Department of Oncology, Department of Biostatistics, Bioinformatics and Biomathematics, Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, 20007, USA.
Bioinformatics Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.
J Math Biol. 2020 Dec;81(6-7):1217-1250. doi: 10.1007/s00285-020-01531-9. Epub 2020 Oct 9.
In this study, we consider admixed populations through their expected heterozygosity, a measure of genetic diversity. A population is termed admixed if its members possess recent ancestry from two or more separate sources. As a result of the fusion of source populations with different genetic variants, admixed populations can exhibit high levels of genetic diversity, reflecting contributions of their multiple ancestral groups. For a model of an admixed population derived from K source populations, we obtain a relationship between its heterozygosity and its proportions of admixture from the various source populations. We show that the heterozygosity of the admixed population is at least as great as that of the least heterozygous source population, and that it potentially exceeds the heterozygosities of all of the source populations. The admixture proportions that maximize the heterozygosity possible for an admixed population formed from a specified set of source populations are also obtained under specific conditions. We examine the special case of [Formula: see text] source populations in detail, characterizing the maximal admixture in terms of the heterozygosities of the two source populations and the value of [Formula: see text] between them. In this case, the heterozygosity of the admixed population exceeds the maximal heterozygosity of the source groups if the divergence between them, measured by [Formula: see text], is large enough, namely above a certain bound that is a function of the heterozygosities of the source groups. We present applications to simulated data as well as to data from human admixture scenarios, providing results useful for interpreting the properties of genetic variability in admixed populations.
在本研究中,我们通过预期杂合度(衡量遗传多样性的指标)来考虑混合人群。如果一个群体的成员具有来自两个或更多个独立来源的近期祖先,那么该群体就被称为混合人群。由于来源群体的融合具有不同的遗传变异,混合人群可以表现出高水平的遗传多样性,反映了其多个祖先群体的贡献。对于一个由 K 个来源群体衍生的混合人群模型,我们得到了其杂合度与其来自不同来源群体的混合比例之间的关系。我们表明,混合人群的杂合度至少与最杂合的来源群体相同,并且它有可能超过所有来源群体的杂合度。在特定条件下,还获得了使由特定来源群体组成的混合群体的杂合度最大化的混合比例。我们详细研究了[Formula: see text]个来源群体的特殊情况,根据两个来源群体的杂合度和它们之间的[Formula: see text]值来描述最大混合。在这种情况下,如果它们之间的差异(由[Formula: see text]衡量)足够大,即超过一定的界限,该界限是源群体杂合度的函数,则混合群体的杂合度超过源群体的最大杂合度。我们对模拟数据以及人类混合情景的数据进行了应用,提供了用于解释混合群体遗传变异性性质的有用结果。