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Inferring population histories using genome-wide allele frequency data
Mathieu Gautier1, Renaud Vitalis
1INRA, UMR CBGP (INRA - IRD - Cirad - Montpellier SupAgro), Campus international de Baillarguet, Montferrier-sur-Lez, France. Mathieu.Gautier@supagro.inra.fr
Molecular Biology and Evolution
|November 17, 2012
Summary
This study introduces the Kimura model, a new Bayesian method using single-nucleotide polymorphism (SNP) data to accurately estimate population divergence times and demographic history. The model proves robust to gene flow and bias, aiding population genetics research.
Area of Science:
- Population Genetics
- Genomics
- Computational Biology
Background:
- High-throughput genotyping technologies generate vast population genetic data.
- These data hold significant information about demographic history.
- Estimating divergence times from large datasets remains challenging.
Purpose of the Study:
- Introduce a novel method for estimating divergence times from large single-nucleotide polymorphism (SNP) datasets.
- Develop a hierarchical Bayesian model based on genetic drift approximations.
- Assess the model's accuracy and robustness in inferring population history.
Main Methods:
- Utilized a hierarchical Bayesian model based on Kimura's diffusion approximation.
- Implemented a Metropolis-Hastings within Gibbs sampler for parameter estimation.
- Applied the deviance information criterion (DIC) for model selection and topology assessment.
Main Results:
- The Kimura model provides accurate estimates of divergence times on simulated data.
- The deviance information criterion (DIC) effectively identifies correct population tree topologies.
- The method demonstrates robustness to low-to-moderate gene flow and ascertainment bias.
Conclusions:
- The Kimura model offers a powerful tool for characterizing demographic history using genome-wide allele frequency data.
- This approach is applicable to both model and nonmodel species.
- The method was successfully illustrated using human population genetic data.
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