related: an R package for analysing pairwise relatedness from codominant molecular markers |
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| Authors: | Jack Pew Paul H. Muir Jinliang Wang Timothy R. Frasier |
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| Affiliation: | 1. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, NS, Canada;2. Institute of Zoology, Zoological Society of London, London, UK;3. Department of Biology, Saint Mary's University, Halifax, NS, Canada |
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| Abstract: | ![]()
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. |
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| Keywords: | inbreeding kinship microsatellite R relatedness simulation
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