Nature genetics最新文献

{"title":"A multilineage screen identifies actionable synthetic lethal interactions in human cancers","authors":"Samson H. Fong, Brent M. Kuenzi, Nicole M. Mattson, John Lee, Kyle Sanchez, Ana Bojorquez-Gomez, Kyle Ford, Brenton P. Munson, Katherine Licon, Sarah Bergendahl, John Paul Shen, Jason F. Kreisberg, Prashant Mali, Jeffrey H. Hager, Michael A. White, Trey Ideker","doi":"10.1038/s41588-024-01971-9","DOIUrl":"https://doi.org/10.1038/s41588-024-01971-9","url":null,"abstract":"<p>Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"168 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665532","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":"Intermediate cells with activated JAK/STAT signaling in prostate regeneration and diseases","authors":"","doi":"10.1038/s41588-024-01977-3","DOIUrl":"https://doi.org/10.1038/s41588-024-01977-3","url":null,"abstract":"NKX3.1-expressing intermediate Basal-B cells represent a transient basal stem cell state during prostate regeneration, inflammation and cancer initiation. Remarkably, activation of JAK/STAT signaling is essential in regulating expansion and differentiation of Basal-B-like cells during prostate inflammation, identifying this signaling pathway as a potential therapeutic target in prostatitis associated with increased Basal-B signature.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"20 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637043","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":"Toward GDPR compliance with the Helmholtz Munich genotype imputation server","authors":"N. William Rayner, Young-Chan Park, Christian Fuchsberger, Andrei Barysenka, Eleftheria Zeggini","doi":"10.1038/s41588-024-02012-1","DOIUrl":"https://doi.org/10.1038/s41588-024-02012-1","url":null,"abstract":"<p>Genomics has the potential to revolutionize healthcare, empowering personalized disease management, including precision prevention. Genome-wide association studies (GWAS) have been instrumental in generating new biological insights into complex human diseases¹. The power of GWAS can be increased by increasing sample size through meta-analysis, which requires the imputation and analysis of genotypes that may be untyped across some studies. Imputation relies on the availability of phased haplotype reference panels of whole-genome-sequenced individuals². These are not amenable to sharing with researchers who need to impute their GWAS data, primarily for reasons of data access and security, dataset size, and scale of computing resources required to enable imputation. Imputation servers have, therefore, been developed to provide a solution: researchers upload their genotyped dataset to the imputation server that hosts the reference panels and imputation machinery, where the data are imputed, and then downloaded back to the researchers’ individual local computing environment. There are a number of imputation servers that serve the global community of researchers, including two based in the USA (University of Michigan, https://imputationserver.sph.umich.edu/index.html and TOPMed, https://imputation.biodatacatalyst.nhlbi.nih.gov/), one based in the UK (Wellcome Sanger Institute, https://imputation.sanger.ac.uk/?about=1) and one based at Kiel University in Germany (https://hybridcomputing.ikmb.uni-kiel.de). Here, we have developed a European Union (EU)-based imputation server serving the community at large, based in Munich, Germany (https://imputationserver.helmholtz-munich.de/), to assist users in complying with their General Data Protection Regulation (GDPR) requirements.</p><p>The need for EU-based imputation servers arises from restrictions imposed by GDPR law³, a comprehensive data privacy law in the EU. Genetic data are considered a special category of personal data under GDPR, and hence they are subject to strict data sharing rules and safeguards⁴. Uploading of genotype data to imputation servers not residing within the EU or covered by an adequacy agreement constitutes a breach of GDPR, unless explicitly covered in informed consent forms for the respective study. Here, we introduce the Helmholtz Munich Imputation Server, designed to provide a cost-free genotype imputation service in a GDPR-compliant manner for EU-based researchers, as well as for researchers globally.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"5 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637060","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 identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target","authors":"Tanmoy Roychowdhury,&nbsp;Derek Klarin,&nbsp;Michael G. Levin,&nbsp;Joshua M. Spin,&nbsp;Yae Hyun Rhee,&nbsp;Alicia Deng,&nbsp;Colwyn A. Headley,&nbsp;Noah L. Tsao,&nbsp;Corry Gellatly,&nbsp;Verena Zuber,&nbsp;Fred Shen,&nbsp;Whitney E. Hornsby,&nbsp;Ina Holst Laursen,&nbsp;Shefali S. Verma,&nbsp;Adam E. Locke,&nbsp;Gudmundur Einarsson,&nbsp;Gudmar Thorleifsson,&nbsp;Sarah E. Graham,&nbsp;Ozan Dikilitas,&nbsp;Jack W. Pattee,&nbsp;Renae L. Judy,&nbsp;Ferran Pauls-Verges,&nbsp;Jonas B. Nielsen,&nbsp;Brooke N. Wolford,&nbsp;Ben M. Brumpton,&nbsp;Jaume Dilmé,&nbsp;Olga Peypoch,&nbsp;Laura Calsina Juscafresa,&nbsp;Todd L. Edwards,&nbsp;Dadong Li,&nbsp;Karina Banasik,&nbsp;Søren Brunak,&nbsp;Rikke L. Jacobsen,&nbsp;Minerva T. Garcia-Barrio,&nbsp;Jifeng Zhang,&nbsp;Lars M. Rasmussen,&nbsp;Regent Lee,&nbsp;Ashok Handa,&nbsp;Anders Wanhainen,&nbsp;Kevin Mani,&nbsp;Jes S. Lindholt,&nbsp;Lasse M. Obel,&nbsp;Ewa Strauss,&nbsp;Grzegorz Oszkinis,&nbsp;Christopher P. Nelson,&nbsp;Katie L. Saxby,&nbsp;Joost A. van Herwaarden,&nbsp;Sander W. van der Laan,&nbsp;Jessica van Setten,&nbsp;Mercedes Camacho,&nbsp;Frank M. Davis,&nbsp;Rachael Wasikowski,&nbsp;Lam C. Tsoi,&nbsp;Johann E. Gudjonsson,&nbsp;Jonathan L. Eliason,&nbsp;Dawn M. Coleman,&nbsp;Peter K. Henke,&nbsp;Santhi K. Ganesh,&nbsp;Y. Eugene Chen,&nbsp;Weihua Guan,&nbsp;James S. Pankow,&nbsp;Nathan Pankratz,&nbsp;Ole B. Pedersen,&nbsp;Christian Erikstrup,&nbsp;Weihong Tang,&nbsp;Kristian Hveem,&nbsp;Daniel Gudbjartsson,&nbsp;Solveig Gretarsdottir,&nbsp;Unnur Thorsteinsdottir,&nbsp;Hilma Holm,&nbsp;Kari Stefansson,&nbsp;Manuel A. Ferreira,&nbsp;Aris Baras,&nbsp;Iftikhar J. Kullo,&nbsp;Marylyn D. Ritchie,&nbsp;Alex H. Christensen,&nbsp;Kasper K. Iversen,&nbsp;Nikolaj Eldrup,&nbsp;Henrik Sillesen,&nbsp;Sisse R. Ostrowski,&nbsp;Henning Bundgaard,&nbsp;Henrik Ullum,&nbsp;Stephen Burgess,&nbsp;Dipender Gill,&nbsp;Katherine Gallagher,&nbsp;Maria Sabater-Lleal,&nbsp;DiscovEHR,&nbsp;Regeneron Genetics Center,&nbsp;UK Aneurysm Growth Study,&nbsp;DBDS Genomic Consortium,&nbsp;VA Million Veteran Program,&nbsp;Ida Surakka,&nbsp;Gregory T. Jones,&nbsp;Matthew J. Bown,&nbsp;Philip S. Tsao,&nbsp;Cristen J. Willer,&nbsp;Scott M. Damrauer","doi":"10.1038/s41588-023-01510-y","DOIUrl":"10.1038/s41588-023-01510-y","url":null,"abstract":"Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model. Genome-wide association meta-analysis of AAA identifies 121 independent risk loci and highlights potential therapeutic targets such as proprotein convertase, subtilisin/kexin-type 9 (PCSK9).","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 11","pages":"1831-1842"},"PeriodicalIF":30.8,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41236921","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":"Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton","authors":"Jiaqi You,&nbsp;Zhenping Liu,&nbsp;Zhengyang Qi,&nbsp;Yizan Ma,&nbsp;Mengling Sun,&nbsp;Ling Su,&nbsp;Hao Niu,&nbsp;Yabing Peng,&nbsp;Xuanxuan Luo,&nbsp;Mengmeng Zhu,&nbsp;Yuefan Huang,&nbsp;Xing Chang,&nbsp;Xiubao Hu,&nbsp;Yuqi Zhang,&nbsp;Ruizhen Pi,&nbsp;Yuqi Liu,&nbsp;Qingying Meng,&nbsp;Jianying Li,&nbsp;Qinghua Zhang,&nbsp;Longfu Zhu,&nbsp;Zhongxu Lin,&nbsp;Ling Min,&nbsp;Daojun Yuan,&nbsp;Corrinne E. Grover,&nbsp;David D. Fang,&nbsp;Keith Lindsey,&nbsp;Jonathan F. Wendel,&nbsp;Lili Tu,&nbsp;Xianlong Zhang,&nbsp;Maojun Wang","doi":"10.1038/s41588-023-01530-8","DOIUrl":"10.1038/s41588-023-01530-8","url":null,"abstract":"Polyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cotton Gossypium hirsutum by sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants. Genome and transcriptome analyses of 376 Gossypium hirsutum accessions uncover the regulation of gene expression during fiber development in allotetraploid cotton and highlight the potential for fiber quality improvement.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 11","pages":"1987-1997"},"PeriodicalIF":30.8,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41236922","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":"Precise identification of cell states altered in disease using healthy single-cell references","authors":"Emma Dann,&nbsp;Ana-Maria Cujba,&nbsp;Amanda J. Oliver,&nbsp;Kerstin B. Meyer,&nbsp;Sarah A. Teichmann,&nbsp;John C. Marioni","doi":"10.1038/s41588-023-01523-7","DOIUrl":"10.1038/s41588-023-01523-7","url":null,"abstract":"Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use. In single-cell studies, combining healthy reference atlases and designed control datasets allows more precise identification of disease-associated cell states.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 11","pages":"1998-2008"},"PeriodicalIF":30.8,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205962","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":"Neuronal chromatin meta-domains","authors":"Tiago Faial","doi":"10.1038/s41588-023-01541-5","DOIUrl":"10.1038/s41588-023-01541-5","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 10","pages":"1609-1609"},"PeriodicalIF":30.8,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205966","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":"Calling SNVs in single cells","authors":"Kyle Vogan","doi":"10.1038/s41588-023-01544-2","DOIUrl":"10.1038/s41588-023-01544-2","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 10","pages":"1609-1609"},"PeriodicalIF":30.8,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205965","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":"Saving endangered species with genomic prediction","authors":"Michael Fletcher","doi":"10.1038/s41588-023-01542-4","DOIUrl":"10.1038/s41588-023-01542-4","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 10","pages":"1609-1609"},"PeriodicalIF":30.8,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205968","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":"Protein structural alignment using deep learning","authors":"Wei Li","doi":"10.1038/s41588-023-01543-3","DOIUrl":"10.1038/s41588-023-01543-3","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"55 10","pages":"1609-1609"},"PeriodicalIF":30.8,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205967","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}