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Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical
Justin H Bohling1, Jennifer R Adams, Lisette P Waits
1Department of Ecosystem Science and Management, Penn State University, University Park, PA 16802, USA. jhb24@psu.edu
Molecular Ecology
|November 21, 2012
Summary
Bayesian clustering methods for genetic admixture were tested on red wolves (Canis rufus). While structure and baps showed limitations, a specialized test accurately identified hybrids, crucial for endangered species management.
Area of Science:
- Conservation Genetics
- Population Genetics
- Molecular Ecology
Background:
- Bayesian clustering methods are widely used for genetic admixture analysis.
- Previous evaluations relied on simulations; empirical data with known ancestry were lacking.
- Red wolves (Canis rufus) hybridize with coyotes (C. latrans), providing a natural system to study admixture.
Purpose of the Study:
- To evaluate the performance of Bayesian clustering programs (baps and structure) using empirical genetic data from a red wolf population with known ancestry.
- To compare these methods against a maximum-likelihood-based test for accuracy in detecting hybridization and estimating ancestry.
- To assess the impact of training set composition and number of loci on program performance.
Main Methods:
- Utilized genetic data from a reintroduced red wolf population in North Carolina with a reconstructed pedigree.
- Employed 17 microsatellite loci to analyze individuals with 50-100% red wolf ancestry.
- Tested two Bayesian clustering programs (baps, structure) and a maximum-likelihood-based test under varying conditions.
Main Results:
- The structure program was more effective than baps at detecting admixture and estimating ancestry but more prone to misclassifying pure individuals.
- A maximum-likelihood-based test significantly outperformed both Bayesian programs, showing no misclassification errors between red wolves and hybrids.
- Both training set composition and the number of loci influenced accuracy, with varying importance depending on the program.
Conclusions:
- Empirical evaluation of genetic admixture detection methods is critical, especially for endangered species management.
- Specialized methods may offer superior accuracy compared to general Bayesian clustering for specific conservation genetics applications.
- Accurate assessment of hybridization is vital for effective conservation strategies for species like the red wolf.
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