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Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites

机译:使用微卫星估计的大量国产马品种中推断的种群遗传结构的主要不一致之处

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摘要

STRUCTURE remains the most applied software aimed at recovering the true, but unknown, population structure from microsatellite or other genetic markers. About 30% of ‐based studies could not be reproduced ( , 21, 2012, 4925). Here we use a large set of data from 2,323 horses from 93 domestic breeds plus the Przewalski horse, typed at 15 microsatellites, to evaluate how program settings impact the estimation of the optimal number of population clusters that best describe the observed data. Domestic horses are suited as a test case as there is extensive background knowledge on the history of many breeds and extensive phylogenetic analyses. Different methods based on different genetic assumptions and statistical procedures ( , , PCoA, and with different run scenarios) all revealed general, broad‐scale breed relationships that largely reflect known breed histories but diverged how they characterized small‐scale patterns. failed to consistently identify using the most widespread approach, the Δ method, despite very large numbers of MCMC iterations (3,000,000) and replicates (100). The interpretation of breed structure over increasing numbers of , without assuming a , was consistent with known breed histories. The over‐reliance on should be replaced by a qualitative description of clustering over increasing , which is scientifically more honest and has the advantage of being much faster and less computer intensive as lower numbers of MCMC iterations and repetitions suffice for stable results. Very large data sets are highly challenging for cluster analyses, especially when populations with complex genetic histories are investigated.
机译:结构仍然是最常用的软件,旨在从微卫星或其他遗传标记中恢复真实但未知的种群结构。约有30%的研究无法复制(,21,2012,4925)。在这里,我们使用来自93个国内品种的2,323匹马加上Przewalski匹马(以15个微卫星分型)的大量数据,来评估程序设置如何影响最佳种群数量的估计 最能描述观察到的数据。家畜适合作为测试用例,因为对许多品种的历史有广泛的背景知识并进行了广泛的系统发育分析。基于不同遗传假设和统计程序(,,PCoA和不同的运行场景)的不同方法都揭示了一般的,广泛的品种关系,这些关系在很大程度上反映了已知的品种历史,但对小规模模式的特征却有所分歧。未能一致确定 尽管有大量的MCMC迭代(3,000,000)和重复(100),但使用了最普遍的方法Δ方法。对数量增加的品种结构的解释,不假设 与已知的犬种历史一致。过度依赖 应该用对增长的聚类进行定性描述来代替,这在科学上更为诚实,并且由于MCMC迭代次数和重复次数的减少足以满足稳定的结果,因此具有更快,计算机强度更低的优势。对于聚类分析,非常大的数据集具有很高的挑战性,尤其是在调查具有复杂遗传历史的种群时。

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