Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA.
Philos Trans R Soc Lond B Biol Sci. 2010 Aug 27;365(1552):2459-68. doi: 10.1098/rstb.2010.0032.
Humans show tremendous phenotypic diversity across geographically distributed populations, and much of this diversity undoubtedly results from genetic adaptations to different environmental pressures. The availability of genome-wide genetic variation data from densely sampled populations offers unprecedented opportunities for identifying the loci responsible for these adaptations and for elucidating the genetic architecture of human adaptive traits. Several approaches have been used to detect signals of selection in human populations, and these approaches differ in the assumptions they make about the underlying mode of selection. We contrast the results of approaches based on haplotype structure and differentiation of allele frequencies to those from a method for identifying single nucleotide polymorphisms strongly correlated with environmental variables. Although the first group of approaches tends to detect new beneficial alleles that were driven to high frequencies by selection, the environmental correlation approach has power to identify alleles that experienced small shifts in frequency owing to selection. We suggest that the first group of approaches tends to identify only variants with relatively strong phenotypic effects, whereas the environmental correlation methods can detect variants that make smaller contributions to an adaptive trait.
人类在地理上分布的种群中表现出巨大的表型多样性，而这种多样性很大程度上无疑是由于对不同环境压力的遗传适应。来自密集采样人群的全基因组遗传变异数据的可用性为鉴定负责这些适应的基因座以及阐明人类适应性特征的遗传结构提供了前所未有的机会。已经使用了几种方法来检测人类群体中的选择信号，这些方法在关于潜在选择模式的假设方面有所不同。我们将基于单倍型结构和等位基因频率分化的方法的结果与一种用于识别与环境变量强烈相关的单核苷酸多态性的方法的结果进行对比。尽管第一组方法倾向于检测因选择而被推向高频率的新有利等位基因，但环境相关性方法有能力识别由于选择而经历频率小变化的等位基因。我们认为，第一组方法往往只识别具有相对较强表型效应的变体，而环境相关性方法可以检测到对适应性特征贡献较小的变体。
