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Considering Genomic Scans for Selection as Coalescent Model Choice.
Genome Biology and Evolution ( IF 3.3 ) Pub Date : 2020-05-12 , DOI: 10.1093/gbe/evaa093
Rebecca B Harris 1 , Jeffrey D Jensen 1
Affiliation  

First inspired by the seminal work of Lewontin and Krakauer (1973) and Maynard Smith and Haigh (1974), genomic scans for positive selection remain a widely utilized tool in modern population genomic analysis. Yet, the relative frequency and genomic impact of selective sweeps has remained a contentious point in the field for decades, largely owing to an inability to accurately identify their presence and quantify their effects - with current methodologies generally being characterized by low true positive rates (TPR) and/or high false positive rates (FPR) under many realistic demographic models. Most of these approaches are based on Wright-Fisher assumptions and the Kingman coalescent, and generally rely on detecting outlier regions which do not conform to these neutral expectations. However, previous theoretical results have demonstrated that selective sweeps are well-characterized by an alternative class of model known as the multiple-merger coalescent (MMC). Taken together, this suggests the possibility of not simply identifying regions which reject the Kingman, but rather explicitly testing the relative fit of a genomic window to the MMC. We describe the advantages of such an approach, which owe to the branching structure differentiating selective and neutral models, and demonstrate improved power under certain demographic scenarios relative to a commonly-used approach. However, regions of the demographic parameter space continue to exist in which neither this approach, nor existing methodologies, have sufficient power to detect selective sweeps.

中文翻译:

将选择基因组扫描作为合并模型选择。

最初受到Lewontin和Krakauer(1973)以及Maynard Smith和Haigh(1974)的开创性工作的启发,用于正选择的基因组扫描仍然是现代人群基因组分析中广泛使用的工具。然而,数十年来,选择性扫描的相对频率和基因组影响仍然是该领域的一个争议点,这在很大程度上是由于无法准确地识别它们的存在并量化其影响-当前的方法通常具有低真实阳性率(TPR)的特征。 )和/或在许多现实的人口模型下的较高的误报率(FPR)。这些方法大多数基于Wright-Fisher假设和Kingman联盟,并且通常依赖于检测不符合这些中性预期的异常区域。然而,先前的理论结果表明,选择性扫描的特征是另一类称为多合并合并(MMC)的模型。综上所述,这暗示了不仅可以识别出拒绝金曼氏菌的区域,而且可以明确地测试基因组窗口与MMC的相对拟合的可能性。我们描述了这种方法的优势,这归因于区分选择性模型和中性模型的分支结构,并展示了相对于常用方法在某些人口场景下的增强功能。但是,人口统计学参数空间的区域继续存在,在该区域中,此方法或现有方法都没有足够的能力来检测选择性扫描。
更新日期:2020-05-12
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