How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?
How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?
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DOI：
10.1534/g3.115.019323
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发表时间：
2015-06-30
期刊：
影响因子：
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通讯作者：
Palsbøll PJ
中科院分区：
文献类型：
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作者：
Kopps AM;Kang J;Sherwin WB;Palsbøll PJ
亲缘关系分析是生态学和保护遗传学研究的重要支柱，具有潜在的深远影响。有必要进行权力分析，解决一系列可能的关系。然而，这样的分析很少被应用，并且使用基于遗传数据的亲缘关系推断的研究往往忽略了内在人口特征的影响。我们调查了11个问题，关于正确的分类率的二元关系的相关性类别（相关性类别分配; RCA）使用基于个人的模型与现实的生活史参数。我们调查了遗传标记数量的影响;标记类型（微卫星、单核苷酸多态性SNP或两者）;次要等位基因频率;分型错误;交配系统;以及不同人口条件下重叠世代的数量。我们发现：（i）遗传标记数量的增加增加了RCA的正确分类率，因此可以正确分配高达>80%的第一堂兄弟;（ii）分配80%和95%正确分类所需的最小遗传标记数量在亲缘关系类别、交配系统和重叠世代数之间存在差异;（iii）通过增加额外的相关性类别和年龄及线粒体DNA数据，提高了正确分类率;（iv）如果有<800个SNP位点可用，微卫星和单核苷酸多态性数据的组合提高了正确分类率。这项研究表明，如何内在的人口特征，如交配系统和重叠世代的数量，生活史性状和遗传标记特征，可以影响正确的分类率的RCA研究。因此，种属特异性功率分析是必要的实证研究。
Kinship analyses are important pillars of ecological and conservation genetic studies with potentially far-reaching implications. There is a need for power analyses that address a range of possible relationships. Nevertheless, such analyses are rarely applied, and studies that use genetic-data-based-kinship inference often ignore the influence of intrinsic population characteristics. We investigated 11 questions regarding the correct classification rate of dyads to relatedness categories (relatedness category assignments; RCA) using an individual-based model with realistic life history parameters. We investigated the effects of the number of genetic markers; marker type (microsatellite, single nucleotide polymorphism SNP, or both); minor allele frequency; typing error; mating system; and the number of overlapping generations under different demographic conditions. We found that (i) an increasing number of genetic markers increased the correct classification rate of the RCA so that up to >80% first cousins can be correctly assigned; (ii) the minimum number of genetic markers required for assignments with 80 and 95% correct classifications differed between relatedness categories, mating systems, and the number of overlapping generations; (iii) the correct classification rate was improved by adding additional relatedness categories and age and mitochondrial DNA data; and (iv) a combination of microsatellite and single-nucleotide polymorphism data increased the correct classification rate if <800 SNP loci were available. This study shows how intrinsic population characteristics, such as mating system and the number of overlapping generations, life history traits, and genetic marker characteristics, can influence the correct classification rate of an RCA study. Therefore, species-specific power analyses are essential for empirical studies.