Variant Prioritization Research Articles

Increased Diagnostic Yield by Reanalysis of Whole Exome Sequencing Data in Mitochondrial Disease.

The genetic diagnosis of mitochondrial disorders is complicated by its genetic and phenotypic complexity. Next generation sequencing techniques have much improved the diagnostic yield for these conditions. A cohort of individuals with multiple respiratory chain deficiencies, reported in the literature 10 years ago, had a diagnostic rate of 60% by whole exome sequencing (WES) but 40% remained undiagnosed. We aimed to identify a genetic diagnosis by reanalysis of the WES data for the undiagnosed arm of this 10-year-old cohort of patients with suspected mitochondrial disorders. The WES data was transferred and processed by the RD-Connect Genome-Phenome Analysis Platform (GPAP) using their standardized pipeline. Variant prioritisation was carried out on the RD-Connect GPAP. Singleton WES data from 14 individuals was reanalysed. We identified a possible or likely genetic diagnosis in 8 patients (8/14, 57%). The variants identified were in a combination of mitochondrial DNA (n = 1, MT-TN), nuclear encoded mitochondrial genes (n = 2, PDHA1, and SUCLA2) and nuclear genes associated with nonmitochondrial disorders (n = 5, PNPLA2, CDC40, NBAS and SLC7A7). Variants in both the NBAS and CDC40 genes were established as disease causing after the original cohort was published. We increased the diagnostic yield for the original cohort by 15% without generating any further genomic data. In the era of multiomics we highlight that reanalysis of existing WES data is a valid tool for generating additional diagnosis in patients with suspected mitochondrial disease, particularly when more time has passed to allow for new bioinformatic pipelines to emerge, for the development of new tools in variant interpretation aiding in reclassification of variants and the expansion of scientific knowledge on additional genes.

Exploring the genetic and epigenetic underpinnings of early-onset cancers: Variant prioritization for long read whole genome sequencing from family cancer pedigrees.

Despite significant advances in our understanding of genetic cancer susceptibility, known inherited cancer predisposition syndromes explain at most 20% of early-onset cancers. As early-onset cancer prevalence continues to increase, the need to assess previously inaccessible areas of the human genome, harnessing a trio or quad family-based architecture for variant filtration, may reveal further insights into cancer susceptibility. To assess a broader spectrum of variation than can be ascertained by multi-gene panel sequencing, or even whole genome sequencing with short reads, we employed long read whole genome sequencing using an Oxford Nanopore Technology (ONT) PromethION of 3 families containing an early-onset cancer proband using a trio or quad family architecture. Analysis included 2 early-onset colorectal cancer family trios and one quad consisting of two siblings with testicular cancer, all with unaffected parents. Structural variants (SVs), epigenetic profiles and single nucleotide variants (SNVs) were determined for each individual, and a filtering strategy was employed to refine and prioritize candidate variants based on the family architecture. The family architecture enabled us to focus on inapposite variants while filtering variants shared with the unaffected parents, significantly decreasing background variation that can hamper identification of potentially disease causing differences. Candidate d e novo and compound heterozygous variants were identified in this way. Gene expression, in matched neoplastic and pre-neoplastic lesions, was assessed for one trio. Our study demonstrates the feasibility of a streamlined analysis of genomic variants from long read ONT whole genome sequencing and a way to prioritize key variants for further evaluation of pathogenicity, while revealing what may be missing from panel based analyses.

SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.

Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

BackgroundAs genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype.ResultsIn this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation.ConclusionsGaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Integrative annotation scores of variants for impact on RNA binding protein activities.

The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. INCA is implemented in R and is available at https://github.com/keleslab/INCA.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BackgroundIn the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.ResultsWe trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.Conclusions3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

SscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

Exome sequencing improves the molecular diagnostics of paediatric unexplained neurodevelopmental disorders

BackgroundNeurodevelopmental disorders (NDDs) and/or associated multiple congenital abnormalities (MCAs) represent a genetically heterogeneous group of conditions with an adverse prognosis for the quality of intellectual and social abilities and common daily functioning. The rapid development of exome sequencing (ES) techniques, together with trio-based analysis, nowadays leads to up to 50% diagnostic yield. Therefore, it is considered as the state-of-the-art approach in these diagnoses.ResultsIn our study, we present the results of ES in a cohort of 85 families with 90 children with severe NDDs and MCAs. The interconnection of the in-house bioinformatic pipeline and a unique algorithm for variant prioritization resulted in a diagnostic yield of up to 48.9% (44/90), including rare and novel causative variants (41/90) and intragenic copy-number variations (CNVs) (3/90). Of the total number of 47 causative variants, 53.2% (25/47) were novel, highlighting the clinical benefit of ES for unexplained NDDs. Moreover, trio-based ES was verified as a reliable tool for the detection of rare CNVs, ranging from intragenic exon deletions (GRIN2A, ZC4H2 genes) to a 6-Mb duplication. The functional analysis using PANTHER Gene Ontology confirmed the involvement of genes with causative variants in a wide spectrum of developmental processes and molecular pathways, which form essential structural and functional components of the central nervous system.ConclusionTaken together, we present one of the first ES studies of this scale from the central European region. Based on the high diagnostic yield for paediatric NDDs in this study, 48.9%, we confirm trio-based ES as an effective and reliable first-tier diagnostic test in the genetic evaluation of children with NDDs.

Contribution of genetic variants in the development of familial premature coronary artery disease in a cohort of cardiac patients.

Coronary artery disease (CAD), the most prevalent cardiovascular disease, is the leading cause of death worldwide. Heritable factors play a significant role in the pathogenesis of CAD. It has been proposed that approximately one-third of patients with CAD have a positive family history, and individuals with such history are at ~1.5-fold increased risk of CAD in their lifespans. Accordingly, the long-recognized familial clustering of CAD is a strong risk factor for this disease. Our study aimed to identify candidate genetic variants contributing to CAD by studying a cohort of 60 large Iranian families with at least two members in different generations afflicted with premature CAD (PCAD), defined as established disease at ≤45 years in men and ≤55 years in women. Exome sequencing was performed for a subset of the affected individuals, followed by prioritization and Sanger sequencing of candidate variants in all available family members. Subsequently, apparently healthy carriers of potential risk variants underwent coronary computed tomography angiography (CCTA), followed by co-segregation analysis of the combined data. Putative causal variants were identified in seven genes, ABCG8, CD36, CYP27A1, PIK3C2G, RASSF9, RYR2, and ZFYVE21, co-segregating with familial PCAD in seven unrelated families. Among these, PIK3C2G, RASSF9, and ZFYVE21 are novel candidate CAD susceptibility genes. Our findings indicate that rare variants in genes identified in this study are involved in CAD development.

Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis

Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduces a novel metric, the Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant explaining the patient’s clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared to a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77/82) of the family-based exomes and in the first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28/35) of the cases. The SAP score, which is independent of detected variants, demonstrates comparable performance to Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be utilized in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.

Matrisome and Immune Pathways Contribute to Extreme Vascular Outcomes in Williams-Beuren Syndrome.

Supravalvar aortic stenosis (SVAS) is a characteristic feature of Williams-Beuren syndrome (WBS). Its severity varies: ~20% of people with Williams-Beuren syndrome have SVAS requiring surgical intervention, whereas ~35% have no appreciable SVAS. The remaining individuals have SVAS of intermediate severity. Little is known about genetic modifiers that contribute to this variability. We performed genome sequencing on 473 individuals with Williams-Beuren syndrome and developed strategies for modifier discovery in this rare disease population. Approaches include extreme phenotyping and nonsynonymous variant prioritization, followed by gene set enrichment and pathway-level association tests. We next used GTEx v8 and proteomic data sets to verify expression of candidate modifiers in relevant tissues. Finally, we evaluated overlap between the genes/pathways identified here and those ascertained through larger aortic disease/trait genome-wide association studies. We show that SVAS severity in Williams-Beuren syndrome is associated with increased frequency of common and rarer variants in matrisome and immune pathways. Two implicated matrisome genes (ACAN and LTBP4) were uniquely expressed in the aorta. Many genes in the identified pathways were previously reported in genome-wide association studies for aneurysm, bicuspid aortic valve, or aortic size. Smaller sample sizes in rare disease studies necessitate new approaches to detect modifiers. Our strategies identified variation in matrisome and immune pathways that are associated with SVAS severity. These findings suggest that, like other aortopathies, SVAS may be influenced by the balance of synthesis and degradation of matrisome proteins. Leveraging multiomic data and results from larger aorta-focused genome-wide association studies may accelerate modifier discovery for rare aortopathies like SVAS.

Combining a prioritization strategy and functional studies nominates 5’UTR variants underlying inherited retinal disease

Background5’ untranslated regions (5’UTRs) are essential modulators of protein translation. Predicting the impact of 5’UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5’UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).MethodsWe performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5’UTRs by different whole exome sequencing (WES) kits. The selected 5’UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5’UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.ResultsIsoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5’UTR compared to that of their canonical isoform. Depending on the probe design, 3–20% of IRD genes have 5’UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5’UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.ConclusionsThis study demonstrates the importance of 5’UTR variants implicated in IRDs and provides a systematic approach for 5’UTR annotation and validation that is applicable to other inherited diseases.

FAVOR-GPT: a generative natural language interface to whole genome variant functional annotations.

Functional Annotation of genomic Variants Online Resources (FAVOR) offers multi-faceted, whole genome variant functional annotations, which is essential for Whole Genome and Exome Sequencing (WGS/WES) analysis and the functional prioritization of disease-associated variants. A versatile chatbot designed to facilitate informative interpretation and interactive, user-centric summary of the whole genome variant functional annotation data in the FAVOR database is needed. We have developed FAVOR-GPT, a generative natural language interface powered by integrating large language models (LLMs) and FAVOR. It is developed based on the Retrieval Augmented Generation (RAG) approach, and complements the original FAVOR portal, enhancing usability for users, especially those without specialized expertise. FAVOR-GPT simplifies raw annotations by providing interpretable explanations and result summaries in response to the user's prompt. It shows high accuracy when cross-referencing with the FAVOR database, underscoring the robustness of the retrieval framework. Researchers can access FAVOR-GPT at FAVOR's main website (https://favor.genohub.org).

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

Integration of multi-omics technologies for molecular diagnosis in ataxia patients.

Background: Episodic ataxias are rare neurological disorders characterized by recurring episodes of imbalance and coordination difficulties. Obtaining definitive molecular diagnoses poses challenges, as clinical presentation is highly heterogeneous, and literature on the underlying genetics is limited. While the advent of high-throughput sequencing technologies has significantly contributed to Mendelian disorders genetics, interpretation of variants of uncertain significance and other limitations inherent to individual methods still leaves many patients undiagnosed. This study aimed to investigate the utility of multi-omics for the identification and validation of molecular candidates in a cohort of complex cases of ataxia with episodic presentation. Methods: Eight patients lacking molecular diagnosis despite extensive clinical examination were recruited following standard genetic testing. Whole genome and RNA sequencing were performed on samples isolated from peripheral blood mononuclear cells. Integration of expression and splicing data facilitated genomic variants prioritization. Subsequently, long-read sequencing played a crucial role in the validation of those candidate variants. Results: Whole genome sequencing uncovered pathogenic variants in four genes (SPG7, ATXN2, ELOVL4, PMPCB). A missense and a nonsense variant, both previously reported as likely pathogenic, configured in trans in individual #1 (SPG7: c.2228T>C/p.I743T, c.1861C>T/p.Q621*). An ATXN2 microsatellite expansion (CAG32) in another late-onset case. In two separate individuals, intronic variants near splice sites (ELOVL4: c.541 + 5G>A; PMPCB: c.1154 + 5G>C) were predicted to induce loss-of-function splicing, but had never been reported as disease-causing. Long-read sequencing confirmed the compound heterozygous variants configuration, repeat expansion length, as well as splicing landscape for those pathogenic variants. A potential genetic modifier of the ATXN2 expansion was discovered in ZFYVE26 (c.3022C>T/p.R1008*). Conclusion: Despite failure to identify pathogenic variants through clinical genetic testing, the multi-omics approach enabled the molecular diagnosis in 50% of patients, also giving valuable insights for variant prioritization in remaining cases. The findings demonstrate the value of long-read sequencing for the validation of candidate variants in various scenarios. Our study demonstrates the effectiveness of leveraging complementary omics technologies to unravel the underlying genetics in patients with unresolved rare diseases such as ataxia. Molecular diagnoses not only hold significant promise in improving patient care management, but also alleviates the burden of diagnostic odysseys, more broadly enhancing quality of life.

Next-generation sequencing and bioinformatics in rare movement disorders.

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Gaining Insights into Inherited Bleeding Disorders of Complex Etiology in Pediatric Patients: Whole-Exome Sequencing as First-Line Investigation Tool.

Investigation of the molecular basis of inherited bleeding disorders (IBD) is mostly performed with gene panel sequencing. However, the continuous discovery of new related genes underlies the limitation of this approach. This study aimed to identify genetic variants responsible for IBD in pediatric patients using whole-exome sequencing (WES), and to provide a detailed description and reclassification of candidate variants. WES was performed for 18 pediatric patients, and variants were filtered using a first-line list of 290 genes. Variant prioritization was discussed in a multidisciplinary team based on genotype-phenotype correlation, and segregation studies were performed with available family members. The study identified 22 candidate variants in 17 out of 18 patients (94%). Eleven patients had complete genotype-phenotype correlation, resulting in a diagnostic yield of 61%, 5 (28%) were classified as partially solved, and 2 (11%) remained unsolved. Variants were identified in platelet (ACTN1, ANKRD26, CYCS, GATA1, GFI1B, ITGA2, NBEAL2, RUNX1, SRC, TUBB1), bleeding (APOLD1), and coagulation (F7, F8, F11, VWF) genes. Notably, 9 out of 22 (41%) variants were previously unreported. Variant pathogenicity was assessed according to the American College of Medical Genetics and Genomics guidelines and reclassification of three variants based on family segregation evidence, resulting in the identification of 10 pathogenic or likely pathogenic variants, 6 variants of uncertain significance, and 6 benign or likely benign variants. This study demonstrated the high potential of WES in identifying rare molecular defects causing IBD in pediatric patients, improving their management, prognosis, and treatment, particularly for patients at risk of malignancy and/or bleeding due to invasive procedures.