选择(遗传算法)
突变
体细胞
人口
比例(比率)
生物
遗传学
进化生物学
地理
人口学
计算机科学
基因
地图学
人工智能
社会学
作者
Andrew Lawson,Federico Abascal,Pantelis Nicola,Stefanie V. Lensing,Amy L. Roberts,Georgios Kalantzis,Adrian Baez‐Ortega,Natalia Brzozowska,Julia S. El-Sayed Moustafa,Dovile Vaitkute,Belma Jakupovic,Ayrun Nessa,Samuel Wadge,Marc F. Österdahl,Anna Paterson,Doris M. Rassl,Raul E. Alcantara,Laura P. O’Neill,Sara Widaa,Siobhan Austin-Guest
出处
期刊:Nature
[Nature Portfolio]
日期:2025-10-08
被引量:3
标识
DOI:10.1038/s41586-025-09584-w
摘要
As we age, many tissues become colonized by microscopic clones carrying somatic driver mutations1-7. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of this phenomenon remains limited due to the challenge of detecting mutations in small clones. Here we introduce a new version of nanorate sequencing (NanoSeq)8, a duplex sequencing method with an error rate lower than five errors per billion base pairs, which is compatible with whole-exome and targeted capture. Deep sequencing of polyclonal samples with single-molecule sensitivity simultaneously profiles large numbers of clones, providing accurate mutation rates, signatures and driver frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 blood samples from a twin cohort, we report an extremely rich selection landscape, with 46 genes under positive selection in oral epithelium, more than 62,000 driver mutations and evidence of negative selection in essential genes. High-resolution maps of selection across coding and non-coding sites are obtained for many genes: a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition or selection of somatic mutations. Accurate single-molecule sequencing provides a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.
科研通智能强力驱动
Strongly Powered by AbleSci AI