Open AccessApplications of random forest feature selection for fine‐scale genetic population assignment
Author(s) -
Sylvester Emma V. A.,
Bentzen Paul,
Bradbury Ian R.,
Clément Marie,
Pearce Jon,
Horne John,
Beiko Robert G.
Publication year - 2018
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.12524
Subject(s) - random forest , selection (genetic algorithm) , biology , population , oncorhynchus , salmo , snp , effective population size , statistics , artificial intelligence , single nucleotide polymorphism , computer science , mathematics , genetics , genetic variation , fishery , fish <actinopterygii> , genotype , demography , sociology , gene
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 We applied these methods to an unpublished SNP data set for Atlantic salmon ( Salmo salar ) and a published SNP data set for Alaskan Chinook salmon ( Oncorhynchus tshawytscha ).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 SNP s 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 SNP s 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.