Pub Date : 2025-12-09DOI: 10.1038/s41588-025-02457-y
Petra Gross
{"title":"Enhancer activity of transposable elements on ecDNA","authors":"Petra Gross","doi":"10.1038/s41588-025-02457-y","DOIUrl":"10.1038/s41588-025-02457-y","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 12","pages":"2942-2942"},"PeriodicalIF":29.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710784","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}
Pub Date : 2025-12-09DOI: 10.1038/s41588-025-02458-x
Hui Hua
{"title":"A twist to rose fragrance","authors":"Hui Hua","doi":"10.1038/s41588-025-02458-x","DOIUrl":"10.1038/s41588-025-02458-x","url":null,"abstract":"","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 12","pages":"2943-2943"},"PeriodicalIF":29.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145715362","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}
Pub Date : 2025-12-09DOI: 10.1038/s41588-025-02411-y
Boyang Fu, Ali Pazokitoroudi, Zhuozheng Shi, Asha Kar, Albert Xue, Aakarsh Anand, Prateek Anand, Zhengtong Liu, Richard Border, Päivi Pajukanta, Noah Zaitlen, Sriram Sankararaman
The contribution of genetic interactions (epistasis) to human complex trait variation remains poorly understood due, in part, to the statistical and computational challenges involved in testing for interaction effects. Here we introduce FAME (FAst Marginal Epistasis test), a method that can test for marginal epistasis of a single-nucleotide polymorphism (SNP) on a quantitative trait (whether the effect of an SNP on the trait is modulated by genetic background). FAME is computationally efficient, enabling tests of marginal epistasis on biobank-scale data. Applying FAME to genome-wide association study (GWAS)-significant trait-SNP associations across 53 quantitative traits and ≈300 000 unrelated White British individuals in the UK Biobank (UKBB), we identified 16 significant marginal epistasis signals across 12 traits ( $$P < frac{5times {10}^{-8}}{53}$$ ). Leveraging the scalability of FAME, we further localized marginal epistasis signals across chromosomes and estimated the proportion of variance explained by marginal epistasis effects. Our study provides evidence for interactions between individual genetic variants and polygenic background influencing complex traits. FAME is a biobank-scale method that tests whether the effect of an SNP on a quantitative trait is modulated by an individual’s polygenic background. FAME can also be used to estimate of the proportion of variance explained by such marginal epistasis effects
{"title":"A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects","authors":"Boyang Fu, Ali Pazokitoroudi, Zhuozheng Shi, Asha Kar, Albert Xue, Aakarsh Anand, Prateek Anand, Zhengtong Liu, Richard Border, Päivi Pajukanta, Noah Zaitlen, Sriram Sankararaman","doi":"10.1038/s41588-025-02411-y","DOIUrl":"10.1038/s41588-025-02411-y","url":null,"abstract":"The contribution of genetic interactions (epistasis) to human complex trait variation remains poorly understood due, in part, to the statistical and computational challenges involved in testing for interaction effects. Here we introduce FAME (FAst Marginal Epistasis test), a method that can test for marginal epistasis of a single-nucleotide polymorphism (SNP) on a quantitative trait (whether the effect of an SNP on the trait is modulated by genetic background). FAME is computationally efficient, enabling tests of marginal epistasis on biobank-scale data. Applying FAME to genome-wide association study (GWAS)-significant trait-SNP associations across 53 quantitative traits and ≈300 000 unrelated White British individuals in the UK Biobank (UKBB), we identified 16 significant marginal epistasis signals across 12 traits ( $$P < frac{5times {10}^{-8}}{53}$$ ). Leveraging the scalability of FAME, we further localized marginal epistasis signals across chromosomes and estimated the proportion of variance explained by marginal epistasis effects. Our study provides evidence for interactions between individual genetic variants and polygenic background influencing complex traits. FAME is a biobank-scale method that tests whether the effect of an SNP on a quantitative trait is modulated by an individual’s polygenic background. FAME can also be used to estimate of the proportion of variance explained by such marginal epistasis effects","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 12","pages":"3175-3184"},"PeriodicalIF":29.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02411-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705137","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}
Pub Date : 2025-12-08DOI: 10.1038/s41588-025-02431-8
Carla Tangermann, Avantika Ghosh, Martin Ziegler, Francesco Facchinetti, Jannis Stappenbeck, Yagmur Oyku Carus Sahin, Marisa Riester, Luise Carmina Viardot, Tobias Zundel, Luc Friboulet, Antoine Hollebecque, José J. Naveja, Angela Wanninger, Maria Elena Hess, Tilman Brummer, Melanie Boerries, Sonja Loges, Yohann Loriot, Anna L. Illert, Sven Diederichs
Variants of uncertain significance represent the biggest challenge for genomics-based precision oncology. Activated fibroblast growth factor receptors (FGFRs) frequently drive tumorigenesis by different genetic aberrations. However, it remains unknown which of the many point mutations affecting FGFR1, FGFR2, FGFR3 or FGFR4 in cancer are druggable, that is, activating signaling while not mediating FGFR inhibitor resistance. Here we implemented a saturation mutational scanning platform to screen all 11,520 possible point mutations covering the kinase domains of FGFR1–4. Pooled positive selection screens identified 474 activating and 738 mutations mediating resistance to the FGFR inhibitors pemigatinib and futibatinib, together revealing 301 druggable FGFR mutations analogous to a strong PS3/BS3 evidence level. The screens also identified loss-of-function mutations and an association of gain-of-function mutations with hydrophobic changes. The functional screens identified 97% of acquired resistance mutations in clinical trials. Our comprehensive catalog of every druggable mutation in the FGFR kinase domains is readily available for clinical decision support. Saturation mutagenesis screening examines 11,520 point mutations in the kinase domains of FGFR1, FGFR2, FGFR3 and FGFR4, identifying their activating and resistance properties to the FGFR inhibitors pemigatinib and futibatinib.
{"title":"Saturation mutagenesis identifies activating and resistance-inducing FGFR kinase domain mutations","authors":"Carla Tangermann, Avantika Ghosh, Martin Ziegler, Francesco Facchinetti, Jannis Stappenbeck, Yagmur Oyku Carus Sahin, Marisa Riester, Luise Carmina Viardot, Tobias Zundel, Luc Friboulet, Antoine Hollebecque, José J. Naveja, Angela Wanninger, Maria Elena Hess, Tilman Brummer, Melanie Boerries, Sonja Loges, Yohann Loriot, Anna L. Illert, Sven Diederichs","doi":"10.1038/s41588-025-02431-8","DOIUrl":"10.1038/s41588-025-02431-8","url":null,"abstract":"Variants of uncertain significance represent the biggest challenge for genomics-based precision oncology. Activated fibroblast growth factor receptors (FGFRs) frequently drive tumorigenesis by different genetic aberrations. However, it remains unknown which of the many point mutations affecting FGFR1, FGFR2, FGFR3 or FGFR4 in cancer are druggable, that is, activating signaling while not mediating FGFR inhibitor resistance. Here we implemented a saturation mutational scanning platform to screen all 11,520 possible point mutations covering the kinase domains of FGFR1–4. Pooled positive selection screens identified 474 activating and 738 mutations mediating resistance to the FGFR inhibitors pemigatinib and futibatinib, together revealing 301 druggable FGFR mutations analogous to a strong PS3/BS3 evidence level. The screens also identified loss-of-function mutations and an association of gain-of-function mutations with hydrophobic changes. The functional screens identified 97% of acquired resistance mutations in clinical trials. Our comprehensive catalog of every druggable mutation in the FGFR kinase domains is readily available for clinical decision support. Saturation mutagenesis screening examines 11,520 point mutations in the kinase domains of FGFR1, FGFR2, FGFR3 and FGFR4, identifying their activating and resistance properties to the FGFR inhibitors pemigatinib and futibatinib.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"58 1","pages":"157-168"},"PeriodicalIF":29.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41588-025-02431-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704666","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}
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