Genome Medicine最新文献

{"title":"STREAM-PRS: a multi-tool pipeline for streamlining polygenic risk score computation.","authors":"Sara Becelaere, Yasmina Abakkouy, Deborah Sarah Jans, Margaux David, Séverine Vermeire, Isabelle Cleynen","doi":"10.1186/s13073-025-01539-0","DOIUrl":"10.1186/s13073-025-01539-0","url":null,"abstract":"<p><strong>Background: </strong>Polygenic risk scores (PRS) offer an elegant approach to estimating an individual's genetic predisposition to a given disease or trait. Numerous tools are available for PRS calculation, each applying different strategies to account for linkage disequilibrium and effect size shrinkage. No single tool is inherently superior. Therefore, multiple tools should be tested to identify the one that best suits the research question. Additionally, challenges such as population stratification and PRS portability further complicate the field. Here, we developed STREAM-PRS, a PRS pipeline designed to calculate scores using five popular tools: PRSice-2, PRS-CS, LDpred2, lassosum, and lassosum2.</p><p><strong>Methods: </strong>STREAM-PRS first computes scores under various settings in a training dataset. The selected variants are subsequently used for score calculation in the test dataset, followed by PC correction and standardization to improve portability across different centers. Finally, the pipeline determines the best PRS tool and settings based on the variance explained (R²) in the test dataset. To demonstrate this PRS pipeline, we applied it to an in-house inflammatory bowel disease (IBD) cohort consisting of 3192 IBD cases and 822 controls. In total, 472 scores were created using The 1000 Genomes non-Finnish European subpopulation as training data and applied to UK Biobank data as the test dataset.</p><p><strong>Results: </strong>Using STREAM-PRS for 472 scores across the 5 PRS tools with 404 individuals in the training and 1000 individuals in the test dataset takes approximately 20 h to complete. For IBD, lassosum was identified as the best-performing tool with optimal settings as follows: a shrinkage value of 0.7 and a lambda value of 0.008859. Applying this optimized PRS to our in-house IBD dataset (validation) resulted in an R^² of 0.203 and an AUC of 0.75. Further, the PRS showed a high positive predictive value of 0.905 but a low negative predictive value of 0.341. This suggests that the PRS is effective in identifying individuals at high risk but might be less reliable in excluding lower risk individuals.</p><p><strong>Conclusions: </strong>Overall, STREAM-PRS provides an efficient framework for selecting the best PRS calculation strategy and helps bridge the portability gap within the PRS field. STREAM-PRS is available at https://github.com/SaraBecelaere/STREAM-PRS.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"119"},"PeriodicalIF":10.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145258067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram.","authors":"Ewa Sieliwonczyk, Arunashis Sau, Konstantinos Patlatzoglou, Kathryn A McGurk, Libor Pastika, Prisca K Thami, Massimo Mangino, Sean L Zheng, George Powell, Lara Curran, Rachel J Buchan, Pantazis Theotokis, Nicholas S Peters, Bart Loeys, Daniel B Kramer, Jonathan W Waks, Fu Siong Ng, James S Ware","doi":"10.1186/s13073-025-01510-z","DOIUrl":"10.1186/s13073-025-01510-z","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Electrocardiograms (ECGs) are widely used to assess cardiac health, but traditional clinical interpretation relies on a limited set of human-defined parameters. While advanced data-driven methods can outperform analyses of conventional ECG features for some tasks, they often lack interpretability. Variational autoencoders (VAEs), a form of unsupervised machine learning, can address this limitation by extracting ECG features that are both comprehensive and interpretable, known as latent factors. These latent factors provide a low-dimensional representation optimised to capture the full informational content of the ECG. The aim of this study was to develop a deep learning model to learn these latent ECG features, and to use this optimised feature set in genetic analyses to identify fundamental determinants of cardiac electrical function. This approach has the potential to expand our understanding of cardiac electrophysiology by uncovering novel phenotypic and genetic relationships.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Our novel VAE model was trained on a dataset comprising over one million secondary care median beat ECGs, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Loci were considered novel if they were not previously associated with ECG traits in the GWAS Catalog and showed no known associations in nearby genes based on literature review. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes, and echocardiographic traits.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The VAE identified 20 independent latent factors that captured ECG morphology with high accuracy (mean Pearson correlation: 0.95). GWAS of latent factors identified 65 unique loci, including 27 novel regions not associated with conventional ECG parameters in the same dataset. Six novel loci were not associated with the ECG in previous larger GWAS studies, including genes implicated in cardiac function and remodelling. Rare variant analysis identified seven additional genes with links to cardiac electrophysiology and remodelling. Phenotypic analyses revealed stronger and more comprehensive associations for latent factors compared to conventional traits, particularly for echocardiographic measures and cardiac phenotypes. Visualisations of latent factor alterations highlighted the interpretability of this approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our study shows that the VAE provides a valuable tool for advancing our understanding of cardiac function and its genetic underpinnings, outperforming traditional approaches in genetic and phenotypic discovery.","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"118"},"PeriodicalIF":10.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145258141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ten mouse organs proteome and metabolome atlas from adult to aging.","authors":"Qingwen Wang, Zhixiao Xu, Xinwen Ding, Aiting Wang, Sunfengda Song, Shuang Zhang, Youming Chen, Yi Ding, Lai Jiang, Xianting Ding","doi":"10.1186/s13073-025-01535-4","DOIUrl":"10.1186/s13073-025-01535-4","url":null,"abstract":"<p><strong>Background: </strong>Aging is a complex biological process characterized by progressive molecular alterations across multiple organ systems, significantly influencing disease susceptibility and mortality. Unraveling molecular interactions driving aging is crucial for interventions promoting healthy aging and mitigating senescence. However, the systemic mechanisms governing both inter-organ interactions and organ-specific aging trajectories remain incompletely characterized.</p><p><strong>Methods: </strong>To investigate the molecular dynamics of aging, we conducted a systematic multi-omics analysis of 400 tissue samples collected from 10 organs (brain, heart, intestine, kidney, liver, lung, muscle, skin, spleen, and stomach) in mice at four distinct life stages: 4, 8, 12, and 20 months (from youth to elderly). Proteomic profiling was performed using data-independent acquisition (DIA) technology, while metabolomic analysis was performed in both positive and negative ion modes. Differential expression analysis of proteins and metabolites was employed to construct a comprehensive multi-organ aging dataset.</p><p><strong>Results: </strong>Proteomic profiling across ten organs at four age stages identified a total of 14,763 protein groups (PGs). Of these, 18 proteins, including Ighm, C4b, and Hpx, exhibited consistent age-related differential expression patterns across all ten organs. Functional enrichment analysis highlighted the humoral immune response as a primary driver of age-related expression changes. Additionally, this study mapped a set of age-unique proteins, such as Hp, Egf, and Arg, with distinct expression patterns in aging organs. Metabolic analysis identified 3779 metabolites, with key aging-related metabolites such as NAD+, inosine, xanthine, and hypoxanthine showing significant expression changes across multiple organs. Pathway enrichment analysis revealed consistent alterations in purine metabolism, pyrimidine metabolism, riboflavin metabolism, and nicotinate/nicotinamide metabolism during multi-organ aging.</p><p><strong>Conclusions: </strong>This study provides a multi-omics atlas of multi-organ aging, revealing both intra- and inter-organ similarities and heterogeneities. These findings offer valuable insights into the molecular mechanisms underlying geriatric health decline and serve as a foundational resource for organism-systematic early warning and targeted interventions against aging-associated pathologies.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"116"},"PeriodicalIF":10.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.","authors":"Xuehan Lu, Xiao Tan, Eun Ju Kim, Xinnan Jin, Meg L Donovan, Jazmina L Gonzalez Cruz, Zherui Xiong, Maria Reyes Becerra de Los Reyes Becerra Perez, Jialei Gong, James Monkman, Divya Agrawal, Arutha Kulasinghe, Quan Nguyen, Zewen Kelvin Tuong","doi":"10.1186/s13073-025-01518-5","DOIUrl":"10.1186/s13073-025-01518-5","url":null,"abstract":"<p><p>The tumor microenvironment (TME) is composed of a diverse and dynamic spectrum of cell types, cellular activities, and cell-cell interactions (CCI). Understanding the complex CCI within the TME is critical for advancing cancer treatment strategies, including modulating or predicting drug responses. Recent advances in omics technologies, including spatial transcriptomics and proteomics, have allowed improved mapping of CCI within the TME. The integration of omics insights from different platforms may facilitate the identification of novel biomarkers and therapeutic targets. This review discusses the latest computational methods for inferring CCIs from different omics data and various CCI and drug databases, emphasizing their applications in predicting drug responses. We also comprehensively summarize recent patents, clinical trials, and publications that leverage these cellular interactions to refine cancer treatment approaches. We believe that the integration of these CCI-focused technologies can improve personalized therapy for cancer patients, thereby optimizing treatment outcomes and paving the way for next-generation precision oncology.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"117"},"PeriodicalIF":10.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12506067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathologist-initiated whole genome and transcriptome sequencing demonstrates diagnostic utility in resolving difficult-to-diagnose tumors.","authors":"Joseph H A Vissers, Catherine Mitchell, Owen W J Prall, Wing-Yee Lo, Sehrish Kanwal, Stephen J Luen, Stephen C Watts, Christopher M Angel, Christine Khoo, Jia-Min B Pang, William K Murray, Cameron Snell, Michael Christie, Richard J Rebello, Richard W Tothill, Kym Pham, Oliver Hofmann, Stephen B Fox, Sean M Grimmond","doi":"10.1186/s13073-025-01534-5","DOIUrl":"10.1186/s13073-025-01534-5","url":null,"abstract":"<p><strong>Background: </strong>Despite significant advances in diagnostic cancer histopathology, a subset of tumors are unable to be classified using WHO criteria. The resulting diagnostic uncertainty can result in inappropriate clinical management and negative patient outcomes.</p><p><strong>Methods: </strong>We investigated whether combining histopathology with whole genome and transcriptome sequencing (WGTS) could improve the classification of tumors that posed diagnostic dilemmas despite extensive histopathology and standard molecular work-up at a quaternary oncology center.</p><p><strong>Results: </strong>We successfully sequenced 45 tumors from an initial set of 54 unclassified tumors (83% success rate). A confident diagnosis was made for 35/45 tumors (78%). Additionally, potential treatment targets were identified in 21/45 tumors (47%). Theoretical comparison with alternative assays demonstrated that WGTS was uniquely capable of detecting critical diagnostic findings in 9/35 tumors (26%).</p><p><strong>Conclusions: </strong>This work supports augmenting histopathology and standard molecular pathology with WGTS in the classification of difficult-to-diagnose tumors.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"107"},"PeriodicalIF":10.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to: 'Predicted loss-of-function variants before Met584 in ARID1B in population cohorts likely reflect reduced penetrance and should be reported diagnostically'.","authors":"Caroline F Wright, Robin N Beaumont","doi":"10.1186/s13073-025-01544-3","DOIUrl":"10.1186/s13073-025-01544-3","url":null,"abstract":"","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"114"},"PeriodicalIF":10.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic underpinnings of the heterogeneous impact of obesity on lipid levels and cardiovascular disease.","authors":"Daeeun Kim, Heather M Highland, Roelof A J Smit, Micah R Hysong, Victoria L Buchanan, Kristin L Young, Chi Zhao, Cassandra N Spracklen, Tuomas O Kilpeläinen, Boya Guo, Burcu F Darst, Yanwei Cai, Zhe Wang, Jessica Lundin, Sonja I Berndt, JoAnn E Manson, Eirini Marouli, Leslie Lange, Ethan Lange, Myriam Fornage, Christopher R Gignoux, Christopher A Haiman, Stephen S Rich, Steven Buyske, Ruth J F Loos, Charles Kooperberg, Ulrike Peters, Christy L Avery, Penny Gordon-Larsen, Mariaelisa Graff, Laura M Raffield, Kari E North","doi":"10.1186/s13073-025-01522-9","DOIUrl":"10.1186/s13073-025-01522-9","url":null,"abstract":"<p><strong>Background: </strong>Obesity is thought to increase cardiovascular disease (CVD) risk partly through dyslipidemia. Yet, obesity's effects on dyslipidemia are not uniform. Understanding the shared genetic basis between obesity and lipid traits can provide insight into this heterogeneity and its implications for CVD risk.</p><p><strong>Methods: </strong>We examined local genetic correlations between three lipid measures [high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides (TG)] and body mass index (BMI) using genome-wide association study summary statistics from European ancestry UK Biobank participants. We identified genomic loci with opposing genetic effects on obesity and dyslipidemia risk (protective BMI-lipid loci) and those with concordant directions for both obesity and dyslipidemia risk (adverse BMI-lipid loci). Gene-based association analyses were used to prioritize potential causal genes. We then constructed polygenic risk scores for BMI (PRS_BMI) based on protective and adverse loci and assessed their associations with BMI, lipid levels, CVD, and related traits in the diverse Population Architecture using Genomics and Epidemiology (PAGE) study. PheWAS was performed in the All of Us cohort. Mendelian randomization (MR) was conducted to assess the causal impact of protective/adverse loci on cardiometabolic outcomes. Finally, we investigated the associations with fat distribution traits using MRI-based fat measures in the UK Biobank.</p><p><strong>Results: </strong>Among 2495 regions, we identified 789 HDL, 26 LDL, and 494 TG loci with significant local genetic correlation with BMI (including overlapping loci). Of these, 3 HDL, 10 LDL, and 8 TG loci showed protective correlations. Gene-based analyses prioritized 18 candidate causal genes. The protective PRS_BMI(+)HDL(+) was associated with higher BMI but favorable lipid profiles and reduced CVD risk in PAGE. PheWAS revealed protective associations with hyperlipidemia, atrial fibrillation, and Alzheimer's disease. MR supported the favorable causal effects of these protective loci on several cardiometabolic outcomes. Notably, protective PRS_BMI(+)TG(-) was uniquely associated with decreased visceral-to-abdominal subcutaneous adipose tissue ratio.</p><p><strong>Conclusions: </strong>Identifying and validating genomic loci with shared genetic signals between BMI and lipid levels further supports the importance of genetics in defining the heterogeneous impact of obesity on dyslipidemia and CVD.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"113"},"PeriodicalIF":10.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicted loss-of-function variants before Met584 in ARID1B in population cohorts likely reflect reduced penetrance and should be reported diagnostically.","authors":"Pleuntje J van der Sluijs, Gijs W E Santen","doi":"10.1186/s13073-025-01545-2","DOIUrl":"10.1186/s13073-025-01545-2","url":null,"abstract":"","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"111"},"PeriodicalIF":10.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MicroRNA gene dynamics in immune cell subpopulations during aging and atherosclerosis disease development at single-cell resolution.","authors":"Ana Hernández de Sande, Tanja Turunen, Maria Bouvy-Liivrand, Tiit Örd, Senthil Palani, Mari Lahnalampi, Celia Tundidor-Centeno, Heidi Liljenbäck, Jenni Virta, Henri Niskanen, Buddika Jayasingha, Olli-Pekka Smålander, Lasse Sinkkonen, Lea Mikkola, Thomas Sauter, Anne Roivainen, Tapio Lönnberg, Minna U Kaikkonen, Merja Heinäniemi","doi":"10.1186/s13073-025-01530-9","DOIUrl":"10.1186/s13073-025-01530-9","url":null,"abstract":"<p><strong>Background: </strong>Regulatory networks controlling aging and disease trajectories remain incompletely understood. MicroRNAs (miRNAs) are a class of regulatory non-coding RNAs that contribute to the regulation of tissue homeostasis by modulating the stability and abundance of their target mRNAs. MiRNA genes are transcribed similarly to protein-coding genes which has facilitated their annotation and quantification from bulk transcriptomes. Here, we show that droplet, spatial, and plate-based single-cell RNA-sequencing platforms can be used to decipher miRNA gene signatures at cellular resolution to reveal their expression dynamics in vivo.</p><p><strong>Methods: </strong>We first benchmarked the approach examining concordance between platforms, species, and cell type-specific bulk expression data. To discover changes in miRNA gene expression that could contribute to the progressive loss of cellular homeostasis during aging and disease development, we annotated the comprehensive aging mouse dataset, Tabula Muris Senis, with cell type-specific miRNA expression and acquired transcriptome and translatome profiles from an atherosclerosis disease model.</p><p><strong>Results: </strong>We generated an openly available workflow and aging-profile resource to characterize miRNA expression from single-cell genomics studies. Comparing immune cells in spleen tissue between young and old mice revealed concordance with previous functional studies, highlighting the upregulation of mmu-mir-146a, mmu-mir-101a, and mmu-mir-30 family genes involved in senescence and inflammatory pathways. Atherosclerosis progression is reflected within adipose tissue as expansion of the myeloid compartment, with elevated pro-inflammatory mmu-mir-511 expression in several macrophage subtypes. Upregulation of the immunosuppressive mmu-mir-23b ~ mir-24-2 ~ mir-27b locus was specific to Trem2 + lipid-associated macrophages, prevalent at late disease. Accordingly, ribosome-associated RNA profiling from myeloid cells in vivo validated significant mmu-mir-23b target gene enrichment in disease-regulated translatomes. Prominent tissue infiltration of monocytes led to upregulated mmu-mir-1938 and mmu-mir-22 expression and in classical monocytes activated mmu-mir-221 ~ 222, mmu-mir-511, and mmu-mir-155 gene loci, confirmed by bulk nascent transcriptomics data from ex vivo macrophage cultures. Overall, the monocyte-associated changes in miRNA expression represented the most significant target gene associations in the disease-trajectory translatome profiles.</p><p><strong>Conclusions: </strong>We demonstrate that miRNA gene transcriptional activity is widely impacted in immune cells by aging and during disease development and further identify the corresponding translatome signature of inflamed adipose tissue.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"112"},"PeriodicalIF":10.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome sequencing for the diagnosis of intellectual disability as a paradigm for rare diseases in the French healthcare setting: the prospective DEFIDIAG study.","authors":"Salima El Chehadeh, Solveig Heide, Chloé Quélin, Marlène Rio, Henri Margot, David Geneviève, Bertrand Isidor, Alice Goldenberg, Caroline Guégan, Gaëtan Lesca, Marjolaine Willems, Clothilde Ormières, Roseline Caumes, Tiffany Busa, Dominique Bonneau, Anne-Marie Guerrot, Isabelle Marey, Gabriella Vera, Pauline Marzin, Anaïs Philippe, Aurore Garde, Christine Coubes, Marie Vincent, Vincent Michaud, Cyril Mignot, Perrine Charles, Sabine Sigaudy, Patrick Edery, Didier Lacombe, Anne Boland, Frédérique Nowak, Marion Bouctot, Marie-Laure Humbert-Asensio, Alban Simon, Kirsley Chennen, Niki Sabour, Christelle Delmas, Gaël Nicolas, Pascale Saugier-Veber, François Lecoquierre, Kévin Cassinari, Boris Keren, Thomas Courtin, Jean-Madeleine De Sainte Agathe, Valérie Malan, Giulia Barcia, Frédéric Tran Mau-Them, Hana Safraou, Christophe Philippe, Julien Thévenon, Nicolas Chatron, Louis Januel, Amélie Piton, Virginie Haushalter, Bénédicte Gérard, Catherine Lejeune, Laurence Faivre, Damien Sanlaville, Delphine Héron, Sylvie Odent, Patrick Nitschké, Caroline Schluth-Bolard, Stanislas Lyonnet, Jean-François Deleuze, Christine Binquet, Hélène Dollfus","doi":"10.1186/s13073-025-01527-4","DOIUrl":"10.1186/s13073-025-01527-4","url":null,"abstract":"<p><strong>Background: </strong>Intellectual disability (ID) is the leading cause of patient referral to medical genetic departments in French academic hospitals. Whole genome sequencing (WGS) as a first diagnostic approach is expected to achieve a higher diagnostic yield than the French national reference strategies (RefStrategy) (fragile X expansion testing, chromosomal microarray analysis, and 44 ID genes panel), given its broad and more homogeneous coverage, its ability to identify copy number, structural and intergenic/deep intronic events.</p><p><strong>Methods: </strong>DEFIDIAG is a national, prospective pilot investigation, carried out in the framework of the French initiative for genomic medicine (Plan France Médecine Génomique 2025), aimed at comparing the diagnostic yield of WGS trio analysis (WGS-trio) (index case, father, mother) with the RefStrategy in real-life conditions of clinical and laboratory workflows. Both strategies were applied in a blinded fashion in 1239 ID probands (50% were already-tested, 50% were never-tested) with no definitive genetic diagnosis. Among them, a subgroup of 187 patients were randomized to undergo WGS-solo (proband only) in addition to WGS-trio and RefStrategy.</p><p><strong>Results: </strong>Four hundred forty two likely pathogenic/pathogenic single-nucleotide variants were identified (for 231 genes) as well as 171 variants of uncertain significance warranting clinical or functional reassessment for a potential reclassification (VUS +) (for 142 genes), 79 likely pathogenic/pathogenic copy number variants and 10 likely pathogenic/pathogenic structural variants. The diagnostic yield for likely pathogenic/pathogenic variants increased from 17.3% with the RefStrategy to 41.9% with WGS-trio in the never-tested patient cohort. An increase of 13.9% was observed in all categories by adding the VUS + , thus raising the yield to 56% for WGS-trio. Overall, WGS-solo enabled the identification of likely pathogenic/pathogenic variants in 29.9% of cases (increasing to 41.1% when including VUS +) compared to 21.9% with the RefStrategy. In addition, following recent reports of de novo variants in the non-coding spliceosomal RNU4-2 gene as a common cause of ID, this gene was subsequently analyzed, leading to the identification of pathogenic de novo variants in 7 patients.</p><p><strong>Conclusions: </strong>As a first line test for ID diagnosis, WGS (including for solo situations) proved to be more effective than the reference strategy, in the context of real-life hospital settings in France.</p><p><strong>Trial registration: </strong>Prospectively registered with ClinicalTrials.gov under the identifier NCT04154891 (07/11/2019).</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"110"},"PeriodicalIF":10.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}