Genomics England

Spontaneous coronary artery dissection (SCAD) is a cause of acute myocardial infarction predominantly affecting adult women. A proportion of SCAD cases are associated with rare heritable connective tissue disorders. Vascular EDS (vEDS), due to deleterious variants in COL3A1, is one of the most common of these. Our aim was to identify specificfeatures of SCAD in vEDS which may aid patient selection for genetic testing. A systematic review of published cases of individuals with SCAD and vEDS was conducted. Additionally, patients with SCAD and genetically confirmed vEDS (SCAD-vEDS) were identified through the UK national EDS service and UK SCAD registry. Data were collected on presentation, management and extra-cardiac findings. Angiography was compared with an age and sex-matched, exome sequenced, control cohort with SCAD but without vEDS (SCAD-nonvEDS). Data from ten SCAD-vEDS patients were identified. There was a lower average age of SCAD and higher proportion of males in individuals with SCAD-vEDS, however differences should be interpreted carefully given cohort size. Fifty-six cases of SCAD-vEDS were identified through systematic review. Systemic features were present in most but not all cases. This report presents a new, angiographically characterised case-control cohort along with a systematic review of the current literature. Whilst clinical differences appear between the SCAD-vEDS and SCAD-nonvEDS groups, these are insufficient to accurately distinguish SCAD-vEDS from the general SCAD population. All individuals with SCAD should be evaluated for underlying vEDS but clinical assessment will miss some cases. Wider genetic testing in some SCAD patients may be merited to enable appropriate management. Systematic review registration: https://www.crd.york.ac.uk/prospero/536751 Identifier: 536751.

Abstract Fomite-mediated and airborne transmission pathways play a significant role in the dissemination of healthcare associated pathogens, contributing to the burden of hospital-acquired infections (HAIs). Effective infection prevention and control therefore require robust surveillance approaches capable of capturing the complexity of air and surface microbial communities present in healthcare settings. However, to date such approaches are not widely employed. In this study we used a rapid and low-biomass optimized metagenomic workflow to detect clinically relevant pathogens, characterise genetic signatures such as antimicrobial resistance and virulence determinants, and profile the wider airborne microbiome, including unculturable taxa) essential for HAI surveillance. Air samples were collected from multiple clinical and non-clinical areas across three multi-storey healthcare units within a UK hospital, alongside to surface samples from a haematology/oncology ward with a documented history of outbreaks. Following DNA extraction and enrichment optimised for low-biomass samples, sequencing was performed using the Oxford Nanopore Technologies MinIon long-read platform. Metagenomics data were analysed using an in-house developed bioinformatics pipeline. Metagenomic profiling revealed high bacterial taxonomic diversity across sampled environments, with limited overlap between the airborne and surface communities. Approximately 0.3% of bacterial reads harboured a large variety of antimicrobial resistance genes (ARGs), virulence factors (VFs) and mobile genetics elements (MGEs) across different hospital sample groups. Notably, critical and emerging pathogens were detected across ten wards and were associated with clinically significant resistance determinants, including multiple bla OXA subtypes, vanA gene clusters, and multidrug efflux pumps conferring resistance to eight important classes of antibiotics, including carbapenems, cephalosporins, penams, and vancomycin. In addition, several plasmid replicons implemented in horizontal gene transfer (HGT) were identified within these pathogens, indicating an increased potential for the emergence and dissemination of multi-drug resistance within hospital associated microbial communities. Our findings highlight the importance of using rapid metagenomics-based methodologies for environmental surveillance in healthcare settings. Correspondingly, this study revealed that hospital air and surface microbiomes comprise complex and dynamic microbial communities that harbour divers e of ARGs and VFs with the potential for rapid transmission across the surface-air interface, particularly in high-risk/vulnerable patient areas where outbreaks are more likely to occur.

Background: Evidence of the diverse genetic architecture of dilated cardiomyopathy (DCM) continues to emerge and requires reassessment of the clinical relevance of implicated disease genes. Building on the 2019-2020 Clinical Genome Resource (ClinGen) evaluation, the DCM Gene Curation Expert Panel (GCEP) reconvened in 2024-2025 to conduct a reassessment of genes in DCM. Methods: The ClinGen semi-quantitative clinical validity classification framework was applied with specifications to DCM to classify genes into categories based upon strength of published evidence for a DCM phenotype. Previously curated genes were reassessed and newly reported gene-disease-mode of inheritance (MOI) relationships, termed ?curations,? were evaluated. Results: Sixty-eight genes were evaluated, inclusive of 72 unique gene-disease-MOI relationships across 51 previously evaluated and 17 newly assessed genes. Thirty-five curations were classified as high evidence (16 Definitive, 10 Strong, 9 Moderate), increasing by 16 from the prior assessment. Nine newly assessed genes were classified as high evidence, including BAG5, FLII, LMOD2, MYLK3, MYZAP, NRAP, PPA2, PPP1R13L, and RPL3L. Twelve genes (11 newly appraised) were rated as high evidence with an autosomal recessive (AR) MOI. Five re-evaluated genes from 2019-2020 had clinically significant changes in classification. Except for JPH2, for which curation was modified to separate autosomal dominant (-AD) and -AR MOI curations, clinically significant changes involved upgrades from low to high evidence categories (PLEKHM2, PRDM16, TBX20, TNNI3K), demonstrating the robustness of the ClinGen gene curation process over time. An additional 29 gene-disease-MOI curations were classified as Limited, including six newly evaluated genes and one new MOI for a previously evaluated gene, MYBPC3-AR; four were classified as No Known Disease Relationship, and four remained Disputed. Four previously evaluated genes were curated for both AD and AR MOIs, including JPH2 (AD-Strong, AR-Limited), LDB3 (AD-Limited, AR-Strong), MYBPC3 (AD-Limited, AR-Limited), and TNNI3 (AD- and AR- Strong). Conclusions: With substantial new evidence, the genetic architecture of DCM has rapidly expanded. This updated assessment of genes reported in DCM yielded 35 high evidence curations, an increase from 19 only five years ago. The results of this evidence-based evaluation process informs clinical interpretation of genetic information in the care of DCM patients and families.

The dopaminergic system has key roles in human physiology and is implicated in a broad range of neurological and neuropsychiatric conditions that are increasingly investigated using induced pluripotent stem cell-derived midbrain models. To determine similarities of such models to human systems, here we undertake single-cell and spatial profiling of first and second trimester fetal midbrain and compare it to in vitro midbrain models. Histological examination reveals that, by the second trimester, fetal midbrain tissue exhibits structural complexity comparable to that of adults. At the molecular level, single-cell profiling uncovers differences in cellular composition across models, with brain organoids most closely resembling late first trimester tissue - an observation supported by meta-integration of existing midbrain datasets. By reconstructing developmental trajectories of neuronal and astrocytic lineages, we map gene expression dynamics associated with maturation. Importantly, integration of spatial transcriptomics provides critical context for aligning organoid models, revealing that their spatial organization and intercellular signaling resemble the architecture and microenvironment of the second trimester midbrain. Ultimately, we leverage our findings to study Dopamine Transporter Deficiency Syndrome progression in patient-derived midbrain organoids, validating their relevance. Understanding the extent of human tissue recapitulation in midbrain laboratory models is essential to justify their use as biological proxies.

<h3></h3> With the increased demand for genomic testing within the NHS Genomic Medicine Service (GMS), we need to utilise the most cost-effective technologies, analysis and reporting pathways. For many clinical indications (including paediatric disorders), WGS is the most comprehensive test available – allowing a wide range of genes to be analysed simultaneously and detecting a range of variant types with higher resolution and sensitivity compared to microarray. In January 2025, SWGLH moved to a new testing pathway for the R27/R29 Paediatric disorder clinical indications, for all patients presenting with moderate+ intellectual disability or syndromic developmental disease, utilising WGS as a ‘one stop’ mainstreamed test available to paediatricians. Since January, data shows a reduction of over 100 array cases tested per month. Case numbers for R27/R29 WGS increased by ~35 WGS cases a month. The R27 WGS diagnostic yield remained consistent; 32% 2023/2024 vs 29% 2024/2025, indicating that referral specificity has been maintained despite an overall reduction in test numbers We present a pathway overview and our experience to date, including the clinician engagement strategy, analyst training and audit data. The utility of this testing strategy is demonstrated using cases examples.

Association studies have linked many genetic variants to a variety of phenotypes but understanding the biological mechanisms underlying these signals remains a major challenge. Since genes operate within complex networks, statistical interactions between genetic mutations that reflect biological pathways are expected to exist. However, their discovery has been hampered by the vast search space of variant combinations and the multiplicatively small expected effect sizes of interactions. To increase power, we created a test for interaction between single-nucleotide polymorphisms (SNPs) and groups of other variants with a direct effect on a phenotype aggregated in a polygenic score (PGS) which can be performed for any quantitative trait. In realistic simulations, this method avoids false positives and is well powered to find interaction networks. We apply it to 97 quantitative phenotypes in European samples in the UK Biobank and identify 144 independent interactions affecting 52 different traits, including important disease risk variants at genes such as APOE, FTO or TCF7L2. We develop approaches to refine identified signals and detect 38 pairwise interactions between SNPs. These include known interactions between ABO, FUT2 and TREH affecting alkaline phosphatase levels, which are shown to be part of a larger network including PIGC and FUT6, as well as an interaction for eosinophil levels between IL33 and ALOX15, two genes whose functional interaction has recently been implicated in asthma. Finally, we propose a method to partition PGSs according to the binding sites of more than 1100 transcription factors using the HOCOMOCO motif database and test for interactions involving functionally partitioned scores. We identify 12 interactions affecting eight traits, two of which directly reflect known regulatory relationships such as that between TCF7L2 (a key regulator of glucose metabolism) and the transcription factor KDM2A, which are known to interact functionally within the Wnt signalling pathway, affecting glycated haemoglobin levels. This work substantially extends the set of known epistatic effects for human phenotypes and shows how statistical interactions can reflect biological interdependencies between genes.

Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, eliminating the need for expert-labeled training datasets. Materials and Methods This retrospective and prospective multicenter study included 81 936 brain MRI examinations and corresponding radiology reports for adult patients at two UK National Health Service hospitals from January 2008 to December 2019 for training and internal testing and 1369 prospectively collected examinations between March 2022 and March 2024 from four separate National Health Service hospitals for external testing (ClinicalTrials.gov no. NCT04368481). A neuroradiology language model (NeuroBERT) was trained using self-supervised tasks to generate report embeddings. Convolutional neural networks (one per MRI sequence) were trained to map scans to embeddings by minimizing mean squared error loss. The framework then detected abnormalities in new examinations by scoring scans against query sentences using text-image similarity. Model diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The framework achieved an AUC of 0.95 (95% CI: 0.94, 0.97) for normal versus abnormal classification and generalized to external sites with examination-level AUCs of 0.90 (95% CI: 0.86, 0.93) in Bedford, 0.87 (95% CI: 0.83, 0.90) in Nottingham, 0.86 (95% CI: 0.83, 0.90) in Norwich, and 0.85 (95% CI: 0.81, 0.89) in Yeovil. In five zero-shot classification tasks-acute stroke, multiple sclerosis, intracranial hemorrhage, meningioma, and hydrocephalus-the framework achieved a mean AUC of 0.89 (range, 0.77-0.93). For visual-semantic image retrieval, mean precision was 0.84 among the top 15 images across seven pathologies. Conclusion The self-supervised text-vision framework accurately detected brain MRI abnormalities without expert-labeled datasets. Clinical trial registration no. NCT04368481 Keywords: Head and Neck, Unsupervised Learning, Convolutional Neural Network (CNN), Neuroradiology © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. See also commentary by Ghodasara in this issue.

ABSTRACT This study was conducted to quantify the variation in environmental impacts of strawberry production across Europe to inform breeders and fruit producers on practical ways to improve the sustainability of their products. We assessed the environmental impact of different strawberry genotypes and cultivation systems, including open field and protected systems, conducted by seven different partners in Europe. The Life Cycle Assessment (LCA) methodology was applied. Fifty‐seven strawberry genotypes were included in the analysis, covering 19 different field trials. The functional unit (FU) was 1 kg of freshly harvested ripe strawberry fruit at the farm gate, produced between 2017 and 2024. The results for the climate change impact category showed an average of 0.58 kg CO 2 eq./FU among all the genotypes analyzed. The highest value was 3.8 kg CO 2 eq./FU for a greenhouse system, and the lowest was 0.21 kg CO 2 eq./FU for a polyethylene‐covered tunnel system. The results highlighted the crucial roles of cultivation systems, genotype selection, produced yield, and various input and management practices in the environmental performance of strawberry production. The work was based on trials connected to the breeding and testing of strawberry genotypes. The results thus help breeders to develop high‐quality strawberry cultivars designed to meet sustainable production under different climatic environments by showing the critical environmental impacts associated with their products. The comparison of the environmental performance of different strawberry cultivation systems across Europe even provides a benchmark to support fruit producers and policymakers in decision‐making for shaping sustainable strawberry production in Europe.

PurposeGenomic sequencing offers the opportunity to screen for hundreds of rare genetic conditions. To minimize potential negative impact on families and clinical services, it is crucial to reduce false-positive results while prioritizing clinical utility. We present an automated variant prioritization approach in the Generation Study, a research study investigating genomic sequencing in 100,000 newborns in England. Prioritized variants will subsequently undergo manual review by a registered clinical scientist and a specialist clinician before being reported back to parents.MethodsWe assessed specificity of our automated variant prioritization approach in 34,410 samples not enriched for rare diseases and sensitivity in 546 samples from patients with diagnostic variants in genes relevant to newborn screening. We used coverage and copy-number variants callability metrics to evaluate variant detection.ResultsWe estimated that 3% to 5% of samples will have prioritized variants that require manual review and that <1% of cases will have reportable variants requiring further confirmation of the condition. Sensitivity in genes included in the Generation Study was estimated to be approximately 80%. Gene-level specificity results led to changes in variant prioritization rules and conditions that are included.ConclusionGene-specific assessment of variant prioritization is crucial to establish analytical validity prior to inclusion in genomic newborn screening.