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MBE Advance Access originally published online on February 1, 2006
Molecular Biology and Evolution 2006 23(4):817-827; doi:10.1093/molbev/msj102
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Research Article

Recombination Estimation Under Complex Evolutionary Models with the Coalescent Composite-Likelihood Method

Antonio Carvajal-Rodríguez*,{dagger}, Keith A. Crandall* and David Posada{dagger}

* Department of Microbiology and Molecular Biology, Brigham Young University, Provo, Utah; and {dagger} Departamento de Bioquímica, Genética e Inmunología, Universidad de Vigo, Vigo, Spain

E-mail: ac549{at}email.byu.edu.

The composite-likelihood estimator (CLE) of the population recombination rate considers only sites with exactly two alleles under a finite-sites mutation model (McVean, G. A. T., P. Awadalla, and P. Fearnhead. 2002. A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160:1231–1241). While in such a model the identity of alleles is not considered, the CLE has been shown to be robust to minor misspecification of the underlying mutational model. However, there are many situations where the putative mutation and demographic history can be quite complex. One good example is rapidly evolving pathogens, like HIV-1. First we evaluated the performance of the CLE and the likelihood permutation test (LPT) under more complex, realistic models, including a general time reversible (GTR) substitution model, rate heterogeneity among sites ({Gamma}), positive selection, population growth, population structure, and noncontemporaneous sampling. Second, we relaxed some of the assumptions of the CLE allowing for a four-allele, GTR+{Gamma} model in an attempt to use the data more efficiently. Through simulations and the analysis of real data, we concluded that the CLE is robust to severe misspecifications of the substitution model, but underestimates the recombination rate in the presence of exponential growth, population mixture, selection, or noncontemporaneous sampling. In such cases, the use of more complex models slightly increases performance in some occasions, especially in the case of the LPT. Thus, our results provide for a more robust application of the estimation of recombination rates.

Key Words: recombination • complex models • coalescent • composite likelihood • HIV-1


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