SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation

SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

BACKGROUND

Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights.

RESULTS

Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.

CONCLUSIONS

In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP . Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP .

Identification of a novel CFAP61 homozygous splicing variant associated with multiple morphological abnormalities of the flagella

In this study, we investigated the role of a newly identified homozygous variant (c.1245 + 6T > C) in the CFAP61 gene in the development of multiple morphologically abnormal flagella (MMAF) in an infertile patient. Using exome sequencing, we identified this variant, which led to exon 12 skipping and the production of a truncated CFAP61 protein. Transmission electron microscopy analysis of the patient's spermatozoa revealed various flagellar abnormalities, including defective nuclear chromatin condensation, axoneme disorganization, and mitochondria embedded in residual cytoplasmic droplets. Despite a fertilization rate of 83.3% through ICSI, there was no successful pregnancy due to poor embryo quality.Our findings suggest a link between the identified CFAP61 variant and MMAF, indicating potential disruption in radial spokes' assembly or function crucial for normal ciliary motility. Furthermore, nearly half of the observed sperm heads displayed chromatin condensation defects, possibly contributing to the low blastulation rate. This case underscores the significance of genetic counseling and testing, particularly for couples dealing with infertility and MMAF. Early identification of such genetic variants can guide appropriate interventions and improve reproductive outcomes.

Whole genome sequencing identifies elusive variants in genetically unsolved Italian inherited retinal disease patients

Inherited retinal diseases (IRDs) are a group of rare monogenic diseases with high genetic heterogeneity (pathogenic variants identified in over 280 causative genes). The genetic diagnostic rate for IRDs is around 60%, mainly thanks to the routine application of next-generation sequencing (NGS) approaches such as extensive gene panels or whole exome analyses. Whole-genome sequencing (WGS) has been reported to improve this diagnostic rate by revealing elusive variants, such as structural variants (SVs) and deep intronic variants (DIVs). We performed WGS on 33 unsolved cases with suspected autosomal recessive IRD, aiming to identify causative genetic variants in non-coding regions or to detect SVs that were unexplored in the initial screening. Most of the selected cases (30 of 33, 90.9%) carried monoallelic pathogenic variants in genes associated with their clinical presentation, hence we first analyzed the non-coding regions of these candidate genes. Whenever additional pathogenic variants were not identified with this approach, we extended the search for SVs and DIVs to all IRD-associated genes. Overall, we identified the missing causative variants in 11 patients (11 of 33, 33.3%). These included three DIVs in ABCA4, CEP290 and RPGRIP1; one non-canonical splice site (NCSS) variant in PROM1 and three SVs (large deletions) in EYS, PCDH15 and USH2A. For the previously unreported DIV in CEP290 and for the NCCS variant in PROM1, we confirmed the effect on splicing by reverse transcription (RT)-PCR on patient-derived RNA. This study demonstrates the power and clinical utility of WGS as an all-in-one test to identify disease-causing variants missed by standard NGS diagnostic methodologies.

Resolving unsolved whole-genome sequencing data in paediatric neurological disorders: a cohort study

OBJECTIVE

To resolve unsolved whole-genome sequencing (WGS) data in individuals with paediatric neurological disorders.

DESIGN

A cohort study method using updated bioinformatic tools, new analysis targets, clinical information and literature databases was employed to reanalyse existing unsolved genome data.

PARTICIPANTS

From January 2016 to September 2023, a total of 615 individuals who aged under 18 years old, exhibited neurological disorders and received singleton WGS were recruited. 364 cases were unsolved during initial WGS analysis, in which 102 consented to reanalyse existing singleton WGS data.

RESULTS

Median duration for reanalysis after initial negative WGS results was 2 years and 4 months. The diagnostic yield was 29 of 102 individuals (28.4%) through reanalysis. New disease gene discovery and new target acquisitions contributed to 13 of 29 solved cases (44.8%). The reasons of non-detected causative variants during initial WGS analysis were variant reclassification in 9 individuals (31%), analytical issue in 9 (31%), new emerging disease-gene association in 8 (27.6%) and clinical update in 3 (10.3%). The 29 new diagnoses increased the cumulative diagnostic yield of clinical WGS in the entire study cohort to 45.5% after reanalysis.

CONCLUSIONS

Unsolved paediatric WGS individuals with neurological disorders could obtain molecular diagnoses through reanalysis within a timeframe of 2-2.5 years. New disease gene, structural variations and deep intronic splice variants make a significant contribution to diagnostic yield. This approach can provide precise genetic counselling to positive reanalysis results and end a diagnostic odyssey.

Increasing the diagnostic yield of childhood glaucoma cases recruited into the 100,000 Genomes Project

Childhood glaucoma (CG) encompasses a heterogeneous group of genetic eye disorders that is responsible for approximately 5% of childhood blindness worldwide. Understanding the molecular aetiology is key to improving diagnosis, prognosis and unlocking the potential for optimising clinical management. In this study, we investigated 86 CG cases from 78 unrelated families of diverse ethnic backgrounds, recruited into the Genomics England 100,000 Genomes Project (GE100KGP) rare disease cohort, to improve the genetic diagnostic yield. Using the Genomics England/Genomic Medicine Centres (GE/GMC) diagnostic pipeline, 13 unrelated families were solved (13/78, 17%). Further interrogation using an expanded gene panel yielded a molecular diagnosis in 7 more unrelated families (7/78, 9%). This analysis effectively raises the total number of solved CG families in the GE100KGP to 26% (20/78 families). Twenty-five percent (5/20) of the solved families had primary congenital glaucoma (PCG), while 75% (15/20) had secondary CG; 53% of this group had non-acquired ocular anomalies (including iris hypoplasia, megalocornea, ectopia pupillae, retinal dystrophy, and refractive errors) and 47% had non-acquired systemic diseases such as cardiac abnormalities, hearing impairment, and developmental delay. CYP1B1 was the most frequently implicated gene, accounting for 55% (11/20) of the solved families. We identified two novel likely pathogenic variants in the TEK gene, in addition to one novel pathogenic copy number variant (CNV) in FOXC1. Variants that passed undetected in the GE100KGP diagnostic pipeline were likely due to limitations of the tiering process, the use of smaller gene panels during analysis, and the prioritisation of coding SNVs and indels over larger structural variants, CNVs, and non-coding variants.

Exome Sequencing has a high diagnostic rate in sporadic congenital hypopituitarism and reveals novel candidate genes

CONTEXT

The pituitary gland is key for childhood growth, puberty, and metabolism. Pituitary dysfunction is associated with a spectrum of phenotypes, from mild to severe. Congenital Hypopituitarism (CH) is the most commonly reported pediatric endocrine dysfunction with an incidence of 1:4000, yet low rates of genetic diagnosis have been reported.

OBJECTIVE

We aimed to unveil the genetic etiology of CH in a large cohort of patients from Argentina.

METHODS

We performed whole exome sequencing of 137 unrelated cases of CH, the largest cohort examined with this method to date.

RESULTS

Of the 137 cases, 19.1% and 16% carried pathogenic or likely pathogenic variants in known and new genes, respectively, while 28.2% carried variants of uncertain significance. This high yield was achieved through the integration of broad gene panels (genes described in animal models and/or other disorders), an unbiased candidate gene screen with a new bioinformatics pipeline (including genes high loss of function intolerance), and analysis of copy number variants. Three novel findings emerged. First, the most prevalent affected gene encodes the cell adhesion factor ROBO1. Affected children had a spectrum of phenotypes, consistent with a role beyond pituitary stalk interruption syndrome. Second, we found that CHD7 mutations also produce a phenotypic spectrum, not always associated with full CHARGE syndrome. Third, we add new evidence of pathogenicity in the genes PIBF1 and TBC1D32, and report 13 novel candidate genes associated with CH (e.g. PTPN6, ARID5B).

CONCLUSION

Overall, these results provide an unprecedented insight into the diverse genetic etiology of hypopituitarism.

Noncoding de novo mutations in SCN2A are associated with autism spectrum disorders

Coding de novo mutations (DNMs) contribute to the risk for autism spectrum disorders (ASD), but the contribution of noncoding DNMs remains relatively unexplored. Here we use whole genome sequencing (WGS) data of 12,411 individuals (including 3,508 probands and 2,218 unaffected siblings) from 3,357 families collected in Simons Foundation Powering Autism Research for Knowledge (SPARK) to detect DNMs associated with ASD, while examining Simons Simplex Collection (SSC) with 6383 individuals from 2274 families to replicate the results. For coding DNMs, SCN2A reached exome-wide significance (p=2.06×10-11) in SPARK. The 618 known dominant ASD genes as a group are strongly enriched for coding DNMs in cases than sibling controls (fold change=1.51, p =1.13×10-5 for SPARK; fold change=1.86, p =2.06×10-9 for SSC). For noncoding DNMs, we used two methods to assess statistical significance: a point-based test that analyzes sites with a Combined Annotation Dependent Depletion (CADD) score ≥15, and a segment-based test that analyzes 1kb genomic segments with segment-specific background mutation rates (inferred from expected rare mutations in Gnocchi genome constraint scores). The point-based test identified SCN2A as marginally significant (p=6.12×10-4) in SPARK, yet segment-based test identified CSMD1, RBFOX1 and CHD13 as exome-wide significant. We did not identify significant enrichment of noncoding DNMs (in all 1kb segments or those with Gnocchi>4) in the 618 known ASD genes as a group in cases than sibling controls. When combining evidence from both coding and noncoding DNMs, we found that SCN2A with 11 coding and 5 noncoding DNMs exhibited the strongest significance (p=4.15×10-13). In summary, we identified both coding and noncoding DNMs in SCN2A associated with ASD, while nominating additional candidates for further examination in future studies.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to American College of Medical Genetics/Association for Molecular Pathology criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes are poorly understood. We identified 168 rare CSSVs in blood-expressed genes in 112 individuals using genome sequencing, and studied their impact on splicing using RNA sequencing (RNA-seq). There was no evidence of a frameshift, nor of reduced expression consistent with nonsense-mediated decay, for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame insertions or deletions, 3.6% resulted in full exon skipping (in frame), and 0.6% resulted in full intron inclusion (in frame). Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. The predicted impact on splicing using (1) SpliceAI, (2) MaxEntScan, and (3) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63%, and 61% of CSSVs, respectively. In summary, approximately one in four rare CSSVs did not show evidence for LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Novel Synonymous Variant in IL7R Causes Preferential Expression of the Soluble Isoform

PURPOSE

The interleukin-7 receptor (IL-7R) is primarily expressed on lymphoid cells and plays a crucial role in the development, proliferation, and survival of T cells. Autosomal recessive mutations that disrupt IL-7Rα chain expression give rise to a severe combined immunodeficiency (SCID), which is characterized by lymphopenia and a T-B+NK+ phenotype. The objective here was to diagnose two siblings displaying the T-B+NK+ SCID phenotype as initial clinical genetic testing did not detect any variants in known SCID genes.

METHODS

Whole genome sequencing (WGS) was utilized to identify potential variants causing the SCID phenotype. Splicing prediction tools were employed to assess the deleterious impact of the mutation. Polymerase Chain Reaction (PCR), Sanger sequencing, flow cytometry, and ELISA were then used to validate the pathogenicity of the detected mutation.

RESULTS

We discovered a novel homozygous synonymous mutation in the IL7R gene. Our functional studies indicate that this variant is pathogenic, causing exon 6, which encodes the transmembrane domain, to be preferentially spliced out.

CONCLUSION

In this study, we identified a novel rare synonymous mutation causing a loss of IL-7Rα expression at the cellular membrane. This case demonstrates the value of reanalyzing genetic data based on the clinical phenotype and highlights the significance of functional studies in determining the pathogenicity of genetic variants.

CFAP47 is a novel causative gene implicated in X-linked polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is a well-described condition in which ~80% of cases have a genetic explanation, while the genetic basis of sporadic cystic kidney disease in adults remains unclear in ~30% of cases. This study aimed to identify novel genes associated with polycystic kidney disease (PKD) in patients with sporadic cystic kidney disease in which a clear genetic change was not identified in established genes. A next-generation sequencing panel analyzed known genes related to renal cysts in 118 sporadic cases, followed by whole-genome sequencing on 47 unrelated individuals without identified candidate variants. Three male patients were found to have rare missense variants in the X-linked gene Cilia And Flagella Associated Protein 47 (CFAP47). CFAP47 was expressed in primary cilia of human renal tubules, and knockout mice exhibited vacuolation of tubular cells and tubular dilation, providing evidence that CFAP47 is a causative gene involved in cyst formation. This discovery of CFAP47 as a newly identified gene associated with PKD, displaying X-linked inheritance, emphasizes the need for further cases to understand the role of CFAP47 in PKD.

Mutation analysis and clinical profile of South African patients with Neurofibromatosis type 1 (NF1) phenotype

Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic condition with complete age-dependent penetrance, variable expressivity and a global prevalence of ∼1/3,000. It is characteriszed by numerous café-au-lait macules, skin freckling in the inguinal or axillary regions, Lisch nodules of the iris, optic gliomas, neurofibromas, and tumour predisposition. The diagnostic testing strategy for NF1 includes testing for DNA single nucleotide variants (SNVs), copy number variants (CNVs) as well as RNA analysis for deep intronic and splice variants, which can cumulatively identify the causative variant in 95% of patients. In the present study, NF1 patients were screened using a next-generation sequencing (NGS) assay targeting NF1 exons and intron/exon boundaries for SNV and NF1 multiple ligation-dependent probe amplification (MLPA) analysis for CNV detection. Twenty-six unrelated Southern African patients clinically suspected of having NF1, based on the clinical diagnostic criteria developed by the National Institute of Health (NIH), were included in the current study. A detection rate of 58% (15/26) was obtained, with SNVs identified in 80% (12/15) using a targeted gene panel and NF1 gene deletion in 20% (3/15) identified using MLPA. Ten patients (38%) had no variants identified, although they met NF1 diagnostic criteria. One VUS was identified in this study in a patient that met NF1 diagnostic criteria, however there was no sufficient information to classify variant as pathogenic. The clinical features of Southern African patients with NF1 are similar to that of the known NF1 phenotype, with the exception of a lower frequency of plexiform neurofibromas and a higher frequency of developmental/intellectual disability compared to other cohorts. This is the first clinical and molecular characterisation of a Southern African ancestry NF1 cohort using both next-generation sequencing and MLPA analysis. A significant number of patients remained without a diagnosis following DNA-level testing. The current study offers a potential molecular testing strategy for our low resource environment that could benefit a significant proportion of patients who previously only received a clinical diagnosis without molecular confirmation.

Preclinical evaluation of CRISPR-based therapies for Noonan syndrome caused by deep-intronic LZTR1 variants

Gene variants in LZTR1 are implicated to cause Noonan syndrome associated with a severe and early-onset hypertrophic cardiomyopathy. Mechanistically, LZTR1 deficiency results in accumulation of RAS GTPases and, as a consequence, in RAS-MAPK signaling hyperactivity, thereby causing the Noonan syndrome-associated phenotype. Despite its epidemiological relevance, pharmacological as well as invasive therapies remain limited. Here, personalized CRISPR-Cas9 gene therapies might offer a novel alternative for a curative treatment in this patient cohort. In this study, by utilizing a patient-specific screening platform based on iPSC-derived cardiomyocytes from two Noonan syndrome patients, we evaluated different clinically translatable therapeutic approaches using small Cas9 orthologs targeting a deep-intronic LZTR1 variant to cure the disease-associated molecular pathology. Despite high editing efficiencies in cardiomyocyte cultures transduced with lentivirus or all-in-one adeno-associated viruses, we observed crucial differences in editing outcomes in proliferative iPSCs vs. non-proliferative cardiomyocytes. While editing in iPSCs rescued the phenotype, the same editing approaches did not robustly restore LZTR1 function in cardiomyocytes, indicating critical differences in the activity of DNA double-strand break repair mechanisms between proliferative and non-proliferative cell types and highlighting the importance of cell type-specific screens for testing CRISPR-Cas9 gene therapies.

Emerging applications of machine learning in genomic medicine and healthcare

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Combining full-length gene assay and SpliceAI to interpret the splicing impact of all possible SPINK1 coding variants

BACKGROUND

Single-nucleotide variants (SNVs) within gene coding sequences can significantly impact pre-mRNA splicing, bearing profound implications for pathogenic mechanisms and precision medicine. In this study, we aim to harness the well-established full-length gene splicing assay (FLGSA) in conjunction with SpliceAI to prospectively interpret the splicing effects of all potential coding SNVs within the four-exon SPINK1 gene, a gene associated with chronic pancreatitis.

RESULTS

Our study began with a retrospective analysis of 27 SPINK1 coding SNVs previously assessed using FLGSA, proceeded with a prospective analysis of 35 new FLGSA-tested SPINK1 coding SNVs, followed by data extrapolation, and ended with further validation. In total, we analyzed 67 SPINK1 coding SNVs, which account for 9.3% of the 720 possible coding SNVs. Among these 67 FLGSA-analyzed SNVs, 12 were found to impact splicing. Through detailed comparison of FLGSA results and SpliceAI predictions, we inferred that the remaining 653 untested coding SNVs in the SPINK1 gene are unlikely to significantly affect splicing. Of the 12 splice-altering events, nine produced both normally spliced and aberrantly spliced transcripts, while the remaining three only generated aberrantly spliced transcripts. These splice-impacting SNVs were found solely in exons 1 and 2, notably at the first and/or last coding nucleotides of these exons. Among the 12 splice-altering events, 11 were missense variants (2.17% of 506 potential missense variants), and one was synonymous (0.61% of 164 potential synonymous variants). Notably, adjusting the SpliceAI cut-off to 0.30 instead of the conventional 0.20 would improve specificity without reducing sensitivity.

CONCLUSIONS

By integrating FLGSA with SpliceAI, we have determined that less than 2% (1.67%) of all possible coding SNVs in SPINK1 significantly influence splicing outcomes. Our findings emphasize the critical importance of conducting splicing analysis within the broader genomic sequence context of the study gene and highlight the inherent uncertainties associated with intermediate SpliceAI scores (0.20 to 0.80). This study contributes to the field by being the first to prospectively interpret all potential coding SNVs in a disease-associated gene with a high degree of accuracy, representing a meaningful attempt at shifting from retrospective to prospective variant analysis in the era of exome and genome sequencing.

Bi-allelic truncating variants in CASP2 underlie a neurodevelopmental disorder with lissencephaly

Lissencephaly (LIS) is a malformation of cortical development due to deficient neuronal migration and abnormal formation of cerebral convolutions or gyri. Thirty-one LIS-associated genes have been previously described. Recently, biallelic pathogenic variants in CRADD and PIDD1, have associated with LIS impacting the previously established role of the PIDDosome in activating caspase-2. In this report, we describe biallelic truncating variants in CASP2, another subunit of PIDDosome complex. Seven patients from five independent families presenting with a neurodevelopmental phenotype were identified through GeneMatcher-facilitated international collaborations. Exome sequencing analysis was carried out and revealed two distinct novel homozygous (NM_032982.4:c.1156delT (p.Tyr386ThrfsTer25), and c.1174 C > T (p.Gln392Ter)) and compound heterozygous variants (c.[130 C > T];[876 + 1 G > T] p.[Arg44Ter];[?]) in CASP2 segregating within the families in a manner compatible with an autosomal recessive pattern. RNA studies of the c.876 + 1 G > T variant indicated usage of two cryptic splice donor sites, each introducing a premature stop codon. All patients from whom brain MRIs were available had a typical fronto-temporal LIS and pachygyria, remarkably resembling the CRADD and PIDD1-related neuroimaging findings. Other findings included developmental delay, attention deficit hyperactivity disorder, hypotonia, seizure, poor social skills, and autistic traits. In summary, we present patients with CASP2-related ID, anterior-predominant LIS, and pachygyria similar to previously reported patients with CRADD and PIDD1-related disorders, expanding the genetic spectrum of LIS and lending support that each component of the PIDDosome complex is critical for normal development of the human cerebral cortex and brain function.

Gene Distribution in Pediatric-Onset Inherited Peripheral Neuropathy: A Single Tertiary Center in Thailand

BACKGROUND

Inherited peripheral neuropathy presents a diagnostic and therapeutic challenge due to its association with mutations in over 100 genes. This condition leads to long-term disability and poses a substantial healthcare burden on society.

OBJECTIVE

This study aimed to investigate the distribution of genes and establish the genotype-phenotype correlations, focusing on pediatric-onset cases.

METHODS

Exome sequencing and other analytical techniques were employed to identify pathogenic variants, including duplication analysis of the PMP22 gene. Each patient underwent physical examination and electrophysiological studies. Genotypes were correlated with phenotypic features, such as age at disease onset and ulnar motor nerve conduction velocity.

RESULTS

We identified 35 patients with pediatric-onset inherited peripheral neuropathy. Pathogenic or likely pathogenic variants were confirmed in 24 out of 35 (68.6%) patients, with 4 of these variants being novel. A confirmed molecular diagnosis was achieved in 90.9% (10/11) of patients with demyelinating Charcot-Marie-Tooth disease (CMT) and 56.3% (9/16) of patients with axonal CMT. Among patients with infantile-onset CMT (≤2 years), the most common causative genes were MFN2 and NEFL, while GDAP1 and MFN2 were frequent causes among patients with childhood- or adolescent-onset CMT (3-9 years).

CONCLUSIONS

The MFN2 gene was the most commonly implicated gene, and the axonal type was predominant in this cohort of Thai patients with pediatric-onset inherited peripheral neuropathy.

[Genetic and phenotypic analysis of MYO15A rare variants associated with autosomal recessive hearing loss]

Objective:To analyze the phenotype and genotype characteristics of autosomal recessive hearing loss caused by MYO15A gene variants, and to provide genetic diagnosis and genetic counseling for patients and their families. Methods:Identification of MYO15A gene variants by next generation sequencing in two sporadic cases of hearing loss at Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. The sequence variants were verified by Sanger sequencing.The pathogenicity of these variants was determined according to the American College of Medical Genetics and Genomics（ACMG） variant classification guidelines, in conjuction with clinical data. Results:The probands of the two families have bilateral,severe or complete hearing loss.Four variants of MYO15A were identified, including one pathogenic variant that has been reported, two likely pathogenic variants,and one splicing variant of uncertain significance. Patient I carries c. 3524dupA（p. Ser1176Valfs*14）, a reported pathogenic variant, and a splicing variant c. 10082+3G>A of uncertain significance according to the ACMG guidelines. Patient I was treated with bilateral hearing aids with satisfactory effect, demonstrated average hearing thresholds of 37.5 dB in the right ear and 33.75 dB in the left ear. Patient Ⅱ carries c. 7441_7442del（p. Leu2481Glufs*86） and c. 10250_10252del（p. Ser3417del）,a pair of as likely pathogenic variants according to the ACMG guidelines. Patient Ⅱ, who underwent right cochlear implantation eight years ago, achieved scores of 9 on the Categorical Auditory Performance-Ⅱ（CAP-Ⅱ） and 5 on the Speech Intelligibility Rating（SIR）. Conclusion:This study's discovery of the rare c. 7441_7442del variant and the splicing variant c. 10082+3G>A in the MYO15A gene is closely associated with autosomal recessive hearing loss, expanding the MYO15A variant spectrum. Additionally, the pathogenicity assessment of the splicing variant facilitates classification of splicing variations.

Clinical and genetic analyses of a Swedish patient series diagnosed with ataxia

Hereditary ataxia is a heterogeneous group of complex neurological disorders. Next-generation sequencing methods have become a great help in clinical diagnostics, but it may remain challenging to determine if a genetic variant is the cause of the patient's disease. We compiled a consecutive single-center series of 87 patients from 76 families with progressive ataxia of known or unknown etiology. We investigated them clinically and genetically using whole exome or whole genome sequencing. Test methods were selected depending on family history, clinical phenotype, and availability. Genetic results were interpreted based on the American College of Medical Genetics criteria. For high-suspicion variants of uncertain significance, renewed bioinformatical and clinical evaluation was performed to assess the level of pathogenicity. Thirty (39.5%) of the 76 families had received a genetic diagnosis at the end of our study. We present the predominant etiologies of hereditary ataxia in a Swedish patient series. In two families, we established a clinical diagnosis, although the genetic variant was classified as "of uncertain significance" only, and in an additional three families, results are pending. We found a pathogenic variant in one family, but we suspect that it does not explain the complete clinical picture. We conclude that correctly interpreting genetic variants in complex neurogenetic diseases requires genetics and clinical expertise. The neurologist's careful phenotyping remains essential to confirm or reject a diagnosis, also by reassessing clinical findings after a candidate genetic variant is suggested. Collaboration between neurology and clinical genetics and combining clinical and research approaches optimizes diagnostic yield.

Waardenburg Syndrome: The Contribution of Next-Generation Sequencing to the Identification of Novel Causative Variants

Waardenburg syndrome (WS) is characterized by hearing loss and pigmentary abnormalities of the eyes, hair, and skin. The condition is genetically heterogeneous, and is classified into four clinical types differentiated by the presence of dystopia canthorum in type 1 and its absence in type 2. Additionally, limb musculoskeletal abnormalities and Hirschsprung disease differentiate types 3 and 4, respectively. Genes PAX3, MITF, SOX10, KITLG, EDNRB, and EDN3 are already known to be associated with WS. In WS, a certain degree of molecularly undetected patients remains, especially in type 2. This study aims to pinpoint causative variants using different NGS approaches in a cohort of 26 Brazilian probands with possible/probable diagnosis of WS1 (8) or WS2 (18). DNA from the patients was first analyzed by exome sequencing. Seven of these families were submitted to trio analysis. For inconclusive cases, we applied a targeted NGS panel targeting WS/neurocristopathies genes. Causative variants were detected in 20 of the 26 probands analyzed, these being five in PAX3, eight in MITF, two in SOX10, four in EDNRB, and one in ACTG1 (type 2 Baraitser-Winter syndrome, BWS2). In conclusion, in our cohort of patients, the detection rate of the causative variant was 77%, confirming the superior detection power of NGS in genetically heterogeneous diseases.

ODAD1 variants resulting from splice-site mutations retain partial function and cause primary ciliary dyskinesia with outer dynein arm defects

Primary ciliary dyskinesia (PCD) is a genetically heterogeneous disorder caused by defects in motile ciliary function and/or structure. Outer dynein arm docking complex subunit 1 (ODAD1) is an important component of the outer dynein arm docking complex (ODA-DC). To date, 13 likely pathogenic mutations of ODAD1 have been reported. However, the pathogenesis of ODAD1 mutations remains elusive. To investigate the pathogenesis of splice-site mutations in ODAD1 discovered in this study and those reported previously, molecular and functional analyses were performed. Whole-exome sequencing revealed a compound mutation in ODAD1 (c.71-2A>C; c.598-2A>C) in a patient with PCD, with c.598-2A>C being a novel mutation that resulted in two mutant transcripts. The compound mutation in ODAD1 (c.71-2A>C; c.598-2A>C) led to aberrant splicing that resulted in the absence of the wild-type ODAD1 and defects of the outer dynein arm in ciliary axonemes, causing a decrease in ciliary beat frequency. Furthermore, we demonstrated that the truncated proteins resulting from splice-site mutations in ODAD1 could retain partial function and inhibit the interaction between wild-type ODAD1 and ODAD3. The results of this study expand the mutational and clinical spectrum of PCD, provide more evidence for genetic counseling, and offer new insights into gene-based therapeutic strategies for PCD.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

Novel Genetic and Phenotypic Expansion in GOSR2-Related Progressive Myoclonus Epilepsy

Biallelic variants in the Golgi SNAP receptor complex member 2 gene (GOSR2) have been reported in progressive myoclonus epilepsy with neurodegeneration. Typical clinical features include ataxia and areflexia during early childhood, followed by seizures, scoliosis, dysarthria, and myoclonus. Here, we report two novel patients from unrelated families with a GOSR2-related disorder and novel genetic and clinical findings. The first patient, a male compound heterozygous for the GOSR2 splice site variant c.336+1G>A and the novel c.364G>A,p.Glu122Lys missense variant showed global developmental delay and seizures at the age of 2 years, followed by myoclonus at the age of 8 years with partial response to clonazepam. The second patient, a female homozygous for the GOSR2 founder variant p.Gly144Trp, showed only mild fine motor developmental delay and generalized tonic-clonic seizures triggered by infections during adolescence, with seizure remission on levetiracetam. The associated movement disorder progressed atypically slowly during adolescence compared to its usual speed, from initial intention tremor and myoclonus to ataxia, hyporeflexia, dysmetria, and dystonia. These findings expand the genotype-phenotype spectrum of GOSR2-related disorders and suggest that GOSR2 should be included in the consideration of monogenetic causes of dystonia, global developmental delay, and seizures.

A genetic study of a Brazilian cohort of patients with X-linked hypophosphatemia reveals no correlation between genotype and phenotype

Aim

X-linked hypophosphatemia (XLH) is the most common inherited form of rickets, and it is caused by pathogenic inactivating variants of the phosphate-regulating endopeptidase homolog X-linked (PHEX) gene. The main purpose of this study is to identify the presence of a genotype-phenotype correlation in a cohort of XLH patients.

Methods

This is a retrospective study including patients diagnosed with hypophosphatemic rickets, confirmed by clinical, radiological, and laboratory findings. Medical records were reviewed for phenotypic analyses. Genomic DNA was extracted from the peripheral blood lymphocytes, and PHEX sequencing was performed by exomic NGS sequencing. The Wilcoxon rank-sum test and the two-tailed Fisher's exact test were employed for the statistical analyses of this study.

Results

A total of 41 patients were included in this study, and 63.41% (26/41) of the patients were female. The mutation analyses identified 29.27% missense variants and 29.72% nonsense variants, most of them were considered deleterious (66.41%). Six novel deleterious variants in the PHEX gene were detected in seven patients. The median concentrations of pretreatment serum calcium, phosphorus, and parathyroid hormone (PTH) were not significantly different among patients with different genotypes. An orthopedic surgery due to bone deformity was required in 57.69%.

Conclusions

Our analysis did not identify any specific genotype as a predictor. No significant genotype-phenotype correlation was found, suggesting that the recognition of subjacent pathogenic mutation in the PHEX gene may have limited prognostic value. Despite this finding, genetic testing may be useful for identifying affected individuals early and providing appropriate treatment.

High diagnostic potential of short and long read genome sequencing with transcriptome analysis in exome-negative developmental disorders

Exome sequencing (ES) has become the method of choice for diagnosing rare diseases, while the availability of short-read genome sequencing (SR-GS) in a medical setting is increasing. In addition, new sequencing technologies, such as long-read genome sequencing (LR-GS) and transcriptome sequencing, are being increasingly used. However, the contribution of these techniques compared to widely used ES is not well established, particularly in regards to the analysis of non-coding regions. In a pilot study of five probands affected by an undiagnosed neurodevelopmental disorder, we performed trio-based short-read GS and long-read GS as well as case-only peripheral blood transcriptome sequencing. We identified three new genetic diagnoses, none of which affected the coding regions. More specifically, LR-GS identified a balanced inversion in NSD1, highlighting a rare mechanism of Sotos syndrome. SR-GS identified a homozygous deep intronic variant of KLHL7 resulting in a neoexon inclusion, and a de novo mosaic intronic 22-bp deletion in KMT2D, leading to the diagnosis of Perching and Kabuki syndromes, respectively. All three variants had a significant effect on the transcriptome, which showed decreased gene expression, mono-allelic expression and splicing defects, respectively, further validating the effect of these variants. Overall, in undiagnosed patients, the combination of short and long read GS allowed the detection of cryptic variations not or barely detectable by ES, making it a highly sensitive method at the cost of more complex bioinformatics approaches. Transcriptome sequencing is a valuable complement for the functional validation of variations, particularly in the non-coding genome.

Computational prediction of human deep intronic variation

BACKGROUND

The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce.

RESULTS

In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods.

CONCLUSIONS

Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.