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Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure
Authors:Kimberly J. Gilbert  Rose L. Andrew  Dan G. Bock  Michelle T. Franklin  Nolan C. Kane  Jean‐Sébastien Moore  Brook T. Moyers  Sébastien Renaut  Diana J. Rennison  Thor Veen  Timothy H. Vines
Affiliation:1. Biodiversity Research Centre, University of British Columbia, , Vancouver, BC, Canada, V6T 1Z4;2. Department of Zoology, University of British Columbia, , Vancouver, BC, Canada, V6T 1Z4;3. Department of Botany, University of British Columbia, , Vancouver, BC, Canada, V6T 1Z4;4. Department of Biological Sciences, Simon Fraser University, , Burnaby, BC, Canada, V5A 1S6;5. Department of Ecology and Evolutionary Biology, University of Colorado at Boulder, , Boulder, CO, 80309 USA;6. Molecular Ecology Editorial Office, , Vancouver, BC, Canada, V6T 1Z4
Abstract:Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of ‘random walk’ model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package structure to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting structure analyses and results in published works.
Keywords:population clustering  population genetics  reproducibility     structure   
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