Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany.
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
Proc Natl Acad Sci U S A. 2019 Jan 29;116(5):1639-1644. doi: 10.1073/pnas.1814338116. Epub 2019 Jan 15.
Several studies have suggested that introgressed Neandertal DNA was subjected to negative selection in modern humans. A striking observation in support of this is an apparent monotonic decline in Neandertal ancestry observed in modern humans in Europe over the past 45,000 years. Here, we show that this decline is an artifact likely caused by gene flow between modern human populations, which is not taken into account by statistics previously used to estimate Neandertal ancestry. When we apply a statistic that avoids assumptions about modern human demography by taking advantage of two high-coverage Neandertal genomes, we find no evidence for a change in Neandertal ancestry in Europe over the past 45,000 years. We use whole-genome simulations of selection and introgression to investigate a wide range of model parameters and find that negative selection is not expected to cause a significant long-term decline in genome-wide Neandertal ancestry. Nevertheless, these models recapitulate previously observed signals of selection against Neandertal alleles, in particular the depletion of Neandertal ancestry in conserved genomic regions. Surprisingly, we find that this depletion is strongest in regulatory and conserved noncoding regions and in the most conserved portion of protein-coding sequences.
几项研究表明，已融入的尼安德特人 DNA 在现代人类中受到了负选择。支持这一观点的一个显著观察结果是，在过去的 45000 年里，在欧洲的现代人类中观察到尼安德特人祖先明显呈单调下降趋势。在这里，我们表明这种下降是一种假象，很可能是由于现代人类群体之间的基因流动造成的，而这一点在以前用于估计尼安德特人祖先的统计数据中没有考虑到。当我们应用一种统计方法时，这种方法通过利用两个高覆盖率的尼安德特人基因组来避免对现代人类人口统计学的假设，我们发现过去 45000 年来欧洲尼安德特人祖先没有变化的证据。我们使用全基因组选择和渗入的模拟来研究广泛的模型参数，并发现负选择不太可能导致基因组范围内尼安德特人祖先的长期显著下降。尽管如此，这些模型再现了先前观察到的针对尼安德特人等位基因的选择信号，特别是在保守基因组区域中尼安德特人祖先的耗竭。令人惊讶的是，我们发现这种耗竭在调控和保守的非编码区域以及蛋白质编码序列中最保守的部分最强。
