Ferrando-Bernal Manuel, Brand Colin M, Capra John A
Bakar Computational Health Science Institute, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Bakar Computational Health Science Institute, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
Curr Opin Genet Dev. 2025 Feb;90:102283. doi: 10.1016/j.gde.2024.102283. Epub 2024 Nov 29.
The increasing availability of ancient DNA (aDNA) from human groups across space and time has yielded deep insights into the movements of our species. However, given the challenges of mapping from genotype to phenotype, aDNA has revealed less about the phenotypes of ancient individuals. In this review, we highlight recent advances in inferring ancient phenotypes - from the molecular to population scale - with a focus on applications enabled by new machine learning approaches. The genetic architecture of complex traits across human groups suggests that the prediction of individual-level complex traits, like behavior or disease risk, is often challenging across the relevant evolutionary distances. Thus, we propose an approach that integrates predictions of molecular phenotypes, whose mechanisms are more conserved, with nongenetic data.
从古人类群体中获取的古代DNA（aDNA）在时空上的可及性不断提高，这让我们对人类的迁徙有了深入了解。然而，鉴于从基因型映射到表型存在挑战，aDNA在揭示古代个体的表型方面作用较小。在这篇综述中，我们重点介绍了在从分子到群体尺度推断古代表型方面的最新进展，尤其关注新机器学习方法带来的应用。人类群体中复杂性状的遗传结构表明，在相关进化距离上，预测个体水平的复杂性状（如行为或疾病风险）往往具有挑战性。因此，我们提出了一种将分子表型预测（其机制更为保守）与非遗传数据相结合的方法。
