Searching for Footprints of Positive Selection in Whole-Genome SNP Data From Nonequilibrium Populations
Searching for Footprints of Positive Selection in Whole-Genome SNP Data From Nonequilibrium Populations
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DOI：
10.1534/genetics.110.116459
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发表时间：
2010-07-01
期刊：
影响因子：
3.3
通讯作者：
Stephan, Wolfgang
中科院分区：
文献类型：
--
作者：
Pavlidis, Pavlos;Jensen, Jeffrey D.;Stephan, Wolfgang
种群基因组学的一个主要目标是重建自然种群的历史，并推断出中性和选择性的情景，可以解释当今的多态性模式。然而，中性假说和选择性假说之间的分离已经被证明是困难的，主要是因为两者都可能预测基因组中的相似模式。本研究的重点是发展的方法，可用于区分中性选择性假设在平衡和非平衡人口。这些方法利用基于位点频率谱（SFS）和连锁不平衡（LD）的统计组合。我们调查遗传变异的模式沿着重组染色体使用大量的中性和选择性假设之间的比较，如选择或中性平衡和非平衡种群和经常性选择模型。我们使用经典的P值方法进行假设检验，但我们也引入了机器学习领域的方法。我们证明，SFS和LD为基础的统计相结合，增加了权力，以检测最近的积极选择的人口经历了过去的人口变化。
A major goal of population genomics is to reconstruct the history of natural populations and to infer the neutral and selective scenarios that can explain the present-day polymorphism patterns. However, the separation between neutral and selective hypotheses has proven hard, mainly because both may predict similar patterns in the genome. This study focuses on the development of methods that can be used to distinguish neutral from selective hypotheses in equilibrium and nonequilibrium populations. These methods utilize a combination of statistics on the basis of the site frequency spectrum (SFS) and linkage disequilibrium (LD). We investigate the patterns of genetic variation along recombining chromosomes using a multitude of comparisons between neutral and selective hypotheses, such as selection or neutrality in equilibrium and nonequilibrium populations and recurrent selection models. We perform hypothesis testing using the classical P-value approach, but we also introduce methods from the machine-learning field. We demonstrate that the combination of SFS- and LD-based statistics increases the power to detect recent positive selection in populations that have experienced past demographic changes.