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Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering
Kimberly J Gilbert1, Rose L Andrew, Dan G Bock
1Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada. kgilbert@zoology.ubc.ca
Reproducing scientific results using the STRUCTURE software is challenging. Thirty percent of analyses failed to replicate population cluster findings, highlighting issues in current data archiving and reporting standards for genetic studies.
Area of Science:
- Population genetics
- Computational biology
- Bioinformatics
Background:
- Reproducibility is crucial for scientific validity, yet systematic studies are infrequent.
- Modern statistical methods, including random walk model fitting, introduce inherent stochasticity.
- Current data archiving and reporting standards may impede result reproduction.
Purpose of the Study:
- To assess the reproducibility of results from the STRUCTURE software.
- To investigate challenges in reproducing genetic cluster analyses.
- To identify areas for improvement in software usage and reporting.
Main Methods:
- Reanalysis of datasets from published papers utilizing the STRUCTURE software.
- Evaluation of the consistency of population cluster identification.
- Assessment of the impact of stochasticity in statistical procedures.
Main Results:
- Reproducing STRUCTURE results proved difficult.
- 30% of reanalyses failed to yield the same number of population clusters.
- Inconsistent results were observed despite the program's straightforward requirements.
Conclusions:
- Current practices in using and reporting STRUCTURE analyses may compromise reproducibility.
- Recommendations are provided for enhancing the reliability of STRUCTURE outputs.
- Improved data archiving and reporting are necessary for genetic studies.
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