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Distribution of ancestral chromosomal segments in admixed genomes and its implications for inferring population history and admixture mapping

机译:祖先染色体片段在混合基因组中的分布及其对推断种群历史和混合物图谱的意义

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摘要

The ancestral chromosomal segments in admixed genomes are of significant importance for both population history inference and admixture mapping, because they essentially provide the basic information for tracking genetic events. However, the distributions of the lengths of ancestral chromosomal segments (LACS) under some admixture models remain poorly understood. Here we introduced a theoretical framework on the distribution of LACS in two representative admixture models, that is, hybrid isolation (HI) model and gradual admixture (GA) model. Although the distribution of LACS in the GA model differs from that in the HI model, we demonstrated that the mean LACS in the HI model is approximately half of that in the GA model if both admixture proportion and admixture time in the two models are identical. We showed that the theoretical framework greatly facilitated the inference and understanding of population admixture history by analyzing African-American and Mexican empirical data. In addition, we found the peak of association signatures in the HI model was much narrower and sharper than that in the GA model, indicating that the identification of putative causal allele in the HI model is more efficient than that in the GA model. Thus admixture mapping with case-only data would be a reasonable and economical choice in the HI model due to the weak background noise. However, according to our previous studies, many populations are likely to be gradually admixed and have pretty high background linkage disequilibrium. Therefore, we suggest using a case-control approach rather than a case-only approach to conduct admixture mapping to retain the statistics power in recently admixed populations.
机译:混合基因组中的祖先染色体片段对于种群历史推断和混合物映射均具有重要意义,因为它们本质上提供了跟踪遗传事件的基本信息。然而,在某些混合模型下,祖先染色体片段(LACS)的长度分布仍然知之甚少。在这里,我们介绍了有关LACS在两种代表性混合模型中分布的理论框架,即混合隔离(HI)模型和渐进混合(GA)模型。尽管GA模型中LACS的分布与HI模型中的分布不同,但我们证明,如果两个模型中的混合比例和混合时间相同,则HI模型中的平均LACS约为GA模型中平均LACS的一半。通过分析非裔美国人和墨西哥的经验数据,我们证明了该理论框架极大地促进了人口混合史的推论和理解。此外,我们发现HI模型中的关联签名峰比GA模型中的关联峰更窄和更尖锐,这表明HI模型中假定的因果等位基因的识别比GA模型中的更有效。因此,由于背景噪声较弱,在HI模型中使用仅包含案例的数据进行混合映射将是一种合理且经济的选择。但是,根据我们先前的研究,许多种群可能会逐渐混合在一起,并且背景连锁不平衡性很高。因此,我们建议使用案例控制方法而非仅案例方法进行混合映射,以保留最近混合人群的统计能力。

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