Data from: Applications of random forest feature selection for fine-scale genetic population assignment

• Main Authors: Sylvester, Emma V.A., Bentzen, Paul, Bradbury, Ian R., Clément, Marie, Pearce, Jon, Horne, John, Beiko, Robert G.
• Language: unknown
• Published: 2017
• Subjects: Life sciences; medicine and health care; Atlantic salmon; Salmo salar
• Online Access: http://nbn-resolving.org/urn:nbn:nl:ui:13-4v-kwh8; https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:98388

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 We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha).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.