Department of Molecular and Cell Biology and Center for Computational Biology, University of California, Berkeley, California, USA; email:
UCL Genetics Institute and Research Department of Genetics, Evolution, and Environment, University College London, London, United Kingdom; email:
Annu Rev Genomics Hum Genet. 2023 Aug 25;24:305-332. doi: 10.1146/annurev-genom-111422-025117. Epub 2023 May 23.
Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.
遗传数据记录了我们的进化历史。大规模的人类群体数据集在不同的地理区域和时间尺度上的可用性,以及分析这些数据的计算方法的进步,改变了我们利用遗传数据了解过去进化的能力。在这里,我们回顾了一些广泛使用的统计方法,这些方法利用基因组数据来探索和描述群体关系和历史。我们描述了常用方法的基本原理、解释和重要的局限性。为了说明问题,我们将这些技术应用于人类基因组多样性计划中 929 名代表 53 个全球群体的全基因组常染色体数据。最后,我们讨论了基因组方法在研究人口历史方面的新前沿。总之,本综述强调了 DNA 推断人类进化历史特征的强大功能(和局限性),补充了考古学、人类学和语言学等其他学科的知识。