Detecting frequency-dependent selection through the effects of genotype similarity on fitness components
Detecting frequency-dependent selection through the effects of genotype similarity on fitness components
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DOI：
10.1093/evolut/qpad028
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发表时间：
2023-04-01
期刊：
影响因子：
3.3
通讯作者：
Shimizu，Kentaro K.
中科院分区：
文献类型：
--
作者：
Sato，Yasuhiro;Takahashi，Yuma;Shimizu，Kentaro K.
频率依赖选择（FDS）是一种能够维持或减少多态性的进化机制。尽管多态性数据的可用性越来越高，但从观察到的适应度分量估计FDS梯度的有效方法却很少。我们建立了基因型相似性对个体适应度的影响模型，建立了FDS的选择梯度分析。该模型使我们能够通过回归个体间基因型相似性的适应度成分来估计FDS。通过对单位点数据的分析，我们在野生拟南芥和豆娘的可见多态性上检测到已知的阴性FDS。此外，我们模拟全基因组多态性和适应度成分来修改单位点分析作为全基因组关联研究（GWAS）。模拟结果表明，通过估计基因型相似性对模拟适应度的影响，可以区分FDS的阴性或阳性。此外，我们对拟南芥生殖分支数进行了GWAS分析，发现在FDS的顶端相关多态性中，FDS的负多态性较为丰富。这些结果表明，所提出的FDS方法对可见多态性和全基因组多态性都具有潜在的适用性。总之，我们的研究为了解多态性的维持或丧失提供了一种有效的选择梯度分析方法。
Frequency-dependent selection (FDS) is an evolutionary regime that can maintain or reduce polymorphisms. Despite the increasing availability of polymorphism data, few effective methods are available for estimating the gradient of FDS from the observed fitness components. We modeled the effects of genotype similarity on individual fitness to develop a selection gradient analysis of FDS. This modeling enabled us to estimate FDS by regressing fitness components on the genotype similarity among individuals. We detected known negative FDS on the visible polymorphism in a wildArabidopsisand damselfly by applying this analysis to single-locus data. Further, we simulated genome-wide polymorphisms and fitness components to modify the single-locus analysis as a genome-wide association study (GWAS). The simulation showed that negative or positive FDS could be distinguished through the estimated effects of genotype similarity on simulated fitness. Moreover, we conducted the GWAS of the reproductive branch number inArabidopsis thalianaand found that negative FDS was enriched among the top-associated polymorphisms of FDS. These results showed the potential applicability of the proposed method for FDS on both visible polymorphism and genome-wide polymorphisms. Overall, our study provides an effective method for selection gradient analysis to understand the maintenance or loss of polymorphism.