Data from: Applications of random forest feature selection for fine-scale genetic population assignment
- Description:
- Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each dataset, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic datasets to improve assignment for management and conservation of exploited populations.
Usage Notes:AtlanticSalmon_93KGenepop
Genepop file containing 93,703 SNPs from Labrador Atlantic salmon, filtered from original 220K SNP chip.format_dataframe_for_RF
R script to convert data in adjusted PLINK format to format suitable for RF analyses. Example run of RF included in script. - , Bentzen, Paul, , , , , , and
- Source Repository:
- Dryad
- Publisher(s):
- Dryad
- Access:
- Public
- License:
- https://creativecommons.org/publicdomain/zero/1.0/
- URL:
- http://datadryad.org/stash/dataset/doi:10.5061/dryad.93h33
- Publication date:
- 2017-07-27
- Keywords:
- Identifier:
- https://doi.org/10.5061/dryad.93h33
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