赖特-费舍图模型和定向选择对遗传变异的影响。

IF 1.2 4区 生物学 Q4 ECOLOGY Theoretical Population Biology Pub Date : 2024-07-15 DOI:10.1016/j.tpb.2024.07.004
Ingemar Kaj , Carina F. Mugal , Rebekka Müller-Widmann
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引用次数: 0

摘要

我们引入了一个具有突变和选择的多等位基因赖特-费舍模型,该模型通过混合跳跃-扩散过程的路径来追踪单个位点的等位基因频率。该过程的状态空间由拓扑图的顶点和边给出,即边是单位间隔。顶点代表单态种群状态,边上的位置代表多态区段中祖先和衍生等位基因的双等位基因比例。在这种情况下,突变只能发生在单态位点上。我们推导出了突变-选择-漂移平衡中的静态分布,并得到了大种群规模缩放下的预期等位基因频率谱。对于具有多个独立基因座的扩展模型,我们推导出了一系列相关遗传变异度量的严格上限。在这一框架内,我们提出了精确的数学论据,从而得出结论:定向选择的存在会降低遗传变异的幅度,这受到中性进化界限的限制。
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A Wright–Fisher graph model and the impact of directional selection on genetic variation

We introduce a multi-allele Wright–Fisher model with mutation and selection such that allele frequencies at a single locus are traced by the path of a hybrid jump–diffusion process. The state space of the process is given by the vertices and edges of a topological graph, i.e. edges are unit intervals. Vertices represent monomorphic population states and positions on the edges mark the biallelic proportions of ancestral and derived alleles during polymorphic segments. In this setting, mutations can only occur at monomorphic loci. We derive the stationary distribution in mutation–selection–drift equilibrium and obtain the expected allele frequency spectrum under large population size scaling. For the extended model with multiple independent loci we derive rigorous upper bounds for a wide class of associated measures of genetic variation. Within this framework we present mathematically precise arguments to conclude that the presence of directional selection reduces the magnitude of genetic variation, as constrained by the bounds for neutral evolution.

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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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