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BACKGROUND: Whole-genome sequencing (WGS)-based methods for circulating tumor DNA (ctDNA) detection typically rely on tumor-informed identification of somatic single nucleotide variants (SNVs). Somatic structural variants (SVs) are another type of cancer-specific genomic alteration, which owing to their larger genomic footprint and unique breakpoint junctions, are easier to distinguish from sequencing noise than SNVs. They are, however, rarely used for ctDNA detection because of (1) artifacts from WGS procedures that SV callers may falsely interpret as genuine SVs. This makes it difficult to establish high-confidence SV catalogos from short-read tumor WGS and can cause false-positive ctDNA detections. (2) Lack of robust strategies to quantify SV-supporting reads in plasma WGS. To address these barriers and enable integration of SV biomarkers into WGS-based ctDNA detection, we present a bioinformatic framework for algorithmic curation of somatic SV calls from fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tumors, coupled with a novel approach for sensitive, accurate mapping and quantification of SV breakpoint-supporting reads in plasma WGS. METHODS: Tumor, normal and plasma WGS data from 144 patients with stage III colorectal cancer was used to establish the bioinformatic framework. This included ~30x WGS data from 1564 serially collected plasma samples. The framework was validated using tumor/normal/plasma WGS data from 32 patients with muscle-invasive bladder cancer. SV-based ctDNA detection was benchmarked against previously published SNV-based ctDNA results for the same samples. RESULTS: After curation of SV calls and quantification in plasma WGS, our SV-based approach enabled robust ctDNA detection with overall specificity exceeding 99% in plasma samples. Furthermore, we observed strong concordance (Pearson’s r > 0.93, p < 2.2 × 10− 16) between ctDNA-positive samples identified by our SV-based method and previous SNV-based analyses, validating the reliability of our approach. Finally, we demonstrated application of the method in an independent bladder cancer cohort, highlighting its generalizability and potential clinical use. CONCLUSIONS: We provide a bioinformatic framework that establishes somatic SVs as ultra-specific biomarkers for WGS-based, tumor-informed ctDNA detection. The approach delivers specific detection even when the SV catalogos are established from FFPE samples. The SV framework can stand alone or enhance SNV-based analysis pipelines.
Whole Genome Sequencing (WGS) is increasingly being used to enhance tuberculosis (TB) surveillance and management. However, evidence on how WGS shapes real-world decision-making remains limited. This study explored the utility of WGS in the context of a TB outbreak in Victoria, Australia. We conducted a case study to (1) describe a TB outbreak in Victoria using epidemiological and genomic data and (2) explore the perceived benefits and limitations of WGS through qualitative interviews with laboratory and public health professionals involved in the investigation. The interviews were analysed thematically. From 2017 - 2023, 36 people were linked to a large lineage 4 TB outbreak comprising 3 sub-clusters. WGS connected two patients who were initially not epidemiologically linked to the outbreak, prompting additional contact screening at a medical clinic. From interviews with 10 laboratory and public health professionals, WGS was considered a useful tool, although there was a gap between its potential and realised utility. WGS strengthened confidence in suspected transmission links, which was particularly valuable when epidemiological evidence was sparce or uncertain. This was relevant in this investigation where TB stigma, a prolonged timeframe, and cross-jurisdictional transmission were challenges. Barriers to public health action from WGS included long turnaround times, difficulties drawing conclusions from identical isolates, and uncertainties around public health follow-up actions. This case study demonstrates that WGS can inform meaningful public health action, while also identifying opportunities to improve its utility. WGS for public health should involve real-time sequencing along with steps to support the translation of findings into actions such as action-focused WGS training, mechanisms to support consistent follow-up, and improved record-keeping systems.
UNLABELLED: Salmonella is a major zoonotic foodborne pathogen, and antimicrobial resistance (AMR) in Salmonella presents a significant public health challenge. Compared with conventional antimicrobial susceptibility testing (AST), whole-genome sequencing (WGS) provides a more rapid and comprehensive approach to AMR characterization, thereby informing antimicrobial selection and supporting public health surveillance. In this study, Oxford Nanopore Technology (ONT)-based WGS was performed on 1,490 Salmonella isolates collected through nationwide surveillance in Taiwan in 2025. Genotypic resistance inferred from WGS data was compared with phenotypic AST results to assess the performance of ONT-WGS. Overall, WGS-inferred resistance showed high concordance with phenotypic resistance for most antimicrobials. However, major genotype-phenotype discordance was observed, attributed to four categories: (i) breakpoint-dependent classification, (ii) reduced or absent phenotypic expression of resistance genes, (iii) minimum inhibitory concentration (MIC) modulation by ramAp, and (iv) absence of known AMR determinants. Notable discrepancies included tigecycline resistance without known genetic determinants, nalidixic acid resistance linked to ramAp-mediated MIC elevation, and a high prevalence of colistin resistance (35.7%) in S. Enteritidis, with most resistant isolates lacking identifiable AMR determinants. Additionally, a significant proportion of ESBL- and AmpC-producing isolates were classified as susceptible or intermediate to cefotaxime and ceftazidime under CLSI criteria, highlighting the potential for misclassification and treatment failure. These findings demonstrate that ONT-WGS enables accurate and comprehensive AMR characterization by directly identifying resistance determinants and avoiding potential misclassification associated with breakpoint-based AST interpretations. When interpreted appropriately, WGS can support better antimicrobial selection and serve as a valuable alternative to conventional susceptibility testing. IMPORTANCE: Accurate prediction of antimicrobial resistance is essential for appropriate therapy and effective surveillance of Salmonella. However, discordance between genotype-based predictions and phenotypic antimicrobial susceptibility testing (AST) can complicate clinical interpretation. In this nationwide study of 1,490 Salmonella isolates, we show that Oxford Nanopore Technology-based whole-genome sequencing (ONT-WGS) provides rapid and comprehensive detection of antimicrobial resistance determinants with high concordance to phenotypic AST. We further identify four major mechanisms underlying genotype-phenotype discordance, including breakpoint-dependent classification, reduced or absent phenotypic expression of resistance genes, minimum inhibitory concentration (MIC) modulation by ramAp, and the absence of known AMR determinants. These findings demonstrate how WGS can complement conventional AST, improve interpretation of challenging susceptibility results, and strengthen genomic surveillance of emerging antimicrobial-resistant Salmonella.
UNLABELLED: Pseudomonas aeruginosa outbreaks frequently occur in intensive care units (ICUs). In particular, ICU patients requiring mechanical ventilation are vulnerable to P. aeruginosa ventilator-associated pneumonia, which is associated with high morbidity and mortality. Fast and accurate genotyping during the early stage is crucial to document and manage P. aeruginosa outbreaks at the ICU. In this study, we have evaluated the applicability of Oxford Nanopore whole-genome sequencing (WGS) for outbreak investigation and antimicrobial resistance (AMR) prediction. To evaluate whether a Nanopore-only WGS workflow was able to reproduce Illumina-confirmed transmission clusters, 19 P. aeruginosa isolates from ICUs at UZ Brussels (Belgium) that were previously sequenced with Illumina were sequenced using a Nanopore-only workflow based on the latest V14 chemistry, followed by bioinformatic analysis via BugSeq and MBioSEQ Ridom Typer. Although both bioinformatic platforms showed high concordance between Illumina and Nanopore data, MBioSEQ Ridom Typer yielded the lowest allelic distance (maximum one cgMLST allele), confirming all outbreak clusters. When applying the Nanopore-only workflow to longitudinally collected isolates, low genetic heterogeneity (maximum three cgMLST alleles) was observed between isolates from the same patient. WGS and subsequent outbreak analysis of 65 respiratory P. aeruginosa isolates collected from 38 different ICU patients across six Belgian hospitals during a 9-month period showed no intra- or inter-hospital transmission. When the Nanopore-only WGS data were used to predict AMR, there was high categorical agreement (95%) between AMR genotype and phenotype. These findings highlight the potential of Nanopore WGS as a rapid and accurate tool for outbreak investigation of P. aeruginosa. IMPORTANCE: In recent years, Nanopore sequencing has found its way to clinical laboratories because of its affordability, scalability, and, most importantly, its ability to obtain sequencing results in near-real time. However, despite improved raw read accuracies with the latest generation R10.4.1 flow cells, the question remains whether the achieved accuracy is sufficient for accurate bacterial outbreak investigation, particularly in high-risk settings such as intensive care units (ICUs). In this study, we show that Nanopore-only whole-genome sequencing (WGS) is able to match Illumina-only WGS in terms of accuracy for Pseudomonas aeruginosa outbreak investigation in the ICU setting, although important sequence type-dependent and even strain-specific methylation issues need to be resolved in order to guarantee this accuracy. By providing a fast and accurate workflow for reliable P. aeruginosa outbreak investigation, this study could pave the way for large-scale implementation of Nanopore-only WGS, leading to faster outbreak response times.
OBJECTIVE: Whole genome sequencing (WGS) can identify more druggable targets than the standard of care (SoC) panels, however, its health effects and costs are highly uncertain. Given the rapidly evolving treatment landscape and pricing, an iterative approach is crucial to continuously reassess evidence and adapt economic models. Our objective was to update a previously developed economic model for WGS. METHODS: We used a structured approach to identify and report model elements requiring updates, based on established tools and methodological guidance, and applied it to the probabilistic decision model by Simons et al.(2021), which compared SoC, WGS, and SoC followed by WGS in patients with inoperable stage IIIB, C/IV NSCLC in the Dutch setting. RESULTS: Updates included a new treatment (sotorasib), revised drug and diagnostic costs, and adherence to the latest guidelines. Drug and WGS diagnostics costs fell by 8% and 26%, respectively. SoC diagnostic prices increased by 17%. We explored the impact of the prevalence of druggable targets, effectiveness of off-label treatments, (academic-specific) diagnostic costs, and price negotiations. The ICER of WGS versus SoC decreased from €737,197 to €419,053/QALY. WGS would become cost-effective if diagnostic costs descended from €2,180 to €1,246 or if additional druggable targets were identified in ≥3.3% of patients. CONCLUSION: Our structured approach effectively identified items in the original analysis requiring updates and provides a foundation for further developing a checklist to guide iterative HTA. Continued monitoring and assessment of new treatment options, the dynamic diagnostics and costs throughout the life-cycle remain necessary to determine when WGS can be considered cost-effective.
Biobank-scale whole-genome sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We highlight the annotated genomic data structure (aGDS) format, substantially reducing the WGS data file size while enabling seamless integration of genomic and functional information for comprehensive WGS analyses. The aGDS format yielded 23 chromosome-specific files for the UK Biobank 500k WGS dataset, occupying only 1.10 tebibytes of storage. We develop the vcf2agds toolkit that streamlines the conversion of WGS data from VCF to aGDS format. Additionally, the STAARpipeline equipped with the aGDS files enabled scalable, comprehensive, and functionally informed WGS analysis, facilitating the detection of common and rare coding and noncoding phenotype-genotype associations. Overall, the vcf2agds toolkit and STAARpipeline provide a streamlined solution that facilitates efficient data management and analysis of biobank-scale WGS data across hundreds of thousands of samples.
OBJECTIVE: To enumerate and describe the effect of whole genome sequencing (WGS) on epidemiological investigations of healthcare-associated transmission of nontuberculous mycobacteria (NTM). DESIGN: Systematic review. METHODS: We performed a literature search using targeted search terms to identify articles meeting inclusion criteria. Data extraction of study characteristics and outcomes was performed by two independent researchers. The primary outcome was the author interpretation of WGS utility in the investigation of suspected healthcare-associated transmission of NTM. The secondary outcome was whether a transmission route was identified through WGS. RESULTS: Thirty-one studies were included in the final analysis with 28 (90%) concluding that WGS was helpful in transmission investigations and in 19 of these 28 (68%) WGS aided in identifying a transmission route. The most common identified transmission routes were water-borne point sources (10), heater-cooler units (6), patient-to-patient (4), and a healthcare worker (1). CONCLUSION: WGS is an informative tool in investigating healthcare transmission of NTM.
Hypervirulent Klebsiella pneumoniae (hvKP) represents an emerging clinical and public-health concern, particularly as hypervirulence increasingly converges with multidrug resistance. Current diagnostic approaches rely on phenotypic assays, such as the string test, or on whole-genome sequencing (WGS), both of which have limitations in specificity, turnaround time, standardization, and feasibility for routine surveillance. To address this gap, we developed a multiplex real-time PCR assay targeting key hvKP-associated virulence loci, including siderophore systems, hypermucoviscosity regulators, and additional markers linked to invasive potential. The assay was evaluated on 110 K. pneumoniae clinical isolates and 9 positive blood cultures, using WGS and the string test as comparators. The molecular panel demonstrated high concordance with WGS for principal virulence determinants, correctly identifying all high-virulence (score 4) profiles, and most intermediate profiles. Against WGS, the assay yielded a sensitivity of 82% and a specificity of 73%; performance against the string test was 96% and 87%, respectively. Direct testing from blood culture pellets yielded results consistent with both WGS and DNA-based PCR for the limited number of targets detected, supporting the technical feasibility of this approach. However, broader validation is needed to confirm performance in this specimen type. Overall, this multiplex PCR assay provides a targeted molecular screening approach for the rapid identification of hvKP-associated virulence profiles. Its agreement with genomic data supports its potential utility as an accessible complement to WGS for hvKP surveillance, although further workflow optimization will be required before broader routine implementation.IMPORTANCEThe global emergence of hypervirulent and multidrug-resistant K. pneumoniae represents a major public-health threat, as the convergence of virulence and antimicrobial resistance dramatically limits therapeutic options and increases the likelihood of severe, invasive, and potentially untreatable infections. Rapid identification of essential virulence determinants is therefore critical for timely clinical management and for preventing onward transmission. However, current diagnostic approaches are either insufficiently sensitive or require substantial resources, limiting their routine use. By providing a rapid and targeted molecular assay capable of detecting the principal loci associated with hypervirulent K. pneumoniae and by demonstrating the preliminary feasibility of its use directly on blood culture pellets previously identified as Klebsiella spp. by MALDI-TOF MS, this work provides a pragmatic approach for early virulence profiling. Implementation of such assays can significantly enhance epidemiological surveillance, support tailored patient management, and reduce the spread of high-risk K. pneumoniae lineages in both community and healthcare environments.
PURPOSE: Whole Genome Sequencing (WGS) comprehensively detects all drug-resistant mutants, which can help in the early initiation of specific treatment for the patient. But there is a need to evaluate the performance of WGS in comparison with Line Probe Assays (LPA) and Phenotypic Drug Susceptibility Tests (pDST). METHODS: Consecutive sputum samples (58) found positive for Mycobacterium tuberculosis (MTB) by GeneXpert were tested for first-and second-line LPA, pDST and WGS for anti-tubercular drugs. RESULTS: Of 58, 34 (58.6%) culture isolates were resistant to one or more drugs. Resistance detected by WGS was as follows: Isoniazid 23(39.6%), Rifampicin 21(36.2%), FQs 21(36.2%), Ethambutol 18(31%), Linezolid 12(20.6%), Streptomycin 8(13.8%), Kanamycin 6(10.3%), Amikacin and Capreomycin 5(8.6%), Para-amino salicylic acid 1(1.7%) and Ethionamide 1(1.7%). No resistance was detected to Pyrazinamide, Bedaquiline, Clofazimine, Delamanid and Pretomanid. Lineage 3(EAI) was the most predominant 23(39.6%) followed by Lineage 2: 13(22.4%). Intermediate Resistance (IR) and potential novel mutations were observed in a few cases, CONCLUSION: Overall accuracy for first-line drugs between pDST vs WGS was >98% &pDST vs LPA >95%, while for second-line drugs accuracy was >96% and >90% respectively. IR and potential novel mutations should be followed up closely to understand their clinical significance.
BACKGROUND: Reliance on complex, custom-built bioinformatics pipelines is a barrier to the implementation of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in high-burden settings in some low-income and middle-income countries (LMICs). Automated analysis pipelines could address this inequity in access to WGS-based diagnostics and surveillance. This study aimed to systematically evaluate the performance and usability of publicly available WGS pipelines for M tuberculosis. METHODS: We identified automated M tuberculosis WGS analysis pipelines through searches of PubMed and GitHub from database inception up to Aug 31, 2024. Accuracy, cost, accessibility, and scalability were assessed for each pipeline. We evaluated the accuracy of genotypic drug susceptibility testing (gDST) using publicly available sequences with phenotypic susceptibility data for 12 antituberculosis drugs. We estimated pooled sensitivity and specificity for each pipeline, across all drugs, by conducting a bivariate meta-analysis, with random effects representing between-drug variability. Lineage classifications were compared, and a previously epidemiologically well-characterised dataset was used to compare measures of genomic relatedness. FINDINGS: Among 28 candidate pipelines, 16 were excluded as they were unmaintained and inexecutable. 12 pipelines (11 compatible with Illumina and four compatible with Nanopore), all free to use, were included for evaluation. Six pipelines processed and stored data remotely, but for five of these six, scalability was limited by the need to upload sequences through web portals. For local processing pipelines, scalability was dependent on substantial local computational resources, data storage capacity, and command-line interfaces that limited user-friendliness. Only one of six remote-processing pipelines removed human DNA sequences before server upload. gDST was similarly accurate across ten of 11 Illumina-compatible pipelines and three of four Nanopore-compatible pipelines. All pipelines classified the main lineages consistently, although there were differences at sublineage resolution. Outputs from three of four pipelines reporting genomic relatedness were compatible with commonly cited single nucleotide polymorphism difference thresholds. INTERPRETATION: Numerous automated analysis pipelines capable of enhancing equity in M tuberculosis WGS are available. Given the overall similarities between the pipelines evaluated in this study in terms of gDST performance, lineage classification, and genomic relatedness inference, non-functional attributes such as availability, accessibility, scalability, and privacy could represent the point of difference for prospective users in LMICs with a high burden of tuberculosis. FUNDING: The Rhodes Trust, Wellcome, Ellison Institute of Technology, and the UK National Institute for Health and Care Research Oxford Biomedical Research Centre.
Histophilosis is an important cause of morbidity and mortality as well as antimicrobial use in feedlot cattle across North America. Detection of Histophilus somni by culture is challenging, and there is no standardized tool for distinguishing isolates that carry virulence factors most likely to contribute to disease. The DR2 repeat of H. somni-associated virulence factor 'immunoglobulin-binding protein A' (ibpA DR2) harbors a Fic domain that mediates host cell cytotoxicity and is essential for histophilosis. For rapid detection of ibpA DR2 in extracted DNA, we developed a real-time recombinase polymerase amplification (RPA) assay with a runtime of 24 min at 39 °C. DNA from H. somni-RPA-positive respiratory swabs (n = 73) was screened for ibpA DR2 using the novel RPA assay and long-read metagenomic sequencing, as well as nanopore whole-genome sequencing (WGS) of H. somni isolated from the same samples. IbpA DR2 was identified in 71% and 70% of tested samples using RPA and WGS, respectively, and in ≤41% of samples using metagenomic sequencing. The likelihood of detection by RPA did not differ (OR 1.1, 95% CI (0.42, 2.9), P > 0.99) from WGS; however, agreement between these assays was only fair (κ = 0.31). Conversely, RPA (OR 3.4, 95% CI (1.6, 8.2)) and WGS (OR 8.0, 95% CI (2.4, 42)) were more likely (P < 0.001) to detect ibpA DR2 than metagenomic sequencing, likely reflecting limited coverage of H. somni by metagenomics. This study demonstrated that RPA and long-read WGS detected ibpA DR2 with similar frequencies in extracted DNA and H. somni isolates, respectively. Further testing of non-target isolates confirmed the analytical specificity of ibpA DR2 to H. somni. Further investigation of the diagnostic validity for RPA-based ibpA DR2 detection is required in a larger cohort of field samples, as a rapid screening tool for H. somni most likely to contribute to disease.
Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.
Recent advances in ultra-low coverage whole-genome sequencing (WGS) of single cells have enabled detailed analysis of copy number variation at a throughput approaching that of single-cell RNA sequencing. However, downstream computational methods have not seen comparable advances and are largely adaptations of deep sequencing methodology with reduced precision. Here, we present ASCENT, a computational method built to take full advantage of modern direct tagmentation-based WGS at ultra-low depth. Using joint segmentation with high-resolution bins, we accurately detect small segments, achieving accurate copy number profiles even at 100 000 reads per cell. ASCENT implements true absolute copy state inference for single cells, based on statistical modeling of coverage rather than comparison to a reference, while taking variable segment copy state into account. Further, ASCENT implements per-segment copy-neutral loss of heterozygosity (LOH) calling without the need for non-tumor or bulk WGS reference. When applied to a pediatric B-ALL sample, ASCENT finds copy-neutral LOH in a small segment and a minor subclone defined by breakpoints missed in bulk WGS. Thus, by applying appropriate computational methods, single-cell WGS provides clear advantages over bulk, even at a relatively low cell number and sequencing depth.
UNLABELLED: Cronobacter species, especially C. sakazakii and C. malonaticus, are opportunistic pathogens that are linked to severe infections in infants with high case fatality rates. In this study, we investigated whole genome sequencing (WGS) analysis approaches, specifically 7-gene multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST) to subtype Cronobacter isolates. We analyzed a comprehensive set of 743 Cronobacter isolates derived from clinical, food, and environmental sources. We also evaluated high-quality single nucleotide polymorphism (hqSNP), cgMLST, and wgMLST to cluster epidemiologically related and differentiate sporadic C. sakazakii isolates. Our results indicate that both cgMLST and wgMLST accurately identify closely related isolates and are consistent with epidemiological findings. The allele-based analyses were also comparable with hqSNP analyses, the current gold standard. Our workflow also outputs 7-gene MLST allele calls, Cronobacter sequence types, and clonal complexes, which may be useful for historic comparisons during outbreak investigations. Following the recent classification of Cronobacter infections as nationally notifiable in the United States, our findings demonstrate the efficacy of WGS-based approaches within the PulseNet framework to improve outbreak detection and response strategies for Cronobacter. IMPORTANCE: Cronobacter species, specifically C. sakazakii and C. malonaticus, are opportunistic pathogens linked to severe infections in infants with high case fatality rates. This study highlights the critical importance of advanced molecular techniques in public health surveillance, using whole genome sequencing (WGS) methodologies such as multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST). The validation of these WGS-based approaches within the PulseNet framework is timely, especially following the recent classification of Cronobacter infections as nationally notifiable in the United States. WGS methods not only enhance outbreak detection but can also inform public health guidance aimed at preventing infections and reducing mortality in vulnerable populations, especially infants. Our research supports implementation of cgMLST as a standardized approach for routine PulseNet surveillance of Cronobacter, with wgMLST and hqSNP analyses providing additional discriminatory power for outbreak investigations and high resolution phylogenetic analysis.
BACKGROUND: Identifying the genetic basis of hypotonia and muscle weakness is critical for patient management and family counseling. However, diagnosis is often hindered by diverse genomic alterations, including repeat expansions, structural variants (SVs), and methylation defects. Standard-of-care testing, largely based on short-read sequencing, is limited in its ability to detect this heterogeneous variation landscape, leaving many patients undiagnosed or requiring lengthy sequential testing. Long-read sequencing represents a promising solution. However, its application as a first-tier diagnostic assay for hypotonia remains unexplored. METHODS: We retrospectively analyzed 227 patients with hypotonia to assess diagnostic yield, time-to-diagnosis, and costs associated with standard-of-care testing. A long-read whole-genome sequencing (LR-WGS) workflow with targeted analysis of hypotonia-associated genes was developed to detect and prioritize pathogenic SNVs, SVs, and CNVs, repeat expansions, and methylation changes at key disease loci. The workflow was validated in a reference-positive cohort with known diagnoses (n = 15) and applied to an unsolved cohort (n = 14). Variant interpretation followed ACMG guidelines and was confirmed with orthogonal methods. RESULTS: Standard-of-care testing achieved a diagnostic yield of 42% with an average time-to-diagnosis of 68.7 days; however, 30% of diagnosed patients experienced significant delays (average 169 days) due to sequential testing. The LR-WGS based approach identified all known pathogenic variants in the positive cohort, including SMN1 deletions, methylation defects at 15q11.2/Prader-Willi locus, FMR1 repeat expansions, and sequence and copy-number variants in > 100 genes underlying myopathies and muscular dystrophies. The targeted long-read pipeline reduced prioritized variant calls by 97.9-99.9% and, in the unsolved cohort, yielded one definitive diagnosis (de novo COL6A3 deletion) and one possible diagnosis (aberrant methylation and copy number at POMK), for an additional 14% yield. Among patients diagnosed after sequential testing (n = 29), LR-WGS is expected to reduce time-to-diagnosis by ~ 85% and decrease cumulative diagnostic delays, with projected healthcare cost savings of $396,000-439,000. Across the entire 227 patient cohort, LR-WGS is anticipated to reduce testing costs by 6.5%, yielding an average savings of $105 per patient. CONCLUSIONS: LR-WGS enables comprehensive discovery of genomic and epigenomic variants in hypotonia and muscle weakness, improving diagnostic yield, shortening diagnostic timelines, and reducing costs compared with current standard-of-care testing.
BACKGROUND: Neurodegenerative disorders are clinically and genetically heterogeneous, characterized by progressive neuronal loss and multidomain functional decline. Despite a presumed genetic etiology, a substantial proportion of cases remain molecularly undiagnosed. OBJECTIVE: The aim was to identify the genetic cause of an early-onset neurodegenerative disorder presenting with ataxia and cognitive impairment. METHODS: Rare copy-number variants were detected via short-read whole-genome sequencing (WGS), with candidate structural models inferred using long-read WGS. We performed transcriptomic profiling of peripheral blood leukocytes by RNA sequencing, with validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: We identified a de novo copy-number gain at 16p13.3. Combined copy-number profiling and long-read WGS suggested a candidate model comprising a triplicated segment in tandem with a proximal duplication, joined to a distal duplication via an inverted junction. Transcriptomic analysis demonstrated significant upregulation of ATP6V0C, AMDHD2, and PDPK1. CONCLUSIONS: These findings support a role for structural variation in early-onset neurodegeneration and highlight the value of combining short-read copy-number profiling with long-read WGS to detect and characterize complex genomic rearrangements. © 2026 International Parkinson and Movement Disorder Society.
BACKGROUND: Surveillance of the invasive disease burden caused by Streptococcus pneumoniae in England is performed by the UK Health Security Agency (UKHSA). In 2017, UKHSA switched from phenotypic methods to whole-genome sequencing (WGS) approaches for pneumococcal surveillance. Here, we present the first results of national WGS surveillance, up to the start of the COVID-19 pandemic, with the aim of describing the population genomics of this important pathogen. METHODS: We examined prospective national surveillance data from England, using bacterial isolates from cases of invasive pneumococcal disease (IPD) submitted to the national reference laboratory at UKHSA. A bioinformatic pipeline was developed to quality control WGS data and routinely report species and serotype. We assembled isolate data, assigned global pneumococcal sequencing clusters (GPSCs), and predicted antimicrobial resistance (AMR) profiles for isolates that passed further quality control. We collected additional data on patient outcomes and characteristics using enhanced surveillance questionnaires completed by patients' general practitioners. We used logistic regression analysis to assess the effects of various genomic and patient characteristics on the outcomes of IPD. FINDINGS: In England, between July 1, 2017, and Feb 29, 2020, there were 15 400 cases of IPD. From these cases, 13 749 (89·3%) isolates were sequenced, passed quality control, and were included in analyses. Serotype diversity was high during the study period, with 2751 (20%) isolates serotyped as 13-valent pneumococcal conjugate vaccine (PCV13) types, whereas serotype 8 was the most prevalent serotype (n=3074 [22·4%]) overall. There were 157 GPSCs within the collection, with GSPC3 the most common, encompassing 98·7% (3033 of 3074) of serotype 8 isolates. Most isolates (n=10 198 [74·2%]) did not contain AMR-associated genes. Resistance to co-trimoxazole was the most frequently predicted resistance (n=2331 [17%]), followed by resistance to tetracycline (n=1199 [8·7%]) and β-lactams (n=1149 [8·4%]). Logistic regression analysis found the presence of AMR-associated genes significantly increased the odds of patient death (odds ratio 1·18, 95% CI 1·01-1·38). Some GPSCs were also associated with a significant increase in the odds of patient death, such as GPSC12 (1·88, 1·48-2·38). Isolates from 2018 were associated with a significant increase in the odds of patient death (1·12, 1·00-1·25), whereas younger patient age was significantly associated with a reduction in the odds of patient death compared with being aged 85 years or older. INTERPRETATION: WGS-based surveillance has allowed us to interrogate country-wide population dynamics driving changes in pneumococcal serotype frequency. Here, we observe a stable but diverse population before the COVID-19 pandemic restrictions were enforced in England, with low rates of AMR. These findings will provide the baseline for pandemic and post-pandemic data, to collectively inform implementation and development of the vaccination programme within the country. FUNDING: None.