标题： 从等位基因频率中稳健识别局部适应性 显示英文标题

标题： Robust identification of local adaptation from allele frequencies

摘要： 比较在环境上不同的种群之间的等位基因频率长期以来一直是检测涉及局部适应的位点的工具。 然而，由于对种群等位基因频率的不完全了解以及由于共享种群历史和基因流动导致的种群之间等位基因频率的中性相关性，此类分析变得复杂。 在这里，我们开发了一套方法，基于之前在软件Bayenv中实现的贝叶斯模型，能够稳健地测试异常的等位基因频率模式，并在考虑这些复杂因素的情况下测试环境变量与等位基因频率之间的相关性。 使用这个模型，我们计算了一组“标准化的等位基因频率”，使研究人员能够在考虑种群历史带来的抽样和协方差的情况下，对多个种群应用他们选择的检验。 我们首先通过展示这些标准化频率可以用来计算强大的检验，以检测与环境变量的非参数相关性，这些检验由于 outlier 种群而更不容易产生虚假结果。 然后我们展示了这些标准化的等位基因频率如何用于构建一个检验，以检测偏离中性种群结构的SNP。 这个检验在概念上与FST相关，但由于我们考虑了种群历史，因此应该更强大。 我们还将该模型扩展到种群池的下一代测序，这是一种成本高效的估计种群等位基因频率的方法，但它意味着额外一层抽样噪声。 这些方法的实用性在模拟中得到证明，并通过对HGDP种群的人类SNP数据进行重新分析来验证。 我们方法的实现将从http://gcbias.org提供。 显示英文摘要

摘要： Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns, and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of `standardized allele frequencies' that allows investigators to apply tests of their choice to multiple populations, while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to calculate powerful tests to detect non-parametric correlations with environmental variables, which are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST but should be more powerful as we account for population history. We also extend the model to next-generation sequencing of population pools, which is a cost-efficient way to estimate population allele frequencies, but it implies an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by re-analyzing human SNP data from the HGDP populations. An implementation of our method will be available from http://gcbias.org.