Genome-wide Insights into the Patterns and Determinants of Fine-Scale Population Structure in Humans
Genome-wide Insights into the Patterns and Determinants of Fine-Scale Population Structure in Humans
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DOI：
10.1016/j.ajhg.2009.04.015
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发表时间：
2009-05-15
影响因子：
9.8
通讯作者：
Akey, Joshua M.
中科院分区：
文献类型：
--
作者：
Biswas, Shameek;Scheinfeldt, Laura B.;Akey, Joshua M.
研究人类种群结构的基因组模式对人类进化史和种群之间的关系具有重要的洞察力，对疾病基因图谱的绘制具有重要的现实意义。在这里，我们描述了一种基于主成分(PC)的方法来研究人类大陆内的种群结构，识别调节观察到的精细种群结构模式的潜在标记，并推断形成局部种群结构的主导进化力量。我们将这种方法应用于52个群体中944个无关个体的650K SNPs基因分型数据集，结果表明，尽管典型的PC分析集中在变异的顶轴，但关于群体结构的大量信息包含在排名较低的PC中。我们确定了18个重要的PC，其中一些区分了不同的群体。除了在PC双图中直观地表示样本簇之外，我们还估计了与每个最具信息量的变异轴显著相关的所有SNP的集合。这些多态不同于祖先信息标记(AIMS)，它们构成了一个更大的基因座集合，这些基因座驱动着种群结构的基因组签名。这些显著相关的标记在全基因组范围内的分布在很大程度上可以归因于遗传漂移的随机效应，尽管在以前被认为是最近适应性进化目标的基因组区域确实发生了显著的聚集。
Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to Studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does Occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.