Nature genetics最新文献

{"title":"Publisher Correction: Analysis of R-loop forming regions identifies RNU2-2 and RNU5B-1 as neurodevelopmental disorder genes","authors":"Adam Jackson, Nishi Thaker, Alexander Blakes, Gillian Rice, Sam Griffiths-Jones, Meena Balasubramanian, Jennifer Campbell, Nora Shannon, Jungmin Choi, Juhyeon Hong, David Hunt, Anna de Burca, Soo Yeon Kim, Taekeun Kim, Seungbok Lee, Melody Redman, Rocio Rius, Cas Simons, Tiong Yang Tan, Jamie Ellingford, Raymond T. O’Keefe, Jong Hee Chae, Siddharth Banka","doi":"10.1038/s41588-025-02274-3","DOIUrl":"10.1038/s41588-025-02274-3","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1792-1792"},"PeriodicalIF":31.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-025-02274-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144371173","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":"Predicting resistance to chemotherapy using chromosomal instability signatures","authors":"Joe Sneath Thompson, Laura Madrid, Barbara Hernando, Carolin M. Sauer, Maria Vias, Maria Escobar-Rey, Wing-Kit Leung, Diego Garcia-Lopez, Jamie Huckstep, Magdalena Sekowska, Karen Hosking, Mercedes Jimenez-Linan, Marika A. V. Reinius, Abhipsa Roy, Omar Abdulle, Justina Pangonyte, Harry Dobson, Amy E. Cullen, Dilrini De Silva, David Gómez-Sánchez, Marina Torres, Ángel Fernández-Sanromán, Deborah Sanders, Filipe Correia Martins, Ionut-Gabriel Funingana, Giovanni Codacci-Pisanelli, Miguel Quintela-Fandino, Florian Markowetz, Jason Yip, James D. Brenton, Anna M. Piskorz, Geoff Macintyre","doi":"10.1038/s41588-025-02233-y","DOIUrl":"10.1038/s41588-025-02233-y","url":null,"abstract":"Chemotherapies are often given without precision biomarkers, exposing patients to toxic side effects without guaranteed benefit. Here we present chromosomal instability signature biomarkers that identify resistance to platinum-, taxane- and anthracycline-based treatments using a single genomic test. In retrospectively emulated randomized-control biomarker clinical trials using real-world cohorts (n = 840), predicted resistant patients had elevated treatment failure risk for taxane (hazard ratio (HR) of 7.44) and anthracycline (HR of 1.88) in ovarian, taxane (HR of 3.98) and anthracycline (HR of 3.69) in metastatic breast and taxane (HR of 5.46) in metastatic prostate. Nonrandomized emulations showed predictive capacity for platinum resistance in ovarian (HR of 1.46) and anthracycline in sarcoma (HR of 3.59). We demonstrate feasibility using whole-genome sequencing, capture-panel sequencing and cell-free DNA. Our findings highlight the clinical value of chromosomal instability signatures in predicting resistance to chemotherapies across multiple cancer types, with the potential to transform the one-size-fits-all chemotherapy approach into precise, tailored treatment. Here the authors show that chromosomal instability signatures can predict resistance to anthracycline-, taxane- and platinum-based chemotherapeutics in breast, ovarian and prostate cancer and sarcoma. Validation is performed through emulation of phase 2 and 3 clinical trials using real-world data.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1708-1717"},"PeriodicalIF":31.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-025-02233-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340981","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":"Using large-scale population-based data to improve disease risk assessment of clinical variants","authors":"Iain S. Forrest, Kuan-Lin Huang, Julie M. Eggington, Wendy K. Chung, Daniel M. Jordan, Ron Do","doi":"10.1038/s41588-025-02212-3","DOIUrl":"10.1038/s41588-025-02212-3","url":null,"abstract":"Understanding the disease risk of genetic variants is fundamental to precision medicine. Estimates of penetrance—the probability of disease for individuals with a variant allele—rely on disease-specific cohorts, clinical testing and emerging electronic health record (EHR)-linked biobanks. These data sources, while valuable, each have limitations in quality, representativeness and analyzability. Here, we provide a historical account of the currently accepted pathogenicity classification system and data available in ClinVar, a public archive that aggregates variant interpretations but lacks detailed data for accurate penetrance assessment, highlighting its oversimplification of disease risk. We propose an integrative Bayesian framework that unifies pathogenicity and penetrance, leveraging both functional and real-world evidence to refine risk predictions. In addition, we advocate for enhancing ClinVar with the inclusion of high-priority phenotypes, age-stratified data and population-based cohorts linked to EHRs. We suggest developing a community repository of population-based penetrance estimates to support the clinical application of genetic data. This Perspective proposes a new framework that integrates large-scale population data with clinical evidence to improve disease risk assessment of genetic variants, enhancing the accuracy and value of genetic information in precision medicine.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1588-1597"},"PeriodicalIF":31.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340982","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":"Incorporating genetic data improves target trial emulations and informs the use of polygenic scores in randomized controlled trial design","authors":"Jakob German, Zhiyu Yang, Sarah Urbut, Pekka Vartiainen, FinnGen, Pradeep Natarajan, Elisabetta Patorno, Zoltan Kutalik, Anthony Philippakis, Andrea Ganna","doi":"10.1038/s41588-025-02229-8","DOIUrl":"10.1038/s41588-025-02229-8","url":null,"abstract":"Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, yet ethical, practical and financial constraints often necessitate reliance on observational data and trial emulations. This study explores how integrating genetic data can enhance both emulated and traditional trial designs. Using FinnGen (n = 425,483), we emulated four major cardiometabolic RCTs and showed how reduced differences in polygenic scores (PGS) between trial arms track improvement in study design. Simulation studies reveal that PGS alone cannot fully adjust for unmeasured confounding. Instead, Mendelian randomization analyses can be used to detect likely confounders. Finally, trial emulations provide a platform to assess and refine PGS implementation for genetic enrichment strategies. By comparing associations of PGS with trial outcomes in the general population and emulated trial cohorts, we highlight the need to validate prognostic enrichment approaches in trial-relevant populations. These results highlight the growing potential of incorporating genetic information to optimize clinical trial design. This study integrates polygenic risk scores with four emulated clinical trials using FinnGen data and shows the feasibility of this approach while highlighting potential pitfalls.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1620-1627"},"PeriodicalIF":31.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-025-02229-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311730","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":"Transferability of European-derived Alzheimer’s disease polygenic risk scores across multiancestry populations","authors":"Aude Nicolas, Richard Sherva, Benjamin Grenier-Boley, Yoontae Kim, Masataka Kikuchi, Jigyasha Timsina, Itziar de Rojas, María Carolina Dalmasso, Xiaopu Zhou, Yann Le Guen, Carlos E. Arboleda-Bustos, Maria Aparecida Camargos Bicalho, Maëlenn Guerchet, Sven van der Lee, Monica Goss, Atahualpa Castillo, Céline Bellenguez, Fahri Küçükali, Claudia L. Satizabal, Bernard Fongang, Qiong Yang, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Han Cao, Nancy Y. Ip, Amy K. Y. Fu, Fanny C. F. Ip, Natividad Olivar, Carolina Muchnik, Carolina Cuesta, Lorenzo Campanelli, Patricia Solis, Daniel Gustavo Politis, Silvia Kochen, Luis Ignacio Brusco, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Jose Luís Royo, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Katrine Laura Rasmussen, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John C. van Swieten, Harro Seelaar, Jurgen A. H. R. Claassen, Willemijn J. Jansen, Inez Ramakers, Frans Verhey, Aad van der Lugt, Philip Scheltens, Jenny Ortega-Rojas, Ana Gabriela Concha Mera, Maria F. Mahecha, Rodrigo Pardo, Gonzalo Arboleda, Shahram Bahrami, Vera Fominykh, Geir Selbæk, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Anne Boland, Jean-François Deleuze, Luiz Armando de Marco, Edgar Nunes de Moraes, Bernardo de Mattos Viana, Marco Túlio Gualberto Cintra, Teresa Juarez-Cedillo, Anthony J. Griswold, Tatiana Forund, Jonathan Haines, Lindsay Farrer, Anita DeStefano, Ellen Wijsman, Richard Mayeux, Margaret Pericak-Vance, Brian Kunkle, Alison Goate, Gerard D. Schellenberg, Badri Vardarajan, Li-San Wang, Yuk Yee Leung, Clifton L. Dalgard, Gael Nicolas, David Wallon, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Bárbara Angel, Sergio Gloger, Maria Victoria Chacon, Rafael Aranguiz, Paulina Orellana, Andrea Slachevsky, Christian Gonzalez-Billault, Cecilia Albala, Patricio Fuentes, Perminder Sachdev, Karen A. Mather, Richard L. Hauger, Victoria Merritt, Matthew Panizzon, Rui Zhang, J. Michael Gaziano, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Benedetta Nacmias, Paolo Caffarra, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, EADB, Hampton L. Leonard, Jenifer S. Yokoyama, Mike A. Nalls, Akinori Miyashita, Norikazu Hara, Kouichi Ozaki, Shumpei Niida, Julie Williams, Carlo Masullo, Philippe Amouyel, Pierre-Marie Preux, Pascal Mbelesso, Bébène Bandzouzi, Andy Saykin, Frank Jessen, Patrick G. Kehoe, Cornelia Van Duijn, Nesrine Ben Salem, Ruth Frikke-Schmidt, Lotfi Cherni, Michael D. Greicius, Magda Tsolaki, Pascual Sánchez-Juan, Marco Aurélio Romano Silva, Tenielle Porter, Simon M. Laws, Kristel Sleegers, Martin Ingelsson, Jean-François Dartigues, Sudha Seshadri, Giacomina Rossi, Laura Morelli, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Humberto Arboleda, Carlos Cruchaga, Valentina Escott-Price, Agustín Ruiz, Kun Ho Lee, Takeshi Ikeuchi, Alfredo Ramirez, Jungsoo Gim, Mark Logue, Jean-Charles Lambert","doi":"10.1038/s41588-025-02227-w","DOIUrl":"10.1038/s41588-025-02227-w","url":null,"abstract":"A polygenic score (PGS) for Alzheimer’s disease (AD) was derived recently from data on genome-wide significant loci in European ancestry populations. We applied this PGS to populations in 17 European countries and observed a consistent association with the AD risk, age at onset and cerebrospinal fluid levels of AD biomarkers, independently of apolipoprotein E locus (APOE). This PGS was also associated with the AD risk in many other populations of diverse ancestries. A cross-ancestry polygenic risk score improved the association with the AD risk in most of the multiancestry populations tested when the APOE region was included. Finally, we found that the PGS/polygenic risk score captured AD-specific information because the association weakened as the diagnosis was broadened. In conclusion, a simple PGS captures the AD-specific genetic information that is common to populations of different ancestries, although studies of more diverse populations are still needed to better characterize the genetics of AD. Polygenic risk scores for Alzheimer’s disease derived from individuals of European ancestry can show improved performance in multiancestry settings after incorporating genome-wide association summary statistics from diverse populations.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1598-1610"},"PeriodicalIF":31.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-025-02227-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311724","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":"Author Correction: A likelihood-based framework for demographic inference from genealogical trees","authors":"Caoqi Fan, Jordan L. Cahoon, Bryan L. Dinh, Diego Ortega-Del Vecchyo, Christian D. Huber, Michael D. Edge, Nicholas Mancuso, Charleston W. K. Chiang","doi":"10.1038/s41588-025-02257-4","DOIUrl":"10.1038/s41588-025-02257-4","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1787-1787"},"PeriodicalIF":31.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-025-02257-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304540","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":"Publisher Correction: GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia","authors":"Lincoln M. P. Shade, Yuriko Katsumata, Erin L. Abner, Khine Zin Aung, Steven A. Claas, Qi Qiao, Bernardo Aguzzoli Heberle, J. Anthony Brandon, Madeline L. Page, Timothy J. Hohman, Shubhabrata Mukherjee, Richard P. Mayeux, Lindsay A. Farrer, Gerard D. Schellenberg, Jonathan L. Haines, Walter A. Kukull, Kwangsik Nho, Andrew J. Saykin, David A. Bennett, Julie A. Schneider, The National Alzheimer’s Coordinating Center, The Alzheimer’s Disease Genetics Consortium, Mark T. W. Ebbert, Peter T. Nelson, David W. Fardo","doi":"10.1038/s41588-024-02046-5","DOIUrl":"10.1038/s41588-024-02046-5","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1791-1791"},"PeriodicalIF":31.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02046-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311726","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":"Author Correction: GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia","authors":"Lincoln M. P. Shade, Yuriko Katsumata, Erin L. Abner, Khine Zin Aung, Steven A. Claas, Qi Qiao, Bernardo Aguzzoli Heberle, J. Anthony Brandon, Madeline L. Page, Timothy J. Hohman, Shubhabrata Mukherjee, Richard P. Mayeux, Lindsay A. Farrer, Gerard D. Schellenberg, Jonathan L. Haines, Walter A. Kukull, Kwangsik Nho, Andrew J. Saykin, David A. Bennett, Julie A. Schneider, The National Alzheimer’s Coordinating Center, The Alzheimer’s Disease Genetics Consortium, Mark T. W. Ebbert, Peter T. Nelson, David W. Fardo","doi":"10.1038/s41588-024-02045-6","DOIUrl":"10.1038/s41588-024-02045-6","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1788-1788"},"PeriodicalIF":31.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02045-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311727","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":"In vivo epigenome editing with NovaIscB","authors":"Wei Li","doi":"10.1038/s41588-025-02243-w","DOIUrl":"10.1038/s41588-025-02243-w","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 6","pages":"1325-1325"},"PeriodicalIF":31.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144269048","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":"Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance","authors":"Elizabeth Dorans, Karthik Jagadeesh, Kushal Dey, Alkes L. Price","doi":"10.1038/s41588-025-02220-3","DOIUrl":"10.1038/s41588-025-02220-3","url":null,"abstract":"Methods that analyze single-cell paired RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) multiome data have shown promise in linking regulatory elements to genes. However, existing methods exhibit low concordance and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on expression quantitative trait locus (eQTL) data to assign a probabilistic score to each candidate single-nucleotide polymorphism–gene link. pgBoost attained higher enrichment than existing methods for evaluation sets derived from eQTL, activity-by-contact, CRISPR and genome-wide association study (GWAS) data. We further determined that restricting pgBoost to features from a focal cell type improved power to identify links relevant to that cell type. We highlight several examples in which pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies improves power to identify target genes underlying GWAS associations. This study introduces pgBoost, a gradient boosting framework that predicts regulatory single-nucleotide polymorphism–gene links by combining existing linking scores from single-cell multiomics datasets and genomic distance.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 7","pages":"1649-1658"},"PeriodicalIF":31.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268609","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}