Bayesian inference of recent migration rates using multilocus genotypes.
Bayesian inference of recent migration rates using multilocus genotypes.
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作者：
G. Wilson;B. Rannala
提出了一种新的贝叶斯方法，使用个人的多位点基因型估计最近的移民率（在过去的几代人）之间的人口。该方法还估计了个体移民祖先的后验概率分布、群体等位基因频率、群体近交系数和其他潜在感兴趣的参数。该方法是在一个计算机程序，依赖于马尔可夫链蒙特卡罗技术进行后验概率的估计。该程序可以处理等位酶、微卫星、RFLP、SNP等基因型数据。我们放松了几个假设的早期方法检测最近的移民，使用基因型数据，最重要的是，我们允许基因型频率偏离人群内的哈迪-温伯格平衡比例。该计划证明了它应用于两个最近发表的微卫星数据集的植物物种矢车菊伞房花序和灰狼物种犬的种群。计算机模拟研究表明，该程序可以提供高度准确的估计迁移率和个人移民的祖先，人口之间的遗传分化和足够数量的标记位点。
A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.