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

• Published in: Evolutionary Applications
• Main Authors: Ian Bradbury, Jon Pearce, Marie Clément, John B. Horne, Emma V. A. Sylvester, Robert G. Beiko, Paul Bentzen
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Wiley 2017
• Subjects: Original Article; Original Articles; fisheries management; individual assignment; SNP selection; envir; geo; Atlantic salmon; Salmo salar
• Online Access: https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Feva.12524; http://onlinelibrary.wiley.com/wol1/doi/10.1111/eva.12524/fullpdf; https://doi.org/10.1111/eva.12524; https://pubmed.ncbi.nlm.nih.gov/29387152/; https://onlinelibrary.wiley.com/doi/full/10.1111/eva.12524; https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.12524; https://www.ncbi.nlm.nih.gov/pubmed/29387152; https://academic.microsoft.com/#/detail/2741011090; http://europepmc.org/articles/PMC5775496

Abstract 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 F ST ranking for selection of single nucleotide polymorphisms (SNP) for fine‐scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha).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 F ST‐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 data set, respectively, a level of accuracy never reached for these species using F ST‐selected panels. Our results demonstrate a role for machine‐learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.