Disentangling the effects of geographic and ecological isolation on genetic differentiation.
Disentangling the effects of geographic and ecological isolation on genetic differentiation.
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DOI：
10.1111/evo.12193
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发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Coop GM
中科院分区：
文献类型：
--
作者：
Bradburd GS;Ralph PL;Coop GM
由于地理距离和生态或环境的差异，种群可能在遗传上被隔离，从而降低了成功迁移的速度。实证研究往往试图调查遗传分化和一些生态变量之间的关系，同时考虑地理距离，但这个问题的常见方法（如部分曼特尔测试）有一些缺点。在这篇文章中，我们提出了一种贝叶斯方法，使用户能够量化地理距离和生态距离的相对贡献，遗传分化之间的抽样群体或个人。我们在一组人口中的一组非连锁基因座的空间相关的高斯过程，其中的协方差结构是一个地理和生态距离的递减函数的等位基因频率建模。模型的参数估计使用马尔可夫链蒙特卡罗算法。我们称这种方法为空间结构和局部生态学的等位基因分化贝叶斯估计（BEDASSLE），并在统计平台R中以用户友好的格式实现了它。我们证明了它的实用性与模拟研究和实证应用人类和大刍草数据集。
Populations can be genetically isolated by both geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte datasets.
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