用吉布斯采样法进行重组进化

IF 1.2 4区 生物学 Q4 ECOLOGY Theoretical Population Biology Pub Date : 2023-06-01 DOI:10.1016/j.tpb.2023.03.005
Jenny M. Poulton , Lee Altenberg , Chris Watkins
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引用次数: 0

摘要

这项工作提出了一个群体遗传进化模型,包括单倍体选择、突变、重组和漂移。对于任意适应度函数,突变选择平衡可以精确地以闭合形式表示,而不需要采用扩散近似。可牵引性是通过使用n-亲本而不是2-亲本重组产生新的后代来实现的。虽然这加强了后代之间的连锁平衡,但它允许在连锁不平衡下分析整个种群。我们推导了适应度波动和对选择的反应之间的一般而精确的关系。我们的假设允许对各种非平凡适应度函数的模型的平稳分布进行分析计算。这些结果使我们能够谈论遗传结构,即不同适应度函数产生的平稳分布。本文给出了精确推导有限和无限总体平稳状态的方法。这种方法可以应用于许多适应度函数,我们给出了其中四个函数的精确计算。这些结果使我们能够研究亚稳态、适应度函数之间的权衡,甚至考虑纠错码。
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Evolution with recombination as Gibbs sampling

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-parent rather than 2-parent recombination. While this enforces linkage equilibrium among offspring, it allows analysis of the whole population under linkage disequilibrium. We derive a general and exact relationship between fitness fluctuations and response to selection. Our assumptions allow analytical calculation of the stationary distribution of the model for a variety of non-trivial fitness functions. These results allow us to speak to genetic architecture, i.e., what stationary distributions result from different fitness functions. This paper presents methods for exactly deriving stationary states for finite and infinite populations. This method can be applied to many fitness functions, and we give exact calculations for four of these. These results allow us to investigate metastability, tradeoffs between fitness functions, and even consider error-correcting codes.

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来源期刊
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
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