Centre for Lifelong Learning, University of Strathclyde, 40 George St., Glasgow G1 1QE, UK.
Jodrell Bank Centre for Astrophysics, University of Manchester, Manchester M13 9PL, UK.
Genes (Basel). 2021 Jun 4;12(6):862. doi: 10.3390/genes12060862.
Databases of commercial DNA-testing companies now contain more customers with sequenced DNA than any completed academic study, leading to growing interest from academic and forensic entities. An important result for both these entities and the test takers themselves is how closely two individuals are related in time, as calculated through one or more molecular clocks. For Y-DNA, existing interpretations of these clocks are insufficiently accurate to usefully measure relatedness in historic times. In this article, I update the methods used to calculate coalescence ages (times to most-recent common ancestor, or TMRCAs) using a new, probabilistic statistical model that includes Y-SNP, Y-STR and ancilliary historical data, and provide examples of its use.
商业 DNA 检测公司的数据库现在包含了比任何已完成的学术研究都多的接受 DNA 测序的客户,这引起了学术和法医实体越来越大的兴趣。对于这些实体和测试者本身来说,一个重要的结果是通过一个或多个分子钟计算出两个人在时间上的亲缘关系有多密切。对于 Y-DNA,这些时钟的现有解释不够准确,无法在历史时期有效地衡量相关性。在本文中,我使用一种新的概率统计模型更新了使用 Y-SNP、Y-STR 和辅助历史数据来计算合并年龄(最近共同祖先或 TMRCAs 的时间)的方法,并提供了其使用示例。