. This comprehensive analysis provided a global mutational landscape of VWF by means of 927 already reported variants in the HGMD and LOVD datasets and 287 novel pathogenic variants identified in the gnomAD. Our results reveal that there is a considerably higher than expected prevalence of putative disease alleles and variants associated with VWD and suggest that a large number of VWD patients are undiagnosed.

All reported non-canonical splice site variants in GLA cause aberrant splicing

BACKGROUND

Fabry disease is an X-linked lysosomal storage disorder caused by insufficient α-galactosidase A (GLA) activity resulting from variants in the GLA gene, which leads to glycosphingolipid accumulation and life-threatening, multi-organ complications. Approximately 50 variants have been reported that cause splicing abnormalities in GLA. Most were found within canonical splice sites, which are highly conserved GT and AG splice acceptor and donor dinucleotides, whereas one-third were located outside canonical splice sites, making it difficult to interpret their pathogenicity. In this study, we aimed to investigate the genetic pathogenicity of variants located in non-canonical splice sites within the GLA gene.

METHODS

13 variants, including four deep intronic variants, were selected from the Human Gene Variant Database Professional. We performed an in vitro splicing assay to identify splicing abnormalities in the variants.

RESULTS

All candidate non-canonical splice site variants in GLA caused aberrant splicing. Additionally, all but one variant was protein-truncating. The four deep intronic variants generated abnormal transcripts, including a cryptic exon, as well as normal transcripts, with the proportion of each differing in a cell-specific manner.

CONCLUSIONS

Validation of splicing effects using an in vitro splicing assay is useful for confirming pathogenicity and determining associations with clinical phenotypes.

Applications of artificial intelligence in clinical laboratory genomics

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

Genetic screening in a Brazilian cohort with inborn errors of immunity

BACKGROUND

Inherited genetic defects in immune system-related genes can result in Inborn Errors of Immunity (IEI), also known as Primary Immunodeficiencies (PID). Diagnosis of IEI disorders is challenging due to overlapping clinical manifestations. Accurate identification of disease-causing germline variants is crucial for appropriate treatment, prognosis, and genetic counseling. However, genetic sequencing is challenging in low-income countries like Brazil. This study aimed to perform genetic screening on patients treated within Brazil's public Unified Health System to identify candidate genetic variants associated with the patient's phenotype.

METHODS

Thirteen singleton unrelated patients from three hospitals in Rio de Janeiro were enrolled in this study. Genomic DNA was extracted from the peripheral blood lymphocytes of each patient, and whole exome sequencing (WES) analyses were conducted using Illumina NextSeq. Germline genetic variants in IEI-related genes were prioritized using a computational framework considering their molecular consequence in coding regions; minor allele frequency ≤ 0.01; pathogenicity classification based on American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines gathered from the VarSome clinical database; and IEI-related phenotype using the Franklin tool. The genes classification into IEI categories follows internationally recognized guidelines informed by the International Union of Immunological Societies Expert Committee. Additional methods for confirmation of the variant included Sanger sequencing, phasing analysis, and splice site prediction.

RESULTS

A total of 16 disease-causing variants in nine genes, encompassing six different IEI categories, were identified. X-Linked Agammaglobulinemia, caused by BTK variations, emerged as the most prevalent IEI disorder in the cohort. However, pathogenic and likely pathogenic variants were also reported in other known IEI-related genes, namely CD40LG, CARD11, WAS, CYBB, C6, and LRBA. Interestingly, two patients with suspected IEI exhibited pathogenic variants in non-IEI-related genes, ABCA12 and SLC25A13, potentially explaining their phenotypes.

CONCLUSIONS

Genetic screening through WES enabled the detection of potentially harmful variants associated with IEI disorders. These findings contribute to a better understanding of patients' clinical manifestations by elucidating the genetic basis underlying their phenotypes.

MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations

We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the "likely pathogenic" or "likely benign" classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.

Identification and in-silico characterization of splice-site variants from a large cardiogenetic national registry

Splice-site variants in cardiac genes may predispose carriers to potentially lethal arrhythmias. To investigate, we screened 1315 probands and first-degree relatives enrolled in the Canadian Hearts in Rhythm Organization (HiRO) registry. 10% (134/1315) of patients in the HiRO registry carry variants within 10 base-pairs of the intron-exon boundary with 78% (104/134) otherwise genotype negative. These 134 probands were carriers of 57 unique variants. For each variant, American College of Medical Genetics and Genomics (ACMG) classification was revisited based on consensus between nine in silico tools. Due in part to the in silico algorithms, seven variants were reclassified from the original report, with the majority (6/7) downgraded. Our analyses predicted 53% (30/57) of variants to be likely/pathogenic. For the 57 variants, an average of 9 tools were able to score variants within splice sites, while 6.5 tools responded for variants outside these sites. With likely/pathogenic classification considered a positive outcome, the ACMG classification was used to calculate sensitivity/specificity of each tool. Among these, Combined Annotation Dependent Depletion (CADD) had good sensitivity (93%) and the highest response rate (131/134, 98%), dbscSNV was also sensitive (97%), and SpliceAI was the most specific (64%) tool. Splice variants remain an important consideration in gene elusive inherited arrhythmia syndromes. Screening for intronic variants, even when restricted to the ±10 positions as performed here may improve genetic testing yield. We compare 9 freely available in silico tools and provide recommendations regarding their predictive capabilities. Moreover, we highlight several novel cardiomyopathy-associated variants which merit further study.

Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

It is estimated that 10%-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5' splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3' intronic variants is complex, that of the 5' is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View

Assessing the impact of variants of unknown significance on splicing has become a critical issue and a bottleneck, especially with the widespread implementation of whole-genome or exome sequencing. Although multiple in silico tools are available, the interpretation and application of these tools are difficult and practical guidelines are still lacking. A streamlined decision-making process can facilitate the downstream RNA analysis in a more efficient manner. Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. The change in the predicted score incurred by the variant of the nearest wild-type splice site was analyzed, and for type II, III, and IV splice variants, the change in the prediction score of de novo or cryptic splice site was also analyzed. SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. For de novo and cryptic splice sites, SpliceAI outperformed all other tools and showed a sensitivity of 95.7% at an optimal cut-off of 0.02 score change. Our results show that deep learning algorithms, especially those of SpliceAI, are validated at a significantly higher rate than other in silico tools for clinically relevant NF1 variants. This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.

Code inside the codon: The role of synonymous mutations in regulating splicing machinery and its impact on disease

In eukaryotes, precise pre-mRNA processing, including alternative splicing, is essential to carry out the intricate protein translation process. Both point mutations (that alter the translated protein sequence) and synonymous mutations (that do not alter the translated protein sequence) are capable of affecting the splicing process. Synonymous mutations are known to affect gene expression via altering mRNA stability, mRNA secondary structure, splicing processes, and translational kinetics. In higher eukaryotes, precise splicing is regulated by three weakly conserved cis-elements, 5' and 3' splice sites and the branch site. Many other cis-acting elements (exonic/intronic splicing enhancers and silencers) and trans-acting splicing factors (serine and arginine-rich proteins and heterogeneous nuclear ribonucleoproteins) have also been found to enhance or suppress the splicing process. The appearance of synonymous mutations in cis-acting elements can alter the splicing process by changing the binding pattern of splicing factors to exonic splicing enhancers or silencer motifs. This results in exon skipping, intron retention, and various other forms of alternative splicing, eventually leading to the emergence of a wide range of diseases. The focus of this review is to elucidate the role of synonymous mutations and their impact on abnormal splicing mechanisms. Further, this study highlights the function of synonymous mutation in mediating abnormal splicing in cancer and development of X-linked, and autosomal inherited diseases.

Polymorphic differences within HLA-C alleles contribute to alternatively spliced transcripts lacking exon 5

The HLA genes are amongst the most polymorphic in the human genome. Alternative splicing could add an extra layer of complexity, but has not been studied extensively. Here, we applied an RNA based approach to study the influence of allele polymorphism on alternative splicing of HLA‐C in peripheral blood. RNA was isolated from these peripheral cells, converted into cDNA and amplified specifically for 12 common HLA‐C allele groups. Through subsequent sequencing of HLA‐C, we observed alternative splicing variants of HLA‐C*04 and *16 that resulted in exon 5 skipping and were co‐expressed with the mature transcript. Investigation of intron 4 sequences of HLA‐C*04 and *16 compared with other HLA‐C alleles demonstrated no effect on predicted splice sites and branch point. To further investigate if the unique polymorphic positions in exon 5 of HLA‐C*04 or *16 may facilitate alternative splicing by acting on splicing regulatory elements (SRE), in‐silico splicing analysis was performed. While the HLA‐C*04 specific SNP in exon 5 had no effect on predicted exonic SRE, the HLA‐C*16 specific exon 5 SNP did alter exonic SRE. Our findings provide experimental and theoretical support for the concept that polymorphisms within the HLA‐C alleles influence the alternative splicing of HLA‐C.

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.

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.

Background splicing as a predictor of aberrant splicing in genetic disease

Mutations of splice sites, auxiliary splicing elements and the splicing machinery cause a wide range of genetic disease. Here we report that many of the complex effects of splicing mutations can be predicted from background splicing information, with emphasis on BRCA1, BRCA2 and DMD. Background splicing arises from very low level splicing between rarely used background splice sites and from low-level exon skipping between intron splice sites. We show how this information can be downloaded from the Snaptron database of spliced RNA, which we then compared with databases of human splice site mutations. We report that inactivating mutations of intron splice sites typically caused the non-mutated partner splice site to splice to a known background splice site in over 90% of cases and to the strongest background splice site in the large majority of cases. Consequently, background splicing information can usefully predict the effects of splice site mutations, which include cryptic splice activation and single or multiple exon skipping. In addition, de novo splice sites and splice sites involved in pseudoexon formation, recursive splicing and aberrant splicing in cancer show a 90% match to background splice sites, so establishing that the enhancement of background splicing causes a wide range of splicing aberrations. We also discuss how background splicing information can identify cryptic splice sites that might be usefully targeted by antisense oligonucleotides (ASOs) and how it might indicate possible multiple exon skipping side effects of ASOs designed to induce single exon skipping.

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning

Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.

Genome Sequencing in a Genetically Elusive Multi-Generational Long QT Syndrome Pedigree Identifies a Novel LQT2-Causative Deeply Intronic KCNH2 Variant

BACKGROUND

Most of long QT syndrome (LQTS) stems from pathogenic variants in KCNQ1, KCNH2, or SCN5A. However, ∼10-20% of LQTS index cases remain genotype-negative.

OBJECTIVE

Here, we identified and characterized functionally a novel LQTS genetic substrate in a multi-generational, "genotype-negative" LQTS pedigree.

METHODS

The patient was a 40-year-old female with a history of syncope, seizures, ventricular fibrillation, and a family history of LQTS and sudden death. Commercial genetic testing of all LQTS-causative genes was negative. Genome sequencing was performed on 6 affected family members. Patient-specific and CRISPR/Cas9 "gene-corrected" isogenic control induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) were generated.

RESULTS

No ultra-rare, nonsynonymous heterozygous variants co-segregated among the 6 LQTS phenotype-positive individuals. Instead, a deep intronic KCNH2 variant (c.3331-316G>T) was present in all affected individuals. RT-PCR analysis of patient-specific iPSC-CM-derived RNA revealed that c.3331-316G>T creates a novel 89 base-pair exon that results in a frame-shift variant (p.S1112Pfs*171). The action potential duration (APD₉₀) was significantly longer in p.S1112Pfs*171-iPSC-CMs (602.4 ± 12.2 ms, n=70) compared to isogenic control iPSC-CMs (425.7 ± 9.3 ms, n=61, p<0.0001). Further, the field potential duration (FPD) was significantly longer in p.S1112Pfs*171-iPSC-CMs (358.9 ± 7.7 ms, n=65) compared to isogenic control iPSC-CMs (282.2 ± 10.8 ms, n=51, p<0.0001).

CONCLUSIONS

A novel deep intronic KCNH2 variant was identified in a multi-generational, genetically elusive LQTS pedigree. The iPSC-CMs establish that the variant is the monogenetic cause for this family's LQTS. Deep intronic variants within the two most common LQTS-susceptibility genes should be considered in patients with seemingly, genetically elusive LQTS.

Silent variant in F8:c.222G>T (p.Thr74Thr) causes a partial exon skipping in a patient with mild hemophilia A

One of the challenges of genetic testing in patients with hemophilia A is the interpretation of sequence variants. Here we report a silent variant found in exon 2 in the F8 gene in a 47‐year‐old patient with a previous von Willebrand disease (VWD) type 1 diagnosis. Clinically he had mild bleeding symptoms restricted to prolonged bleeding from minor wounds. Sanger sequencing of F8 gene using genomic DNA showed a hemizygous silent variant in exon 2: c.222G>T, p.Thr74Thr. When applying ACMG criteria, the variant was predicted to be “likely benign” in the analyzing software or VUS after curating. Sanger sequencing of the patient's cDNA after nested polymerase chain reaction showed that the patient had both a normal transcript containing exons 1–4 and a defect transcript lacking exon 2. These findings explain the patient's low FVIII:C level and led to the diagnosis of mild hemophilia A instead of VWD type 1. This case illustrates that mRNA work‐up may be needed to clarify a patient's phenotype–genotype.

In silico prediction of HBD gene variants in the Iranian population

Background

The quantification of hemoglobin A2 (Hb A2; α2δ2) is used as a valuable test to differentiate α- and ß-thal carriers in clinical laboratories. Therefore, the HBD (δ-globin) gene variants could result in reduced levels of Hb A2 and have implications for thalassemia screening programs. The aim of the present study was to predict the consequences of HBD gene variants identified in the Iranome project.

Results

The highest number of variants was in the Persian Gulf Islanders. The variants of p.Gln132Glu (HBD: c.394C>G), p.Gly17Arg (HBD: c.49G>C), p.Thr5Ile (HBD: c.14C>T), and p.Ala28Ser (HBD: c.82G>T) presented damage results in three or more prediction tools. In addition, it seems that the p.Gly30= (HBD: c.90C>T) decreases the use of authentic splice and, instead, creates a new donor splice site (DSS) or leads to the use of a cryptic DSS.

Conclusions

Most of these variants have been associated with a decrease in Hb A2 levels. Due to the high mutational diversity in the HBB gene in the Iranian population and the use of Hb A2 quantification to differentiate α- and ß-thal carriers among Iranian clinical laboratories, some attention should be taken to a possible co-inheritance of HBD gene variants to avoid the misdiagnosis of ß-thal carriers.

A spotter's guide to SNPtic exons: The common splice variants underlying some SNP-phenotype correlations

BACKGROUND

Cryptic exons are typically characterised as deleterious splicing aberrations caused by deep intronic mutations. However, low-level splicing of cryptic exons is sometimes observed in the absence of any pathogenic mutation. Five recent reports have described how low-level splicing of cryptic exons can be modulated by common single-nucleotide polymorphisms (SNPs), resulting in phenotypic differences amongst different genotypes.

METHODS

We sought to investigate whether additional 'SNPtic' exons may exist, and whether these could provide an explanatory mechanism for some of the genotype-phenotype correlations revealed by genome-wide association studies. We thoroughly searched the literature for reported cryptic exons, cross-referenced their genomic coordinates against the dbSNP database of common SNPs, then screened out SNPs with no reported phenotype associations.

RESULTS

This method discovered five probable SNPtic exons in the genes APC, FGB, GHRL, MYPBC3 and OTC. For four of these five exons, we observed that the phenotype associated with the SNP was compatible with the predicted splicing effect of the nucleotide change, whilst the fifth (in GHRL) likely had a more complex splice-switching effect.

CONCLUSION

Application of our search methods could augment the knowledge value of future cryptic exon reports and aid in generating better hypotheses for genome-wide association studies.

Evaluation of Suspected Autosomal Alport Syndrome Synonymous Variants

Background

Alport syndrome is an inherited disorder characterized by progressive renal disease, variable sensorineural hearing loss, and ocular abnormalities. Although many pathogenic variants in COL4A3 and COL4A4 have been identified in patients with autosomal Alport syndrome, synonymous mutations in these genes have rarely been identified.

Methods

We conducted in silico splicing analysis using Human Splicing Finder (HSF) and Alamut to predict splicing domain strength and disruption of the sites. Furthermore, we performed in vitro splicing assays using minigene constructs and mRNA analysis of patient samples to determine the pathogenicity of four synonymous variants detected in four patients with suspected autosomal dominant Alport syndrome (COL4A3 [c.693G>A (p.Val231=)] and COL4A4 [c.1353C>T (p.Gly451=), c.735G>A (p.Pro245=), and c.870G>A (p.Lys290=)]).

Results

Both in vivo and in vitro splicing assays showed exon skipping in two out of the four synonymous variants identified (c.735G>A and c.870G>A in COL4A4). Prediction analysis of wild-type and mutated COL4A4 sequences using HSF and Alamut suggested these two variants may lead to the loss of binding sites for several splicing factors, e.g., in acceptor sites and exonic splicing enhancers. The other two variants did not induce aberrant splicing.

Conclusions

This study highlights the pitfalls of classifying the functional consequences of variants by a simple approach. Certain synonymous variants, although they do not alter the amino acid sequence of the encoded protein, can dramatically affect pre-mRNA splicing, as shown in two of our patients. Our findings indicate that transcript analysis should be carried out to evaluate synonymous variants detected in patients with autosomal dominant Alport syndrome.

Genetic Diagnosis in Hereditary Hemochromatosis: Discovering and Understanding the Biological Relevance of Variants

BACKGROUND

Hereditary hemochromatosis (HH) is a genetic disease, leading to iron accumulation and possible organ damage. Patients are usually homozygous for p. Cys282Tyr in the homeostatic iron regulator gene but may have mutations in other genes involved in the regulation of iron. Next-generation sequencing is increasingly being utilized for the diagnosis of patients, leading to the discovery of novel genetic variants. The clinical significance of these variants is often unknown.

CONTENT

Determining the pathogenicity of such variants of unknown significance is important for diagnostics and genetic counseling. Predictions can be made using in silico computational tools and population data, but additional evidence is required for a conclusive pathogenicity classification. Genetic disease models, such as in vitro models using cellular overexpression, induced pluripotent stem cells or organoids, and in vivo models using mice or zebrafish all have their own challenges and opportunities when used to model HH and other iron disorders. Recent developments in gene-editing technologies are transforming the field of genetic disease modeling.

SUMMARY

In summary, this review addresses methods and developments regarding the discovery and classification of genetic variants, from in silico tools to in vitro and in vivo models, and presents them in the context of HH. It also explores recent gene-editing developments and how they can be applied to the discussed models of genetic disease.

Mutations at a split codon in the GTPase-encoding domain of OPA1 cause dominant optic atrophy through different molecular mechanisms

Exonic (i.e. coding) variants in genes associated with disease can exert pathogenic effects both at the protein and mRNA level, either by altering the amino acid sequence or by affecting pre-mRNA splicing. The latter is often neglected due to the lack of RNA analyses in genetic diagnostic testing. In this study we considered both pathomechanisms and performed a comprehensive analysis of nine exonic nucleotide changes in OPA1, which is the major gene underlying autosomal dominant optic atrophy (DOA) and is characterized by pronounced allelic heterogeneity. We focused on the GTPase-encoding domain of OPA1, which harbors most of the missense variants associated with DOA. Given that the consensus splice sites extend into the exons, we chose a split codon, namely codon 438, for our analyses. Variants at this codon are the second most common cause of disease in our large cohort of DOA patients harboring disease-causing variants in OPA1. In silico splice predictions, heterologous splice assays, analysis of patient’s RNA when available, and protein modeling revealed different molecular outcomes for variants at codon 438. The wildtype aspartate residue at amino acid position 438 is directly involved in the dimerization of OPA1 monomers. We found that six amino acid substitutions at codon 438 (i.e. all substitutions of the first and second nucleotide of the codon) destabilized dimerization while only substitutions of the first nucleotide of the codon caused exon skipping. Our study highlights the value of combining RNA analysis and protein modeling approaches to accurately assign patients to future precision therapies.

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Association of PARK-2 Non-synonyms Polymorphisms and Their In Silico Validation Among North Indian Colorectal Cancer Patients

PURPOSE

PARK2 is a potential tumour suppressor gene and its genetic alterations (regionic loss) are common across many human cancers. The association of PARK2 germline variations (SNPs) with Parkinson's has been shown, but their association in development and progression of cancer remains elusive. The aim of this study was to identify association of PARK2 polymorphisms (rs1801474, rs1801334) with colorectal cancer in a case control study design.

METHODS

This case control study included a total of 650 genetically unrelated subjects comprising 300 colorectal cancer cases and 350 healthy controls belonging to North Indian. Both SNPs were analyzed using the PCR-RFLP assay. Statistical analysis for describing risk and association was performed using SPSS-17 software. Structural deviations due to non- synonymous substitutions (S167N and D394N) were analyzed using MD simulations.

RESULTS

The genotype distributions of both the SNPs were in Hardy-Weinberg equilibrium. For both the polymorphisms, the allelic model showed statistically significant risk with OR ~ 1.3. Many of the associations remained significant even after Bonferroni correction (P < 0.00125). The result suggested that both S167N and D394N were deviated from wild type and structures and were stable after 5 ns. The average value of RMSD for backbone atoms was calculated from 5 to 10 ns molecular dynamics simulation data.

CONCLUSION

In conclusion, our study revealed a significant association of PARK2 SNPs with colorectal cancer as well as their relations with other clinical parameters highlighting their contribution towards colorectal cancer susceptibility in North Indian population.

Identification of novel single-nucleotide variants altering RNA splicing of PKD1 and PKD2

The development of sequencing techniques identified numerous genetic variants, and accurate evaluation of the clinical significance of these variants facilitates the diagnosis of Mendelian diseases. In the present study, 549 rare single- nucleotide variants of uncertain significance were extracted from the ADPKD and ClinVar databases. MaxEntScan scoresplice is an in silico splicing prediction tool that was used to analyze rare PKD1 and PKD2 variants of unknown significance. An in vitro minigene splicing assay was used to verify 37 splicing-altering candidates that were located within seven residues of the splice donor sequence excluding canonical GT dinucleotides or within 21 residues of the acceptor sequence excluding canonical AG dinucleotides of PKD1 and PKD2. We demonstrated that eight PKD1 variants alter RNA splicing and were predicted to be pathogenic.

Benchmarking deep learning splice prediction tools using functional splice assays

Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice motifs are almost always disrupting splicing, the pathogenicity of variants in the noncanonical splice sites (NCSS) and deep intronic (DI) regions are difficult to predict. Multiple splice prediction tools have been developed for this purpose, with the latest tools employing deep learning algorithms. We benchmarked established and deep learning splice prediction tools on published gold standard sets of 71 NCSS and 81 DI variants in the ABCA4 gene and 61 NCSS variants in the MYBPC3 gene with functional assessment in midigene and minigene splice assays. The selection of splice prediction tools included CADD, DSSP, GeneSplicer, MaxEntScan, MMSplice, NNSPLICE, SPIDEX, SpliceAI, SpliceRover, and SpliceSiteFinder-like. The best-performing splice prediction tool for the different variants was SpliceRover for ABCA4 NCSS variants, SpliceAI for ABCA4 DI variants, and the Alamut 3/4 consensus approach (GeneSplicer, MaxEntScacn, NNSPLICE and SpliceSiteFinder-like) for NCSS variants in MYBPC3 based on the area under the receiver operator curve. Overall, the performance in a real-time clinical setting is much more modest than reported by the developers of the tools.

Mutations in cis that affect mRNA synthesis, processing and translation

Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.

Comparative genomics in the search for conserved long noncoding RNAs

Long noncoding RNAs (lncRNAs) have emerged as prominent regulators of gene expression in eukaryotes. The identification of lncRNA orthologs is essential in efforts to decipher their roles across model organisms, as homologous genes tend to have similar molecular and biological functions. The relatively high sequence plasticity of lncRNA genes compared with protein-coding genes, makes the identification of their orthologs a challenging task. This is why comparative genomics of lncRNAs requires the development of specific and, sometimes, complex approaches. Here, we briefly review current advancements and challenges associated with four levels of lncRNA conservation: genomic sequences, splicing signals, secondary structures and syntenic transcription.

Cis-regulatory elements in conserved non-coding sequences of nuclear receptor genes indicate for crosstalk between endocrine systems

Nuclear receptors (NRs) are ligand-activated transcription factors that regulate gene expression when bound to specific DNA sequences. Crosstalk between steroid NR systems has been studied for understanding the development of hormone-driven cancers but not to an extent at a genetic level. This study aimed to investigate crosstalk between steroid NRs in conserved intron and exon sequences, with a focus on steroid NRs involved in prostate cancer etiology. For this purpose, we evaluated conserved intron and exon sequences among all 49 members of the NR Superfamily (NRS) and their relevance as regulatory sequences and NR-binding sequences. Sequence conservation was found to be higher in the first intron (35%), when compared with downstream introns. Seventy-nine percent of the conserved regions in the NRS contained putative transcription factor binding sites (TFBS) and a large fraction of these sequences contained splicing sites (SS). Analysis of transcription factors binding to putative intronic and exonic TFBS revealed that 5 and 16%, respectively, were NRs. The present study suggests crosstalk between steroid NRs, e.g., vitamin D, estrogen, progesterone, and retinoic acid endocrine systems, through cis-regulatory elements in conserved sequences of introns and exons. This investigation gives evidence for crosstalk between steroid hormones and contributes to novel targets for steroid NR regulation.

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

The complexities of gene expression pose challenges for the clinical interpretation of splicing variants. To better understand splicing variants and their contribution to hereditary disease, we evaluated their prevalence, clinical classifications, and associations with diseases, inheritance, and functional characteristics in a 689,321-person clinical cohort and two large public datasets. In the clinical cohort, splicing variants represented 13% of all variants classified as pathogenic (P), likely pathogenic (LP), or variants of uncertain significance (VUSs). Most splicing variants were outside essential splice sites and were classified as VUSs. Among all individuals tested, 5.4% had a splicing VUS. If RNA analysis were to contribute supporting evidence to variant interpretation, we estimated that splicing VUSs would be reclassified in 1.7% of individuals in our cohort. This would result in a clinically significant result (i.e., P/LP) in 0.1% of individuals overall because most reclassifications would change VUSs to likely benign. In ClinVar, splicing VUSs were 4.8% of reported variants and could benefit from RNA analysis. In the Genome Aggregation Database (gnomAD), splicing variants comprised 9.4% of variants in protein-coding genes; most were rare, precluding unambiguous classification as benign. Splicing variants were depleted in genes associated with dominant inheritance and haploinsufficiency, although some genes had rare variants at essential splice sites or had common splicing variants that were most likely compatible with normal gene function. Overall, we describe the contribution of splicing variants to hereditary disease, the potential utility of RNA analysis for reclassifying splicing VUSs, and how natural variation may confound clinical interpretation of splicing variants.

CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores

Background

Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies.

Methods

It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants.

Results

We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance.

Conclusions

While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00835-9.

Intronic variant screening with targeted next-generation sequencing reveals first pseudoexon in LDLR in familial hypercholesterolemia

BACKGROUND AND AIMS

Familial hypercholesterolemia (FH) is caused by pathogenic variants in LDLR, APOB, or PCSK9 genes (designated FH+). However, a significant number of clinical FH patients do not carry these variants (designated FH-). Here, we investigated whether variants in intronic regions of LDLR attribute to FH by affecting pre-mRNA splicing.

METHODS

LDLR introns are partly covered in routine sequencing of clinical FH patients using next-generation sequencing. Deep intronic variants, >20 bp from intron-exon boundary, were considered of interest once (a) present in FH- patients (n = 909) with LDL-C >7 mmol/L (severe FH-) or after in silico analysis in patients with LDL-C >5 mmol/L (moderate FH-) and b) absent in FH + patients (control group). cDNA analysis and co-segregation analysis were performed to assess pathogenicity of the identified variants.

RESULTS

Three unique variants were present in the severe FH- group. One of these was the previously described likely pathogenic variant c.2140+103G>T. Three additional variants were selected based on in silico analyses in the moderate FH- group. One of these variants, c.2141-218G>A, was found to result in a pseudo-exon inclusion, producing a premature stop codon. This variant co-segregated with the hypercholesterolemic phenotype.

CONCLUSIONS

Through a screening approach, we identified a deep intronic variant causal for FH. This finding indicates that filtering intronic variants in FH- patients for the absence in FH + patients might enrich for true FH-causing variants and suggests that intronic regions of LDLR need to be considered for sequencing in FH- patients.

Comprehensive Mutation Analysis and Report of 12 Novel Mutations in a Cohort of Patients with Spinal Muscular Atrophy in Iran

Spinal muscular atrophies (SMAs) are a heterogeneous group of neuromuscular diseases characterized by loss of motor neurons, muscle weakness, hypotonia and muscle atrophy, with different modes of inheritance; however, the survival motor neuron 1 (SMN1) gene is predominantly involved. The aims of the current study were to clarify the genetic basis of SMA and determine the mutation spectrum of SMN1 and other associated genes, in order to provide molecular information for more accurate diagnosis and future prospects for treatment. We performed a comprehensive analysis of 5q SMA in 1765 individuals including 528 patients from 432 unrelated families with at least one child with suspected clinical presentation of SMA. Copy number variations of the SMN1 and SMN2 genes and linkage analysis were performed using multiplex ligation-dependent probe amplification (MLPA) and short tandem repeat (STR) markers linked to the SMN1 gene. Cases without mutation in the SMA locus on 5q were analyzed for the DNAJB2, IGHMBP2, SIGMAR1 and PLEKHG5 genes using linked STR markers. Sanger sequencing of whole genes was performed for cases with homozygous haplotypes. Whole-genome sequencing (WGS) and whole-exome analysis was conducted for some of the remaining cases. Mutations in the SMN1 gene were identified in 287 (66.43%) families including 269 patients (62.26%) with homozygous deletion of the entire SMN1 gene. Only one of the patients had a homozygous point mutation in the SMN1 gene. Among the remaining families, three families showed mutations in either the DNAJB2, SIGMAR1 or PLEKHG5 genes, which were linked using STR analysis and Sanger sequencing. From 10 families who underwent WGS, we found six homozygous point mutations in six families for either the TNNT1, TPM3, TTN, SACS or COL6A2 genes. Two mutations in the PLA2G6 gene were also found in another patient as compound heterozygous. This rather large cohort allowed us to identify genotype patterns in Iranian 5q SMA patients. The process of identifying 11 mutations (9 novel) in 9 different genes among non-5q SMA patients shows the diversity of genes involved in non-5q SMA in Iranians. Genotyping of patients with SMA is essential for prenatal and preimplantation genetic diagnosis (PGD), and may be very helpful for guiding treatment, with the advent of new, more effective, albeit very expensive, therapies. Also, combining linkage analysis was shown to be beneficial in many ways, including sample authenticity and segregation analysis, and for ruling out maternal cell contamination during prenatal diagnosis (PND).

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

Transcriptome sequencing identifies a noncoding, deep intronic variant in CLCN7 causing autosomal recessive osteopetrosis

Background

Over half of children with rare genetic diseases remain undiagnosed despite maximal clinical evaluation and DNA‐based genetic testing. As part of an Undiagnosed Diseases Program applying transcriptome (RNA) sequencing to identify the causes of these unsolved cases, we studied a child with severe infantile osteopetrosis leading to cranial nerve palsies, bone deformities, and bone marrow failure, for whom whole‐genome sequencing was nondiagnostic.

Methods

We performed transcriptome (RNA) sequencing of whole blood followed by analysis of aberrant transcript isoforms and osteoclast functional studies.

Results

We identified a pathogenic deep intronic variant in CLCN7 creating an unexpected, frameshifting pseudoexon causing complete loss of function. Functional studies, including osteoclastogenesis and bone resorption assays, confirmed normal osteoclast differentiation but loss of osteoclast function.

Conclusion

This is the first report of a pathogenic deep intronic variant in CLCN7, and our approach provides a model for systematic identification of noncoding variants causing osteopetrosis—a disease for which molecular‐genetic diagnosis can be pivotal for potentially curative hematopoietic stem cell transplantation. Our work illustrates that cryptic splice variants may elude DNA‐only sequencing and supports broad first‐line use of transcriptome sequencing for children with undiagnosed diseases.

Splicing mutations in inherited retinal diseases

Mutations which induce aberrant transcript splicing represent a distinct class of disease-causing genetic variants in retinal disease genes. Such mutations may either weaken or erase regular splice sites or create novel splice sites which alter exon recognition. While mutations affecting the canonical GU-AG dinucleotides at the splice donor and splice acceptor site are highly predictive to cause a splicing defect, other variants in the vicinity of the canonical splice sites or those affecting additional cis-acting regulatory sequences within exons or introns are much more difficult to assess or even to recognize and require additional experimental validation. Splicing mutations are unique in that the actual outcome for the transcript (e.g. exon skipping, pseudoexon inclusion, intron retention) and the encoded protein can be quite different depending on the individual mutation. In this article, we present an overview on the current knowledge about and impact of splicing mutations in inherited retinal diseases. We introduce the most common sub-classes of splicing mutations including examples from our own work and others and discuss current strategies for the identification and validation of splicing mutations, as well as therapeutic approaches, open questions, and future perspectives in this field of research.

Identification of a deep intronic POLR3A variant causing inclusion of a pseudoexon derived from an Alu element in Pol III-related leukodystrophy

Pseudoexon inclusion caused by deep intronic variants is an important genetic cause for various disorders. Here, we present a case of a hypomyelinating leukodystrophy with developmental delay, intellectual disability, autism spectrum disorder, and hypodontia, which are consistent with autosomal recessive POLR3-related leukodystrophy. Whole-exome sequencing identified only a heterozygous missense variant (c.1451G>A) in POLR3A. To explore possible involvement of a deep intronic variant in another allele, we performed whole-genome sequencing of the patient with variant annotation by SpliceAI, a deep-learning-based splicing prediction tool. A deep intronic variant (c.645 + 312C>T) in POLR3A, which was predicted to cause inclusion of a pseudoexon derived from an Alu element, was identified and confirmed by mRNA analysis. These results clearly showed that whole-genome sequencing, in combination with deep-learning-based annotation tools such as SpliceAI, will bring us further benefits in detecting and evaluating possible pathogenic variants in deep intronic regions.

Evolution of approaches to identify melanoma missing heritability

Introduction: Around 10% of melanoma patients have a positive family history of melanoma and/or related cancers. Although a germline pathogenic variant in a high-risk gene can be identified in up to 40% of these patients, the remaining part of melanoma heritability remains largely unexplained.Areas covered: The aim of this review is to provide an overview of the impact that new technologies and new research approaches had and are having on finding more efficient ways to unravel the missing heritability in melanoma.Expert opinion: High-throughput sequencing technologies have been crucial in increasing the number of genes/loci that might be implicated in melanoma predisposition. However, results from these approaches may have been inferior to the expectations, due to an increase in quantitative information which hasn't been followed at the same speed by an improvement of the methods to correctly interpret these data. Optimal approaches for improving our knowledge on melanoma heritability are currently based on segregation analysis coupled with functional assessment of candidate genes. An improvement of computational methods to infer genotype-phenotype correlations could help address the issue of missing heritability.

Key Value of RNA Analysis of MYBPC3 Splice-Site Variants in Hypertrophic Cardiomyopathy

BACKGROUND

MYBPC3 splicing errors are a common cause of hypertrophic cardiomyopathy (HCM). Variants affecting essential splice-site dinucleotides inhibit splicing, whereas the impact of variants at conserved flanking nucleotides is less clear. We evaluated the contribution of MYBPC3 splice-site variants in a large cohort of patients with HCM and assessed the impact on splicing with RNA analysis.

METHODS

Patients attending a specialized multidisciplinary clinic, with a clinical diagnosis of HCM and genetic testing of at least 46 cardiomyopathy-associated genes, were included. Patients with variants in MYBPC3 splice sites with in silico-predicted effects on splicing were selected. RNA was extracted from fresh venous blood or paraffin-embedded myocardial tissue of the patients, amplified, and sequenced. Variants were classified for pathogenicity using the American College of Medical Genetics and Genomics guidelines.

RESULTS

We found 29 rare MYBPC3 splice-site variants in 56 of 557 (10%) unrelated HCM probands. Three variants were not predicted to alter RNA splicing, and 13 essential splice dinucleotide, nonsense, and short insertion or deletion variants were not further assessed. RNA analysis was performed on 9 variants (c.654+5G>C, c.772G>A, c.821+3G>T, c.927-9G>A, c.1090G>A, c.1624G>A, c.1624+4A>T, c.3190+5G>A, and c.3491-3C>G), and RNA splicing errors were confirmed for 7. Four variants in 4 families resulted in clinically meaningful reclassifications.

CONCLUSIONS

After RNA analysis, 4 of 56 (7%) families with MYBPC3 splice-site variants were reclassified from uncertain clinical significance to likely pathogenic. RNA analysis of splice-site variants can assist in understanding pathogenicity and increase the diagnostic yield of genetic testing in HCM.

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

Background: With the advent of next-generation sequencing in genetic testing, predicting the pathogenicity of missense variants represents a major challenge potentially leading to misdiagnoses in the clinical setting. In neurofibromatosis type 1 (NF1), where clinical criteria for diagnosis may not be fully present until late infancy, correct assessment of variant pathogenicity is fundamental for appropriate patients' management. Methods: Here, we analyzed three different computational methods, VEST3, REVEL and ClinPred, and after extracting predictions scores for 1585 NF1 missense variants listed in ClinVar, evaluated their performances and the score distribution throughout the neurofibromin protein. Results: For all the three methods, no significant differences were present between the scores of "likely benign", "benign", and "likely pathogenic", "pathogenic" variants that were consequently collapsed into a single category. The cutoff values for pathogenicity were significantly different for the three methods and among benign and pathogenic variants for all methods. After training five different models with a subset of benign and pathogenic variants, we could reclassify variants in three sharply separated categories. Conclusions: The recently developed metapredictors, which integrate information from multiple components, after gene-specific fine-tuning, could represent useful tools for variant interpretation, particularly in genetic diseases where a clinical diagnosis can be difficult.