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HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations

机译：HacDivSel：两种新方法（基于单倍型和离群值的）用于检测人口对中的趋异选择

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摘要

The detection of genomic regions involved in local adaptation is an important topic in current population genetics. There are several detection strategies available depending on the kind of genetic and demographic information at hand. A common drawback is the high risk of false positives. In this study we introduce two complementary methods for the detection of divergent selection from populations connected by migration. Both methods have been developed with the aim of being robust to false positives. The first method combines haplotype information with inter-population differentiation (FST). Evidence of divergent selection is concluded only when both the haplotype pattern and the FST value support it. The second method is developed for independently segregating markers i.e. there is no haplotype information. In this case, the power to detect selection is attained by developing a new outlier test based on detecting a bimodal distribution. The test computes the FST outliers and then assumes that those of interest would have a different mode. We demonstrate the utility of the two methods through simulations and the analysis of real data. The simulation results showed power ranging from 60–95% in several of the scenarios whilst the false positive rate was controlled below the nominal level. The analysis of real samples consisted of phased data from the HapMap project and unphased data from intertidal marine snail ecotypes. The results illustrate that the proposed methods could be useful for detecting locally adapted polymorphisms. The software HacDivSel implements the methods explained in this manuscript.
机译：涉及局部适应的基因组区域的检测是当前种群遗传学中​​的重要课题。根据手头的遗传和人口统计学信息的种类，有几种检测策略可用。一个常见的缺点是误报的高风险。在这项研究中，我们介绍了两种互补的方法，用于从迁移连接的人口中检测差异选择。两种方法的开发目的都是对误报具有鲁棒性。第一种方法将单倍型信息与种群间分化（FST）结合在一起。仅当单倍型和FST值都支持时才得出差异选择的证据。第二种方法被开发用于独立地分离标记，即没有单倍型信息。在这种情况下，通过基于检测双峰分布开发新的离群值测试来获得检测选择的能力。该测试将计算FST异常值，然后假定感兴趣的那些将具有不同的模式。我们通过仿真和对实际数据的分析证明了这两种方法的实用性。仿真结果表明，在几种情况下，功率范围为60％至95％，而误报率控制在标称水平以下。实际样本的分析包括HapMap项目的分阶段数据和潮间带海洋蜗牛生态型的非分阶段数据。结果表明，所提出的方法可用于检测局部适应的多态性。 HacDivSel软件实现了本手稿中介绍的方法。

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    Antonio Carvajal-Rodríguez;

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  • 年(卷),期 -1(12),4
  • 年度 -1
  • 页码 e0175944
  • 总页数 25
  • 原文格式 PDF
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