了解混血分数:基因流的理论和估计。

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-10-04 DOI:10.1007/s00285-024-02146-0
Mason Liang, Mikhail Shishkin, Vladimir Shchur, Rasmus Nielsen
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引用次数: 0

摘要

估计混杂比例已成为群体基因组学中最常用的计算工具之一。然而,有关这些变量统计特性的群体遗传理论却少得可怜。我们利用重组和遗传漂移模型,开发了能准确预测种群内混杂比例均值和方差的理论结果。基于已建立的多焦点不平衡度量理论,我们证明有一组递推关系可用于推导掺杂比例分布高阶矩的期望值。我们得到了一些特殊情况下的闭式解。利用这些结果,我们开发了一种方法,用于从结构或混杂等程序中获得的估计混杂比例中估计混杂参数。我们将这种方法应用于 HapMap 3 数据,发现非裔美国人的种群历史并不能像预期的那样,用欧洲人和非洲人之间的单一混血事件来解释。从 8 代开始到现在前 2 代结束的恒定基因流模型的拟合效果最好。
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Understanding admixture fractions: theory and estimation of gene-flow.

Estimation of admixture proportions has become one of the most commonly used computational tools in population genomics. However, there is remarkably little population genetic theory on statistical properties of these variables. We develop theoretical results that can accurately predict means and variances of admixture proportions within a population using models with recombination and genetic drift. Based on established theory on measures of multilocus disequilibrium, we show that there is a set of recurrence relations that can be used to derive expectations for higher moments of the admixture proportions distribution. We obtain closed form solutions for some special cases. Using these results, we develop a method for estimating admixture parameters from estimated admixture proportions obtained from programs such as Structure or Admixture. We apply this method to HapMap 3 data and find that the population history of African Americans, as expected, is not best explained by a single admixture event between people of European and African ancestry. The model of constant gene flow starting at 8 generations and ending at 2 generations before present gives the best fit.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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