Resumen
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 We applied these methods to an unpublished SNP data set for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha).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 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 data set, 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 data sets to improve assignment for management and conservation of exploited populations.
Idioma original | English |
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Páginas (desde-hasta) | 153-165 |
Número de páginas | 13 |
Publicación | Evolutionary Applications |
Volumen | 11 |
N.º | 2 |
DOI | |
Estado | Published - feb. 1 2018 |
Nota bibliográfica
Funding Information:The authors would like to thank Judith Savoie for contributing the map images for Figure 1 and Ryan Stanley, Nick Jeffery, Alex Keddy and Michael Hall for assisting with analyses, as well as the Nunatsiavut Government, the Sivunivut Inuit Community Corporation, the Innu Nation, the Labrador Hunting and Fishing Association and local fishers for their support and active participation in this project. This work was funded by NSERC (Natural Sciences and Engineering Research Council of Canada) Strategic Grant to Paul Bentzen and Robert Beiko, and the Canada Graduate Scholarship (CGS-M), as well as NSGS (Nova Scotia Graduate Scholarship), Atlantic Salmon Conservation Foundation, Labrador Institute (Atlantic Canada Opportunities Agency and Department of Business, Tourism, Culture and Rural Development) and Olin Fellowships (Atlantic Salmon Federation).
Funding Information:
Natural Sciences and Engineering Research Council of Canada (NSERC); Nova Scotia Graduate Scholarship (NSGS); Canada Graduate Scholarship (CGS-M); Labrador Institute (Atlantic Canada Opportunities Agency and Department of Business, Tourism, Culture and Rural Development); Olin Fellowships (Atlantic Salmon Federation)
Publisher Copyright:
© 2017 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd
ASJC Scopus Subject Areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- General Agricultural and Biological Sciences