Data from: Graphics for relatedness research

Studies of relatedness have been crucial in molecular ecology over the last decades. Good evidence of this is the fact that studies of population structure, evolution of social behaviours, genetic diversity and quantitative genetics all involve relatedness research. The main aim of this article is to review the most common graphical methods used in allele sharing studies for detecting and identifying family relationships. Both IBS and IBD based allele sharing studies are considered. Furthermore, we propose two additional graphical methods from the field of compositional data analysis: the ternary diagram and scatterplots of isometric log-ratios of IBS and IBD probabilities. We illustrate all graphical tools with genetic data from the HGDP-CEPH diversity panel, using mainly 377 microsatellites genotyped for 25 individuals from the Maya population of this panel. We enhance all graphics with convex hulls obtained by simulation and use these to confirm the documented relationships. The proposed compositional graphics are shown to be useful in relatedness research, as they also single out the most prominent related pairs. The ternary diagram is advocated for its ability to display all three allele sharing probabilities simultaneously. The log-ratio plots are advocated as an attempt to overcome the problems with the Euclidean distance interpretation in the classical graphics.

近数十年来，亲缘关系研究在分子生态学领域始终占据至关重要的地位。有力佐证之一便是：种群结构研究、社会行为演化研究、遗传多样性研究以及数量遗传学研究，均需涉及亲缘关系分析。本文的核心目标是梳理当前用于等位基因共享研究、以检测和鉴定家族亲缘关系的主流可视化方法，涵盖基于相同状态（IBS, Identity by State）与相同血统（IBD, Identity by Descent）的两类等位基因共享研究。此外，本文从成分数据分析（compositional data analysis）领域提出两种新型可视化方法：三元图（ternary diagram）以及基于IBS与IBD概率的等距对数比散点图。我们采用HGDP-CEPH多样性面板（HGDP-CEPH diversity panel）的遗传数据对所有可视化工具进行演示：该数据集包含该面板中玛雅人群25名个体的基因分型数据，核心标记为377个微卫星位点。我们通过模拟生成凸包（convex hulls）对所有图表进行优化，并利用凸包验证已报道的亲缘关系。实验结果表明，本文提出的成分数据分析可视化方法在亲缘关系研究中具备良好应用价值，可精准识别出最显著的亲缘个体对。三元图因可同时展示三种等位基因共享概率的特性，被推荐为优选可视化方案；而对数比散点图则被提出用于解决传统可视化方法中欧氏距离解释存在的局限性问题。