Department of Mathematics and Computing Science, Saint Mary's University, 923 Robie Street, Halifax, NS, Canada, B3H 3C3.
Mol Ecol Resour. 2015 May;15(3):557-61. doi: 10.1111/1755-0998.12323. Epub 2014 Sep 20.
Analyses of pairwise relatedness represent a key component to addressing many topics in biology. However, such analyses have been limited because most available programs provide a means to estimate relatedness based on only a single estimator, making comparison across estimators difficult. Second, all programs to date have been platform specific, working only on a specific operating system. This has the undesirable outcome of making choice of relatedness estimator limited by operating system preference, rather than being based on scientific rationale. Here, we present a new R package, called related, that can calculate relatedness based on seven estimators, can account for genotyping errors, missing data and inbreeding, and can estimate 95% confidence intervals. Moreover, simulation functions are provided that allow for easy comparison of the performance of different estimators and for analyses of how much resolution to expect from a given data set. Because this package works in R, it is platform independent. Combined, this functionality should allow for more appropriate analyses and interpretation of pairwise relatedness and will also allow for the integration of relatedness data into larger R workflows.
成对相关关系分析是解决生物学许多问题的关键组成部分。然而，此类分析受到限制，因为大多数可用的程序仅提供了一种基于单个估计器来估计相关关系的方法，这使得在估计器之间进行比较变得困难。其次，迄今为止所有的程序都是特定于平台的，仅在特定的操作系统上运行。这带来了一个不理想的结果，即相关关系估计器的选择受到操作系统偏好的限制，而不是基于科学原理。在这里，我们提出了一个新的 R 包，称为 related，它可以基于七个估计器计算相关关系，可以考虑基因分型错误、缺失数据和近交，并且可以估计 95%置信区间。此外，还提供了模拟功能，允许轻松比较不同估计器的性能，并分析从给定数据集获得的分辨率。由于这个包在 R 中运行，因此它是与平台无关的。综合起来，这些功能应该允许对成对相关关系进行更适当的分析和解释，并且还可以将相关关系数据集成到更大的 R 工作流程中。
