首页> 外文期刊>The New Phytologist >Conceptual analysis of methods applied to assessment of diversity within and distance between populations with asexual or mixed mode of reproduction.
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Conceptual analysis of methods applied to assessment of diversity within and distance between populations with asexual or mixed mode of reproduction.

机译:对用于评估无性繁殖或混合繁殖方式的种群内和种群间多样性的方法的概念分析。

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摘要

Measures of diversity within populations, and distance between populations, are compared for organisms with an asexual or mixed mode of reproduction. Examples are drawn from studies of plant pathogenic fungi based on binary traits including presence/absence of DNA bands or virulence/avirulence to differential hosts. Commonly used measures of population diversity or genetic distance consider either genotype frequencies or allele frequencies. Kosman's diversity and distance measures are the most suitable for populations with an asexual or mixed mode of reproduction, because by considering genetic patterns of all individuals they take into account not just the genotype frequencies but also the genetic similarities between genotypes in the populations. The Kosman distance and diversity measures for populations can be calculated using different measures of dissimilarity between individuals (the simple mismatch, Jaccard and Dice coefficients of dissimilarity). Kosman's distances based on the simple mismatch and Jaccard dissimilarities are metrics. Comparisons of diversity indices for hypothetical examples as well as for actual data sets are presented to demonstrate that inferences from diversity analysis of populations can be driven by techniques of diversity and distance assessments and not only data driven..
机译:比较具有无性繁殖或混合繁殖方式的生物的种群内部多样性度量以及种群之间距离。实例是基于二元性状从植物致病真菌的研究中得出的,这些二元性状包括存在/不存在DNA条带或对分化宿主的致病性/毒力。常用的种群多样性或遗传距离测量方法考虑基因型频率或等位基因频率。科斯曼的多样性和距离测度最适合无性繁殖或混合繁殖方式的人群,因为考虑到所有个体的遗传模式,他们不仅考虑了基因型频率,还考虑了人口中基因型之间的遗传相似性。可以使用不同的个体间差异性度量(简单的不匹配,Jaccard和Dice差异性系数)来计算人口的Kosman距离和多样性度量。基于简单失配和Jaccard差异的科斯曼距离是度量标准。提出了假设示例和实际数据集的多样性指数的比较,以证明来自人口多样性分析的推论可以通过多样性和距离评估技术来驱动,而不仅仅是数据驱动。

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