Using Environmental Correlations to Identify Loci Underlying Local Adaptation
Using Environmental Correlations to Identify Loci Underlying Local Adaptation
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DOI：
10.1534/genetics.110.114819
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发表时间：
2010-08-01
期刊：
影响因子：
3.3
通讯作者：
Pritchard, Jonathan K.
中科院分区：
文献类型：
--
作者：
Coop, Graham;Witonsky, David;Pritchard, Jonathan K.
参与局部适应的基因座可能通过等位基因频率与重要生态变量之间的异常相关性或地理区域之间的极端等位基因频率差异来识别。然而，这种比较由于样本量的差异以及由于共享历史和基因流动而导致的群体间等位基因频率的中性相关性而变得复杂。为了克服这些困难，我们开发了一种贝叶斯方法，该方法估计来自一组标记的群体之间等位基因频率的协方差的经验模式，然后将其用作单个SNP测试的空模型。在我们的模型中，一个等位基因在整个群体中的样本频率是从一组潜在的群体频率中提取的;这些群体频率的变换被假设为遵循多元正态分布。我们首先估计的协方差矩阵的多变量正常跨位点使用蒙特卡罗马尔可夫链。在每个SNP处，我们然后为模型提供支持的度量，即贝叶斯因子，其中与仅由协方差矩阵给出的模型相比，环境变量对变换的等位基因频率具有线性影响。该测试通过功率模拟显示优于现有的相关性测试。我们还证明，我们的方法可用于识别具有异常大的等位基因频率差异的SNP，并为基于成对或全局F-ST的测试提供了强有力的替代方案。http://www.eve.ucdavis.edu/gmcoop/
Loci involved in local adaptation can potentially be identified by an unusual correlation between allele frequencies and important ecological variables or by extreme allele frequency differences between geographic regions. However, such comparisons are complicated by differences in sample sizes and the neutral correlation of allele frequencies across populations due to shared history and gene flow. To overcome these difficulties, we have developed a Bayesian method that estimates the empirical pattern of covariance in allele frequencies between populations from a set of markers and then uses this as a null model for a test at individual SNPs. In our model the sample frequencies of an allele across populations are drawn from a set of underlying population frequencies; a transform of these population frequencies is assumed to follow a multivariate normal distribution. We first estimate the covariance matrix of this multivariate normal across loci using a Monte Carlo Markov chain. At each SNP, we then provide a measure of the support, a Bayes factor, for a model where an environmental variable has a linear effect on the transformed allele frequencies compared to a model given by the covariance matrix alone. This test is shown through power simulations to outperform existing correlation tests. We also demonstrate that our method can be used to identify SNPs with unusually large allele frequency differentiation and offers a powerful alternative to tests based on pairwise or global F-ST. Software is available at http://www.eve.ucdavis.edu/gmcoop/.