Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
Science. 2022 Feb 25;375(6583):eabi8264. doi: 10.1126/science.abi8264.
The sequencing of modern and ancient genomes from around the world has revolutionized our understanding of human history and evolution. However, the problem of how best to characterize ancestral relationships from the totality of human genomic variation remains unsolved. Here, we address this challenge with nonparametric methods that enable us to infer a unified genealogy of modern and ancient humans. This compact representation of multiple datasets explores the challenges of missing and erroneous data and uses ancient samples to constrain and date relationships. We demonstrate the power of the method to recover relationships between individuals and populations as well as to identify descendants of ancient samples. Finally, we introduce a simple nonparametric estimator of the geographical location of ancestors that recapitulates key events in human history.
来自世界各地的现代和古代基因组的测序极大地改变了我们对人类历史和进化的理解。然而，如何从人类基因组变异的整体中最好地描述祖先关系的问题仍未解决。在这里，我们使用能够推断现代人和古代人类统一谱系的非参数方法来解决这一挑战。这种对多个数据集的紧凑表示形式探索了缺失和错误数据的挑战，并利用古代样本来约束和确定关系的时间。我们展示了该方法恢复个体和群体之间关系以及识别古代样本后代的能力。最后，我们引入了一种简单的祖先地理位置的非参数估计器，该估计器再现了人类历史上的关键事件。
