A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size.
A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size.
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联合估计赖特邻域规模和长期有效人口规模的空间方法。
DOI：
10.1101/2023.03.10.532094
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Bradburd,GideonS
中科院分区：
文献类型：
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作者：
Hancock,ZacharyB;Toczydlowski,RachelH;Bradburd,GideonS
空间连续的遗传分化模式，这是常见的性质，往往是很差的描述现有的人口遗传理论或方法，假设要么panmixia或离散，明确界定的人口。因此，有必要在人口遗传学的统计方法，可以适应连续的地理结构，并在理想情况下使用地理参考的个人作为分析单位，而不是人口或亚群。此外，研究人员往往对描述在空间连续分布的种群的多样性感兴趣;这种多样性与生物体的扩散潜力和种群密度密切相关。一个统计模型，利用信息的隔离模式的距离，以联合推断参数，控制当地的人口（如赖特的邻里大小），和长期有效规模（Ne）的人口将是有用的。在这里，我们介绍这样一个模型，使用个人水平的成对遗传和地理距离来推断赖特的邻域大小和long-termNe。我们证明了我们的模型的实用性，将其应用到复杂的，前瞻性的人口模拟，以及两种形式的大黄蜂（熊蜂bifarius）的经验数据集。该模型表现良好的模拟数据相对于替代方法，并产生合理的经验结果，考虑到熊蜂的自然历史。由此产生的推论提供了重要的见解空间结构种群的种群遗传动力学。
Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume either panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population genetics that can accommodate continuous geographic structure, and that ideally use georeferenced individuals as the unit of analysis, rather than populations or subpopulations. In addition, researchers are often interested in describing the diversity of a population distributed continuously in space; this diversity is intimately linked to both the dispersal potential and the population density of the organism. A statistical model that leverages information from patterns of isolation by distance to jointly infer parameters that control local demography (such as Wright's neighborhood size), and the long-term effective size (Ne) of a population would be useful. Here, we introduce such a model that uses individual-level pairwise genetic and geographic distances to infer Wright's neighborhood size and long-termNe. We demonstrate the utility of our model by applying it to complex, forward-time demographic simulations as well as an empirical dataset of the two-form bumblebee (Bombus bifarius). The model performed well on simulated data relative to alternative approaches and produced reasonable empirical results given the natural history of bumblebees. The resulting inferences provide important insights into the population genetic dynamics of spatially structured populations.