Nature genetics最新文献

{"title":"Rapid epigenomic classification of acute leukemia.","authors":"Til L Steinicke,Salvatore Benfatto,Maria R Capilla-Guerra,Andre B Monteleone,Jonathan H Young,Subha Shankar,Phillip D Michaels,Harrison K Tsai,Jonathan D Good,Antonia Kreso,Peter van Galen,Christoph Schliemann,Evan C Chen,Gabriel K Griffin,Volker Hovestadt","doi":"10.1038/s41588-025-02321-z","DOIUrl":"https://doi.org/10.1038/s41588-025-02321-z","url":null,"abstract":"Acute leukemia requires precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time-intensive and do not capture the full spectrum of acute leukemia heterogeneity. Here, we developed a framework to classify acute leukemia using genome-wide DNA methylation profiling. We first assembled a comprehensive reference cohort (n = 2,540 samples) and defined 38 methylation classes. Methylation-based classification matched standard-pathology lineage classification in most cases and revealed heterogeneity in addition to that captured by genetic categories. Using this reference, we developed a neural network (MARLIN; methylation- and AI-guided rapid leukemia subtype inference) for acute leukemia classification from sparse DNA methylation profiles. In retrospective cohorts profiled by nanopore sequencing, high-confidence predictions were concordant with conventional diagnoses in 25 out of 26 cases. Real-time MARLIN classification in patients with suspected acute leukemia provided accurate predictions in five out of five cases, which were typically generated within 2 h of sample receipt. In summary, we present a framework for rapid acute leukemia classification that complements and enhances standard-of-care diagnostics.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"61 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects.","authors":"Konrad J Karczewski,Rahul Gupta,Masahiro Kanai,Wenhan Lu,Kristin Tsuo,Ying Wang,Raymond K Walters,Patrick Turley,Shawneequa Callier,Nirav N Shah,Nikolas Baya,Duncan S Palmer,Jacqueline I Goldstein,Gopal Sarma,Matthew Solomonson,Nathan Cheng,Sam Bryant,Claire Churchhouse,Caroline M Cusick,Timothy Poterba,John Compitello,Daniel King,Wei Zhou,Cotton Seed,Hilary K Finucane,Mark J Daly,Benjamin M Neale,Elizabeth G Atkinson,Alicia R Martin","doi":"10.1038/s41588-025-02335-7","DOIUrl":"https://doi.org/10.1038/s41588-025-02335-7","url":null,"abstract":"Large biobanks, such as the UK Biobank (UKB), enable massive phenome by genome-wide association studies that elucidate genetic etiology of complex traits. However, people from diverse genetic ancestry groups are often excluded from association analyses due to concerns about population structure introducing false positive associations. Here we generate mixed model associations and meta-analyses across genetic ancestry groups, inclusive of a larger fraction of the UK Biobank than previous efforts, to produce freely available summary statistics for 7,266 traits. We build a quality control and analysis framework informed by genetic architecture. Overall, we identify 14,676 significant loci (P < 5 × 10-8) in the meta-analysis that were not found in the EUR genetic ancestry group alone, including new associations, for example between CAMK2D and triglycerides. We also highlight associations from ancestry-enriched variation, including a known pleiotropic missense variant in G6PD associated with several biomarker traits. We release these results publicly alongside frequently asked questions that describe caveats for interpretation of results, enhancing available resources for interpretation of risk variants across diverse populations.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"16 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic and epigenetic screens in primary human T cells link candidate causal autoimmune variants to T cell networks.","authors":"Ching-Huang Ho,Maxwell A Dippel,Meghan S McQuade,LeAnn P Nguyen,Arpit Mishra,Stephan Pribitzer,Samantha Hardy,Harshpreet Chandok,Florence M Chardon,Troy A McDiarmid,Hannah A DeBerg,Jane H Buckner,Jay Shendure,Carl G de Boer,Michael H Guo,Ryan Tewhey,John P Ray","doi":"10.1038/s41588-025-02301-3","DOIUrl":"https://doi.org/10.1038/s41588-025-02301-3","url":null,"abstract":"Genetic variants associated with autoimmune diseases are highly enriched within putative cis-regulatory regions of CD4+ T cells, suggesting that they could alter disease risk through changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. Here we tested >18,000 autoimmune disease-associated variants for allele-specific effects on expression using massively parallel reporter assays in primary human CD4+ T cells. We find 545 variants that modulate expression in an allele-specific manner (emVars). Primary T cell emVars greatly enrich for likely causal variants, are mediated by common upstream pathways and their putative target genes are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis-regulatory elements modulate both known and previously unappreciated target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"1 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide association meta-analysis of childhood ADHD symptoms and diagnosis identifies new loci and potential effector genes.","authors":"Camiel M van der Laan,Hill F Ip,Marijn Schipper,Jouke-Jan Hottenga,Beate St Pourcain,Tetyana Zayats,René Pool,Eva M L Krapohl,Isabell Brikell,María Soler Artigas,Judit Cabana-Domínguez,Ilja M Nolte,Koen Bolhuis,Teemu Palviainen,Hadi Zafarmand,Scott Gordon,Fazil Aliev,S Alexandra Burt,Carol A Wang,Gretchen Saunders,Ville Karhunen,Daniel E Adkins,Richard Border,Roseann E Peterson,Joseph A Prinz,Elisabeth Thiering,Natàlia Vilor-Tejedor,Tarunveer S Ahluwalia,Andrea Allegrini,Kaili Rimfeld,Qi Chen,Yi Lu,Joanna Martin,Rosa Bosch,Josep Antoni Ramos-Quiroga,Alexander Neumann,Judith Ensink,Katrina L Grasby,José J Morosoli,Xiaoran Tong,Shelby Marrington,James G Scott,Andrey A Shabalin,Robin Corley,Luke M Evans,Karen Sugden,Silvia Alemany,Lærke Sass,Rebecca Vinding,Erik A Ehli,Fiona A Hagenbeek,Eske M Derks,Henrik Larsson,Harold Snieder,Charlotte Cecil,Alyce M Whipp,Tellervo Korhonen,Eero Vuoksimaa,Richard J Rose,André G Uitterlinden,Jan Haavik,Jennifer R Harris,Øyvind Helgeland,Stefan Johansson,Gun Peggy S Knudsen,Pal Rasmus Njolstad,Qing Lu,Alina Rodriguez,Anjali K Henders,Abdullah Mamun,Jackob M Najman,Sandy Brown,Christian Hopfer,Kenneth Krauter,Chandra A Reynolds,Andrew Smolen,Michael Stallings,Sally Wadsworth,Tamara L Wall,Lindon Eaves,Judy L Silberg,Allison Miller,Alexandra Havdahl,Sabrina Llop,Maria-Jose Lopez-Espinosa,Klaus Bønnelykke,Jordi Sunyer,Louise Arseneault,Marie Standl,Joachim Heinrich,Joseph Boden,John Pearson,John Horwood,Martin Kennedy,Richie Poulton,Hermine H Maes,John Hewitt,William E Copeland,Christel M Middeldorp,Gail M Williams,Naomi Wray,Marjo-Riitta Järvelin,Matt McGue,William Iacono,Avshalom Caspi,Terrie E Moffitt,Andrew J O Whitehouse,Craig E Pennell,Kelly L Klump,Chang Jiang,Danielle M Dick,Ted Reichborn-Kjennerud,Nicholas G Martin,Sarah E Medland,Tanja Vrijkotte,Jaakko Kaprio,Henning Tiemeier,George Davey Smith,Catharina A Hartman,Albertine J Oldehinkel,Miquel Casas,Marta Ribasés,Paul Lichtenstein,Sebastian Lundström,Robert Plomin,Meike Bartels,Michel G Nivard,Dorret I Boomsma,Natalia Llonga","doi":"10.1038/s41588-025-02295-y","DOIUrl":"https://doi.org/10.1038/s41588-025-02295-y","url":null,"abstract":"We performed a genome-wide association meta-analysis (GWAMA) of 290,134 attention-deficit/hyperactivity disorder (ADHD) symptom measures of 70,953 unique individuals from multiple raters, ages and instruments (ADHDSYMP). Next, we meta-analyzed the results with a study of ADHD diagnosis (ADHDOVERALL). ADHDSYMP returned no genome-wide significant variants. We show that the combined ADHDOVERALL GWAMA identified 39 independent loci, of which 17 were new. Using a recently developed gene-mapping method, Fine-mapped Locus Assessment Model of Effector genes, we identified 22 potential ADHD effector genes implicating several new biological processes and pathways. Moderate negative genetic correlations (rg < -0.40) were observed with multiple cognitive traits. In three cohorts, polygenic scores (PGSs) based on ADHDOVERALL outperformed PGSs based on ADHD symptoms and diagnosis alone. Our findings support the notion that clinical ADHD is at the extreme end of a continuous liability that is indexed by ADHD symptoms. We show that including ADHD symptom counts helps to identify new genes implicated in ADHD.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"1 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated Bayesian inference of population size history from recombining sequence data","authors":"Jonathan Terhorst","doi":"10.1038/s41588-025-02323-x","DOIUrl":"https://doi.org/10.1038/s41588-025-02323-x","url":null,"abstract":"<p>This study introduces population history learning by averaging sampled histories (PHLASH), a new method for inferring population history from whole-genome sequence data. It works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a pairwise sequentially Markovian coalescent-like model and averaging them together to form an accurate and adaptive estimator. On simulated data, PHLASH tends to be faster and have lower error than several competing methods, including SMC++, MSMC2 and FITCOAL. Moreover, it provides automatic uncertainty quantification and leads to new Bayesian testing procedures for detecting population structure and ancient bottlenecks. The key technical advance is a new algorithm for computing the score function (gradient of the log likelihood) of a coalescent hidden Markov model, which has the same computational cost as evaluating the log likelihood. PHLASH has been released as an easy-to-use Python software package and leverages graphics processing unit acceleration when available.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"28 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Realizing the full potential of Our Future Health through data linkage and trans-biobank efforts","authors":"Vincent J. Straub, Stefania Benonisdottir, Augustine Kong, Melinda C. Mills","doi":"10.1038/s41588-025-02327-7","DOIUrl":"https://doi.org/10.1038/s41588-025-02327-7","url":null,"abstract":"The scale and population coverage of Our Future Health, alongside other next-generation biobanks, offers unique opportunities to advance genomic medicine. Focusing on the UK context, we provide a researcher’s perspective of how this new resource could reach its full potential in a way that is impactful, user-friendly and informs related global efforts.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"15 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing biological understanding of cellular senescence with computational multiomics","authors":"Sheng Li, Paula A. Agudelo Garcia, Constantin Aliferis, Michael J. Becich, Jazmin Calyeca, Benjamin D. Cosgrove, Jennifer Elisseeff, Negin Farzad, Elana J. Fertig, Carolyn Glass, Liangcai Gu, Qianjiang Hu, Zhicheng Ji, Melanie Königshoff, Nathan K. LeBrasseur, Dongmei Li, Anjun Ma, Qin Ma, Vilas Menon, Jacob T. Mitchell, Ana L. Mora, Sushma Nagaraj, Andrew C. Nelson, Laura J. Niedernhofer, Mauricio Rojas, Hash Brown Taha, Jinhua Wang, Siyuan Wang, Pei-Hsun Wu, Jichun Xie, Ming Xu, Miao Yu, Xu Zhang, Yue Zhao, Peter D. Adams, Cristina Aguayo-Mazzucato, Darren J. Baker, Christopher Benz, David A. Bernlohr, Marta Bueno, Jin Chen, Bennett G. Childs, Jeffrey H. Chuang, Dongjun Chung, Mythili Dileepan, Li Ding, Mingze Dong, Francesca Duncan, Archibald Enninful, William F. Flynn, Ana Catarina Franco, David Furman, Vesna Garovic, Stephanie Halene, Allison B. Herman, Ann V. Hertzel, Kanako Iwasaki, Hyeongseon Jeon, Jeon Woong Kang, Shilpita Karmakar, James L. Kirkland, Ron Korstanje, Erich Kummerfeld, Jun Hee Lee, Yang Liu, Yao Lu, Jose Lugo-Martinez, Helene Martini, Simon Melov, Nicolas Musi, João F. Passos, Samuel T. Peters, Irfan Rahman, Ramalakshmi Ramasamy, Alexandra N. Rindone, Paul D. Robbins, Paul Robson, Jhonny Rodriguez-Lopez, Lorena Rosas, Nadia Rosenthal, Marissa J. Schafer, Birgit Schilling, Elizabeth L. Schmidt, Kevin Schneider, Kaustav Sengupta, Jian Shu, Peter T. C. So, Li Sun, Tamar Tchkonia, Marcos G. Teneche, Natalia Vanegas, Julia Wang, Juan Xie, Shanshan Yin, Ke Zhang, Quan Zhu, Rong Fan","doi":"10.1038/s41588-025-02314-y","DOIUrl":"https://doi.org/10.1038/s41588-025-02314-y","url":null,"abstract":"<p>Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features. This Review provides a comprehensive overview of multiomic approaches for the characterization and biological understanding of cellular senescence. The technical capability and challenges of each approach are discussed, and practical guidelines are provided for selecting tools for identifying, characterizing and spatially mapping senescent cells. The importance of computational analyses in multiomics research, including senescent cell identification, signature detection and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. Finally, future directions and the potential impact of multiomic approaches on the biological understanding of cellular senescence are discussed.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"36 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bivalent chromatin state regulates the function of composite transposons","authors":"Chiara Anania","doi":"10.1038/s41588-025-02337-5","DOIUrl":"10.1038/s41588-025-02337-5","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 9","pages":"2083-2083"},"PeriodicalIF":29.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting obesity from childhood","authors":"Margot Brandt","doi":"10.1038/s41588-025-02338-4","DOIUrl":"10.1038/s41588-025-02338-4","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 9","pages":"2083-2083"},"PeriodicalIF":29.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building immune tolerance in the thymus","authors":"Safia Danovi","doi":"10.1038/s41588-025-02339-3","DOIUrl":"10.1038/s41588-025-02339-3","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 9","pages":"2083-2083"},"PeriodicalIF":29.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}