Bourgeois Yann X C, Warren Ben H
School of Biological Sciences, University of Portsmouth, Portsmouth, UK.
Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, UA, CP 51, Paris, France.
Mol Ecol. 2021 Dec;30(23):6036-6071. doi: 10.1111/mec.15989. Epub 2021 Jun 19.
Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.
描绘物种的种群历史并识别局部适应性背后的基因座在功能生态学、进化生物学、保护生物学和农学中至关重要。高通量测序技术的不断改进推动了广泛物种全基因组数据的产生。种群基因组学现在提供了一些工具，以便更好地将选择整合到历史框架中，并在重建种群历史时考虑选择因素。然而，这种改进伴随着大量的分析工具，这可能会让用户感到困惑并望而却步。这种困惑限制了从复杂基因组数据集中有效获取的信息量，并阻碍了最新分析工具在保护生物学等领域的传播。它还可能导致方法之间的冗余。为了解决这些问题，我们对100多种可处理全基因组数据的先进方法进行了概述。我们总结了它们用于推断种群历史和选择的策略，并讨论了它们的一些局限性。一个列出这些方法的网站可在www.methodspopgen.com上获取。
