Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA.
Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA.
Nat Commun. 2018 Feb 19;9(1):703. doi: 10.1038/s41467-018-03100-7.
Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.
评估基因组异常值的显著性，将相关的选择度量整合到一个分析框架中，以及区分适应性变体和 hitchhiking 中性变体。在这里，我们引入了 SWIF(r)，这是一种通过学习不同进化情景下多个选择统计量的分布，并计算每个基因组位点上的 sweeps 后验概率来检测选择 sweeps 的概率方法。SWIF(r) 使用用户指定的人口模型的模拟进行训练，并明确地对选择统计量的联合分布进行建模，从而提高了识别正在发生的 sweeps 区域和定位适应性突变的能力。我们使用来自南非南部的 45 名 Khomani San 狩猎采集者的基因芯片和外显子数据，鉴定出与代谢和肥胖相关的基因中适应性信号的富集。SWIF(r) 为定位有益突变提供了一个透明的概率框架，该框架可扩展到各种进化情景。
