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本文引用的文献

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作为选择性清除标志的连锁不平衡。

Linkage disequilibrium as a signature of selective sweeps.

作者信息

Kim Yuseob, Nielsen Rasmus

机构信息

Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853, USA.

出版信息

Genetics. 2004 Jul;167(3):1513-24. doi: 10.1534/genetics.103.025387.

DOI:10.1534/genetics.103.025387
PMID:15280259
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1470945/
Abstract

The hitchhiking effect of a beneficial mutation, or a selective sweep, generates a unique distribution of allele frequencies and spatial distribution of polymorphic sites. A composite-likelihood test was previously designed to detect these signatures of a selective sweep, solely on the basis of the spatial distribution and marginal allele frequencies of polymorphisms. As an excess of linkage disequilibrium (LD) is also known to be a strong signature of a selective sweep, we investigate how much statistical power is increased by the inclusion of information regarding LD. The expected pattern of LD is predicted by a genealogical approach. Both theory and simulation suggest that strong LD is generated in narrow regions at both sides of the location of beneficial mutation. However, a lack of LD is expected across the two sides. We explore various ways to detect this signature of selective sweeps by statistical tests. A new composite-likelihood method is proposed to incorporate information regarding LD. This method enables us to detect selective sweeps and estimate the parameters of the selection model better than the previous composite-likelihood method that does not take LD into account. However, the improvement made by including LD is rather small, suggesting that most of the relevant information regarding selective sweeps is captured by the spatial distribution and marginal allele frequencies of polymorphisms.

摘要

有益突变的搭便车效应,即选择性清除,会产生独特的等位基因频率分布和多态性位点的空间分布。之前设计了一种复合似然检验,仅基于多态性的空间分布和边际等位基因频率来检测选择性清除的这些特征。由于连锁不平衡(LD)过剩也是选择性清除的一个强烈特征,我们研究纳入LD信息能在多大程度上提高统计功效。LD的预期模式由系谱方法预测。理论和模拟均表明,在有益突变位置两侧的狭窄区域会产生强LD。然而,预计两侧之间不存在LD。我们探索通过统计检验来检测这种选择性清除特征的各种方法。提出了一种新的复合似然方法来纳入LD信息。与之前未考虑LD的复合似然方法相比,该方法能使我们更好地检测选择性清除并估计选择模型的参数。然而,纳入LD带来的改进相当小,这表明关于选择性清除的大多数相关信息已由多态性的空间分布和边际等位基因频率所捕获。