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需要多少个标记位点？使用两种流行的群体遗传算法分析显性标记数据集。

How many marker loci are necessary? Analysis of dominant marker data sets using two popular population genetic algorithms.

机构信息

Department of Horticultural Science, University of Minnesota 1970 Folwell Ave, Saint Paul, Minnesota, 55108.

出版信息

Ecol Evol. 2013 Sep;3(10):3455-70. doi: 10.1002/ece3.725. Epub 2013 Aug 28.

DOI:10.1002/ece3.725
PMID:24223282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3797491/
Abstract

The number of marker loci required to answer a given research question satisfactorily is especially important for dominant markers since they have a lower information content than co-dominant marker systems. In this study, we used simulated dominant marker data sets to determine the number of dominant marker loci needed to obtain satisfactory results from two popular population genetic analyses: STRUCTURE and AMOVA (analysis of molecular variance). Factors such as migration, level of population differentiation, and unequal sampling were varied in the data sets to mirror a range of realistic research scenarios. AMOVA performed well under all scenarios with a modest quantity of markers while STRUCTURE required a greater number, especially when populations were closely related. The popular ΔK method of determining the number of genetically distinct groups worked well when sampling was balanced, but underestimated the true number of groups with unbalanced sampling. These results provide a window through which to interpret previous work with dominant markers and we provide a protocol for determining the number of markers needed for future dominant marker studies.

摘要

所需的标记基因座数量来满足特定研究问题的要求对于显性标记尤其重要，因为它们的信息量比共显性标记系统低。在这项研究中，我们使用模拟的显性标记数据集来确定获得两个流行的群体遗传分析（STRUCTURE 和 AMOVA（分子方差分析））满意结果所需的显性标记基因座数量。数据集中的迁移、种群分化程度和不等采样等因素各不相同，以反映一系列现实研究场景。AMOVA 在所有情况下都表现良好，只需少量标记，而 STRUCTURE 需要更多标记，尤其是当种群密切相关时。当采样平衡时，流行的ΔK 方法确定遗传上不同群体的数量效果很好，但低估了不平衡采样时真实群体的数量。这些结果为解释以前使用显性标记的工作提供了一个窗口，我们还提供了一个确定未来显性标记研究所需标记数量的方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/5b464bfedccc/ece30003-3455-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/549f7b27dcdb/ece30003-3455-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/0edecd397ad9/ece30003-3455-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/898c93da450a/ece30003-3455-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/427af8da853f/ece30003-3455-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/e4022688235d/ece30003-3455-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/8bdafc0058bf/ece30003-3455-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/6e3252d89a7a/ece30003-3455-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/5b464bfedccc/ece30003-3455-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/549f7b27dcdb/ece30003-3455-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/0edecd397ad9/ece30003-3455-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/898c93da450a/ece30003-3455-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/427af8da853f/ece30003-3455-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/e4022688235d/ece30003-3455-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/8bdafc0058bf/ece30003-3455-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/6e3252d89a7a/ece30003-3455-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ee4/3797491/5b464bfedccc/ece30003-3455-f8.jpg

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