Zhang Xinjun, Yang Jiongxuan, Zhu Lingxuan, Sachdev Nina, Mooney Jazlyn, Sankararaman Sriram, Lohmueller Kirk E
Department of Human Genetics, Medical School, University of Michigan.
Department of Biostatistics, School of Public Health, University of Michigan.
bioRxiv. 2025 May 7:2025.05.07.652751. doi: 10.1101/2025.05.07.652751.
Negative natural selection on deleterious mutations plays a key role in shaping human genetic variation. Understanding the dominance of deleterious mutations is critical as it can fundamentally impact the rate and efficiency of natural selection, the magnitude of inbreeding depression, and the prevalence and evolution of genetic diseases. Despite its inarguable importance, the dominance effects of mutations remain poorly understood in humans, primarily because existing statistical methods cannot distinguish them from the overall selective effects of mutations. In this work, we take a fundamentally different approach to infer dominance by leveraging the distribution of Neanderthal ancestry across the human genome. We show through simulations that recessive deleterious mutations lead to an increase in archaic introgressed ancestry in the absence of positive selection, contrary to what is expected when deleterious mutations are additive. Leveraging this unique pattern, we develop a machine learning classifier to infer dominance in genomic windows at a megabase resolution, trained on simulations of a human demographic model with Neanderthal introgression using fully recessive or additive mutations. Our method demonstrates robust accuracy at detecting genomic windows containing recessive deleterious mutations, with particularly high power in exon-dense regions. When applied to the non-African populations from the 1000 Genomes Project, we find that approximately 3-9% of the human genome is enriched for recessive mutations with most recessive regions shared across human populations. Furthermore, our method reveals that recessive deleterious mutations are not evenly distributed across the genome: regions enriched for recessive mutations are significantly depleted of haploinsufficient genes and runs of homozygosity, and are enriched with non-additive variants associated with complex traits. Overall, our Neanderthal ancestry-based approach reveals the presence of recessive deleterious mutations in the human genome and suggests that these mutations are found in regions containing genes associated with metabolism and immune-related traits.
对有害突变的负向自然选择在塑造人类遗传变异中起着关键作用。了解有害突变的显性情况至关重要，因为它会从根本上影响自然选择的速率和效率、近亲繁殖衰退的程度以及遗传疾病的患病率和演变。尽管其重要性无可争议，但人类对突变的显性效应仍知之甚少，主要原因是现有统计方法无法将它们与突变的整体选择效应区分开来。在这项工作中，我们采用了一种截然不同的方法，通过利用尼安德特人祖先在人类基因组中的分布来推断显性情况。我们通过模拟表明，在没有正向选择的情况下，隐性有害突变会导致古老渗入祖先的增加，这与有害突变是加性时的预期情况相反。利用这种独特模式，我们开发了一种机器学习分类器，以兆碱基分辨率推断基因组窗口中的显性情况，该分类器在使用完全隐性或加性突变的尼安德特人渗入人类人口模型模拟上进行训练。我们的方法在检测包含隐性有害突变的基因组窗口方面表现出强大的准确性，在外显子密集区域具有特别高的功效。当应用于千人基因组计划中的非非洲人群时，我们发现大约3 - 9%的人类基因组富含隐性突变，大多数隐性区域在不同人群中共享。此外，我们的方法表明隐性有害突变在基因组中分布并不均匀：富含隐性突变的区域单倍体不足基因和纯合子连续区域显著减少，并且富含与复杂性状相关的非加性变异。总体而言，我们基于尼安德特人祖先的方法揭示了人类基因组中存在隐性有害突变，并表明这些突变存在于与代谢和免疫相关性状的基因所在区域。
