Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations

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Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.
Publication type Article
Publication Subtype Journal Article
Title Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations
Series title PLoS ONE
DOI 10.1371/journal.pone.0098470
Volume 9
Issue 6
Year Published 2014
Language English
Publisher Public Library of Science
Publisher location San Francisco, CA
Contributing office(s) Alaska Science Center
Description 13 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title PLoS ONE
Online Only (Y/N) Y
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