Reliably discriminating stock structure with genetic markers: Mixture models with robust and fast computation

Delineating naturally occurring and self‐sustaining subpopulations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borro...

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Published in:Molecular ecology resources Vol. 18; no. 6; pp. 1310 - 1325
Main Authors: Foster, Scott D., Feutry, Pierre, Grewe, Peter M., Berry, Oliver, Hui, Francis K. C., Davies, Campbell R.
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 01.11.2018
Subjects:
Admixtures
algorithms
analytical methods
ancestry
Animals
Breeding
Computational Biology - methods
computational methodology
Computer applications
Computer Simulation
Data processing
Genetic Markers
Genetics
Genotyping Techniques - methods
human genetics
humans
Livestock - classification
Livestock - genetics
Natural resource management
Natural resources
Parameter uncertainty
Resource management
Robustness (mathematics)
Subpopulations
Thunnus albacares
Tuna - classification
Tuna - genetics
uncertainty
ISSN:1755-098X, 1755-0998, 1755-0998
Online Access:Get full text
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Summary:Delineating naturally occurring and self‐sustaining subpopulations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borrowed from superficially similar analytical tasks in human genetics even though models specifically for stock identification have been previously developed. Unfortunately, the analytical tasks in resource management and human genetics are not identical—questions about humans are typically aimed at inferring ancestry (often referred to as “admixture”) rather than breeding stocks. In this article, we argue, and show through simulation experiments and an analysis of yellowfin tuna data, that ancestral analysis methods are not always appropriate for stock delineation. In this work, we advocate a variant of a previously introduced and simpler model that identifies stocks directly. We also highlight that the computational aspects of the analysis, irrespective of the model, are difficult. We introduce some alternative computational methods and quantitatively compare these methods to each other and to established methods. We also present a method for quantifying uncertainty in model parameters and in assignment probabilities. In doing so, we demonstrate that point estimates can be misleading. One of the computational strategies presented here, based on an expectation–maximization algorithm with judiciously chosen starting values, is robust and has a modest computational cost.
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ISSN:1755-098X
1755-0998
1755-0998
DOI:10.1111/1755-0998.12920