使用场地频率比分离选择性力和非选择性力。

IF 4 2区 生物学 Q1 GENETICS & HEREDITY
PLoS Genetics Pub Date : 2025-04-21 eCollection Date: 2025-04-01 DOI:10.1371/journal.pgen.1011427
Jody Hey, Vitor A C Pavinato
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引用次数: 0

摘要

提出了一种利用位点频谱估计突变适应度效应分布的新方法。与之前的方法不同,这些方法对非选择性因素进行假设,或者试图将这些因素纳入基础模型,这种新方法主要通过使用选定位点与中性位点的计数比率来避免非选择性影响。通过将两个泊松随机变量的比率视为两个高斯随机变量的比率,可以找到一组选择/中性比率的可能性表达式。这种方法也避免了估计选定位点和中性位点的相对突变率的需要。对具有关联选择效应的各种人口统计学模型的模拟表明,新的sratios方法在选择的统计检验和估计选择效应的分布方面表现良好。弱选择模型、扩展和结构化人口模型的性能优于瓶颈模型。对两个黑腹果蝇种群的应用揭示了同义位点上明显但非常弱的选择。对于非同义的地点,选择被发现与先前的估计一致,并且非洲人口比来自北卡罗来纳的人口更强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Isolating selective from non-selective forces using site frequency ratios.

A new method is introduced for estimating the distribution of mutation fitness effects using site frequency spectra. Unlike previous methods, which make assumptions about non-selective factors, or that try to incorporate such factors into the underlying model, this new method mostly avoids non-selective effects by working with the ratios of counts of selected sites to neutral sites. An expression for the likelihood of a set of selected/neutral ratios is found by treating the ratio of two Poisson random variables as the ratio of two gaussian random variables. This approach also avoids the need to estimate the relative mutation rates of selected and neutral sites. Simulations over a wide range of demographic models, with linked selection effects show that the new SFRatios method performs well for statistical tests of selection, and it performs well for estimating the distribution of selection effects. Performance was better with weak selection models and for expansion and structured demographic models than for bottleneck models. Applications to two populations of Drosophila melanogaster reveal clear but very weak selection on synonymous sites. For nonsynonymous sites, selection was found to be consistent with previous estimates and stronger for an African population than for one from North Carolina.

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来源期刊
PLoS Genetics
PLoS Genetics GENETICS & HEREDITY-
自引率
2.20%
发文量
438
期刊介绍: PLOS Genetics is run by an international Editorial Board, headed by the Editors-in-Chief, Greg Barsh (HudsonAlpha Institute of Biotechnology, and Stanford University School of Medicine) and Greg Copenhaver (The University of North Carolina at Chapel Hill). Articles published in PLOS Genetics are archived in PubMed Central and cited in PubMed.
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