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首页> 美国卫生研究院文献>International Journal of Molecular Sciences >Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics
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Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics

机译：贝叶斯聚类和边缘检测方法在景观遗传学中推断边界的比较

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摘要

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.
机译：近来，可利用来自自然种群的遗传数据来识别个体集群或集群之间边界的技术迅速发展。因此，需要评估这些不同的技术。我们使用空间显式仿真模型来比较三种空间贝叶斯聚类程序和两种边缘检测方法。模拟了空间结构化的种群，其中连续种群被障碍细分。我们评估了每种方法在变化的同时正确识别边界位置的能力：（i）分歧后的时间，（ii）通过距离的隔离强度，（iii）遗传多样性的水平，以及（iv）跨障碍的基因流量。为了进一步评估该方法检测自然种群中遗传簇的有效性，我们使用了先前发表的有关北美美洲狮和欧洲灌木的数据。我们的结果表明，利用模拟和经验数据，贝叶斯空间聚类算法的性能优于直接边缘检测方法。在存在强距离隔离模式的情况下，所有方法均会错误地检测边界。基于此发现，我们支持贝叶斯空间聚类算法在经验数据集中进行边界检测的应用，并通过距离隔离的影响进行必要的测试。

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