Vanderbilt Genetics Institute, Vanderbilt University Medical Center, USA.
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, USA.
Genome Biol Evol. 2021 Nov 5;13(11). doi: 10.1093/gbe/evab237.
As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1, GPX1, and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits.
随着人类遍布全球，他们适应了许多不同的环境因素，包括气候、饮食和病原体。由于许多这些适应是由多种对基因调控有微小影响的非编码变体介导的，因此很难将选择的基因组信号与特定基因联系起来，并描述对选择的调控反应。为了克服这一挑战，我们改编了 PrediXcan，一种从基因型数据推断基因调控的机器学习方法，用于分析低覆盖率的古代人类 DNA（aDNA）。首先，我们使用模拟基因组来评估适应 PrediXcan 以增加对不完整数据的稳健性的策略。将由此产生的模型应用于 490 名古代欧亚人，我们发现，在具有狩猎采集、牧民和农业生活方式的古代人群中，基因调控差异最大的基因富集了代谢和免疫功能。接下来，我们探索了在两个具有近期适应强证据的特征中，基因调控差异的贡献：饮食代谢和皮肤色素沉着。我们发现，在被认为与饮食相关的局部适应有关的基因中，存在差异调节的富集，并且对调节的预测作用常常为已知选择信号提供了解释，例如 FADS1、GPX1 和 LEPR 中的信号。相比之下，皮肤色素沉着基因在 2999 名古代欧洲人 38000 年的时间序列中几乎没有发生调节变化，这表明适应主要涉及大效应的编码变体。这项工作表明，将古代 DNA 与现代基因组相结合，可以提供有关古代人群之间的生物学差异、基因调控在适应中的作用以及遗传多样性与复杂特征之间的关系的信息。
