Zhai Weiwei, Nielsen Rasmus, Slatkin Montgomery
Department of Integrative Biology, University of California, Berkeley, USA.
Mol Biol Evol. 2009 Feb;26(2):273-83. doi: 10.1093/molbev/msn231. Epub 2008 Oct 14.
In this report, we investigate the statistical power of several tests of selective neutrality based on patterns of genetic diversity within and between species. The goal is to compare tests based solely on population genetic data with tests using comparative data or a combination of comparative and population genetic data. We show that in the presence of repeated selective sweeps on relatively neutral background, tests based on the d(N)/d(S) ratios in comparative data almost always have more power to detect selection than tests based on population genetic data, even if the overall level of divergence is low. Tests based solely on the distribution of allele frequencies or the site frequency spectrum, such as the Ewens-Watterson test or Tajima's D, have less power in detecting both positive and negative selection because of the transient nature of positive selection and the weak signal left by negative selection. The Hudson-Kreitman-Aguadé test is the most powerful test for detecting positive selection among the population genetic tests investigated, whereas McDonald-Kreitman test typically has more power to detect negative selection. We discuss our findings in the light of the discordant results obtained in several recently published genomic scans.
在本报告中，我们研究了基于物种内部和物种之间遗传多样性模式的几种选择中性检验的统计功效。目的是比较仅基于群体遗传数据的检验与使用比较数据或比较数据与群体遗传数据相结合的检验。我们表明，在相对中性背景上存在重复选择清除的情况下，基于比较数据中d(N)/d(S)比率的检验几乎总是比基于群体遗传数据的检验具有更强的检测选择的能力，即使总体分化水平较低。仅基于等位基因频率分布或位点频率谱的检验，如Ewens-Watterson检验或Tajima's D检验，由于正选择的短暂性和负选择留下的微弱信号，在检测正选择和负选择方面的能力较弱。在所研究的群体遗传检验中，Hudson-Kreitman-Aguadé检验是检测正选择最有效的检验，而McDonald-Kreitman检验通常在检测负选择方面具有更强的能力。我们根据最近发表的几项基因组扫描中获得的不一致结果讨论了我们的发现。
