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Bayesian analysis of an admixture model with mutations and arbitrarily linked markers
Laurent Excoffier1, Arnaud Estoup, Jean-Marie Cornuet
1Institut National de la Recherche Agronomique, Centre de Biologie et de Gestion des Populations (CBGP), Campus International de Baillarguet, Montferrier-sur-Lez, France. laurent.excoffier@zoo.unibe.ch
Genetics
|January 18, 2005
Summary
We present a Bayesian analysis using approximate Bayesian computation (ABC) for admixture modeling. This method accurately estimates admixture proportions, especially for ancient events, outperforming maximum-likelihood approaches.
Area of Science:
- Population Genetics
- Computational Biology
- Statistical Genetics
Background:
- Admixture models are crucial for understanding population history and genetic diversity.
- Estimating admixture parameters, including admixture proportions, divergence times, and population sizes, presents statistical challenges.
- Existing methods like maximum likelihood (ML) have limitations in accuracy and parameter estimation.
Purpose of the Study:
- To introduce a Bayesian analysis framework for classical admixture models using approximate Bayesian computation (ABC).
- To simultaneously estimate all parameters within the admixture model.
- To compare the performance of the ABC approach against a maximum-likelihood (ML) method.
Main Methods:
- Utilized a Bayesian analysis within the approximate Bayesian computation (ABC) framework.
- Employed massive simulations and a rejection-regression algorithm for parameter estimation.
- Incorporated complex mutation models and partially linked loci into the analysis.
Main Results:
- The ABC approach provides accurate admixture proportion estimates, particularly for ancient admixture events, surpassing ML methods.
- Parameters such as divergence time, admixture time, and population sizes were well-estimated by the ABC method, unlike ML.
- Partially linked markers did not bias admixture estimation, though ML confidence intervals were inaccurate without accounting for linkage.
Conclusions:
- The developed ABC method offers a robust and accurate approach for analyzing complex admixture scenarios.
- This Bayesian framework provides superior estimation of admixture parameters compared to traditional ML methods, especially for deep evolutionary timescales.
- Application to a bee population suggests recent admixture (10-40 generations) and ancient separation of parental lineages since the last glacial maximum.