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Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo.

Wu CH, Drummond AJ - Genetics (2011)

Bottom Line: With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty.Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model.Finally we apply our method to a red colobus monkey data set as an example.

View Article: PubMed Central - PubMed

Affiliation: University of Auckland, Auckland 1001, New Zealand.

ABSTRACT
We provide a framework for Bayesian coalescent inference from microsatellite data that enables inference of population history parameters averaged over microsatellite mutation models. To achieve this we first implemented a rich family of microsatellite mutation models and related components in the software package BEAST. BEAST is a powerful tool that performs Bayesian MCMC analysis on molecular data to make coalescent and evolutionary inferences. Our implementation permits the application of existing nonparametric methods to microsatellite data. The implemented microsatellite models are based on the replication slippage mechanism and focus on three properties of microsatellite mutation: length dependency of mutation rate, mutational bias toward expansion or contraction, and number of repeat units changed in a single mutation event. We develop a new model that facilitates microsatellite model averaging and Bayesian model selection by transdimensional MCMC. With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty. Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model. Finally we apply our method to a red colobus monkey data set as an example.

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Related in: MedlinePlus

Reduced model space produced by the restrictions described in the Prior on model space section in materials and methods.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig4: Reduced model space produced by the restrictions described in the Prior on model space section in materials and methods.

Mentions: There are six free parameters in the full model, which theoretically give us 64 submodels. However, parameter p cannot be estimated for submodels in which g is not a free parameter (i.e., fixed to 0). This is because if g is fixed to 0, p does not have any effect on the likelihood. Furthermore, when modeling with regression, it is convention to estimate all polynomial terms in the model up to the largest degree considered in the model. We apply this convention to functions α(1, a1, a2, 1, a1, a2) and β(b0, b1, i) in Equation 3. The application of these restrictions to the model space results in a connected subspace of 27 models, and we apply a uniform prior so that the prior probability on each model is 1/27, while the remaining 37 models have a prior probability of 0.0. Figure A1 in appendix a shows the restricted model space.


Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo.

Wu CH, Drummond AJ - Genetics (2011)

Reduced model space produced by the restrictions described in the Prior on model space section in materials and methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3120151&req=5

fig4: Reduced model space produced by the restrictions described in the Prior on model space section in materials and methods.
Mentions: There are six free parameters in the full model, which theoretically give us 64 submodels. However, parameter p cannot be estimated for submodels in which g is not a free parameter (i.e., fixed to 0). This is because if g is fixed to 0, p does not have any effect on the likelihood. Furthermore, when modeling with regression, it is convention to estimate all polynomial terms in the model up to the largest degree considered in the model. We apply this convention to functions α(1, a1, a2, 1, a1, a2) and β(b0, b1, i) in Equation 3. The application of these restrictions to the model space results in a connected subspace of 27 models, and we apply a uniform prior so that the prior probability on each model is 1/27, while the remaining 37 models have a prior probability of 0.0. Figure A1 in appendix a shows the restricted model space.

Bottom Line: With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty.Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model.Finally we apply our method to a red colobus monkey data set as an example.

View Article: PubMed Central - PubMed

Affiliation: University of Auckland, Auckland 1001, New Zealand.

ABSTRACT
We provide a framework for Bayesian coalescent inference from microsatellite data that enables inference of population history parameters averaged over microsatellite mutation models. To achieve this we first implemented a rich family of microsatellite mutation models and related components in the software package BEAST. BEAST is a powerful tool that performs Bayesian MCMC analysis on molecular data to make coalescent and evolutionary inferences. Our implementation permits the application of existing nonparametric methods to microsatellite data. The implemented microsatellite models are based on the replication slippage mechanism and focus on three properties of microsatellite mutation: length dependency of mutation rate, mutational bias toward expansion or contraction, and number of repeat units changed in a single mutation event. We develop a new model that facilitates microsatellite model averaging and Bayesian model selection by transdimensional MCMC. With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty. Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model. Finally we apply our method to a red colobus monkey data set as an example.

Show MeSH
Related in: MedlinePlus