Rothstein Andrew P, McLaughlin Ryan, Acevedo-Gutiérrez Alejandro, Schwarz Dietmar
Department of Biology, Western Washington University, Bellingham, WA, 98225, USA.
Mol Ecol Resour. 2017 Mar;17(2):267-277. doi: 10.1111/1755-0998.12590. Epub 2016 Aug 29.
Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.
基于个体的数据集跟踪生物在空间和时间上的分布，对于回答生态学和进化领域的广泛问题至关重要。“永久性”基因标签避免了对动物进行侵入性标记或 tagging 的需要，特别是当个体之间几乎没有表型差异时。然而，对个体进行基因跟踪也有其局限性；正确匹配基因型以及与实验室工作相关的错误率，可能会使识别匹配个体变得困难。此外，确定一种能有效匹配野外个体的抽样设计，对研究人员来说可能是一项挑战。在这里，我们通过一个基于 Python 的高效计算机建模程序 wisepair，将定义抽样设计和减少基因分型错误这两个目标结合起来。我们描述了用于开发该计算机程序的方法，并通过三个实证数据集评估其有效性，这些数据集有参考基因型和无参考基因型。我们的结果表明，wisepair 在使用先前发表的日行毒蛙（Allobates femoralis）参考基因型数据以及海豹（Phoca vitulina）和欧亚水獭（Lutra lutra）的无参考（粪便）基因型样本数据集时，其表现优于类似的基因型匹配程序。此外，由于海豹数据的抽样工作量有限，我们为未来项目提出了最优抽样设计。wisepair 在现有方法中做出的牺牲最小，因为它纳入了样本重新运行错误数据、等位基因成对比较和概率模拟来确定匹配阈值。我们的程序是研究人员可用于为基因跟踪研究事先定义参数的唯一工具。
