A population‐genomic approach for estimating selection on polygenic traits in heterogeneous environments
A population‐genomic approach for estimating selection on polygenic traits in heterogeneous environments
复制标题
用于估计异质环境中多基因性状选择的群体基因组方法
DOI：
10.1111/1755-0998.13371
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发表时间：
2021
影响因子：
7.7
通讯作者：
Gompert, Zachariah
中科院分区：
文献类型：
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作者：
Gompert, Zachariah
强选择可以导致快速的进化变化，但选择的形式、方向和强度的时间波动可以限制更长时间内的净进化变化。波动性选择可以影响分子多样性水平以及可塑性和生态专化性的进化。尽管如此，这一现象仍然没有得到充分研究，部分原因是分析的局限性和一般难以发现不以一致方式发生的选择。在这里，我通过提出一种近似贝叶斯计算（ABC）方法来填补这一分析空白，以检测和量化群体基因组时间序列数据中多基因性状的波动选择。我提出了一个环境依赖型选择模型。然后，基于基因型-表型图来模拟选择的进化遗传后果。使用模拟，我表明，所提出的方法产生准确和精确的估计选择时，生成的数据模型是类似的模型假设的方法。当应用于豇豆种子甲虫（Callosobruchus maculatus）宿主适应的进化和再序列研究时，该方法的性能更具特质，并取决于特定的分析选择。尽管存在一些局限性，但这些结果表明，所提出的方法提供了一种强大的方法，可以将（变量）选择的原因与性状和全基因组进化模式联系起来。可以从github（https：//github.com/zgompert/fsabc.git）获得实现该方法的文档和开源计算机软件（fsabc）。
Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population genomic time‐series data. I propose a model for environment‐dependent phenotypic selection. The evolutionary genetic consequences of selection are then modelled based on a genotype–phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. The performance of the method when applied to an evolve‐and‐resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect the causes of (variable) selection to traits and genome‐wide patterns of evolution. Documentation and open‐source computer software (fsabc) implementing this method are available fromgithub(https://github.com/zgompert/fsabc.git).