Genetics, Vol. 160, 1283-1293, April 2002, Copyright © 2002

Estimation of Effective Population Size of HIV-1 Within a Host: A Pseudomaximum-Likelihood Approach

Tae-Kun Seoa,b, Jeffrey L. Thornec, Masami Hasegawaa,b, and Hirohisa Kishinod
a Department of Biosystems Science, The Graduate University for Advanced Studies, Hayama, Kanagawa, 240-0193, Japan,
b The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu Minato-ku, Tokyo 106-8569, Japan,
c Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695-7566
d Laboratory of Biometrics, Graduate School of Agriculture and Life Sciences, University of Tokyo, Yayoi 1-1-1 Bunkyo-ku, Tokyo 113-8657, Japan

Corresponding author: Tae-Kun Seo, Box 7566, North Carolina State University, Raleigh, NC 27695-7566., seo{at}statgen.ncsu.edu (E-mail)

Communicating editor: G. B. GOLDING

Using pseudomaximum-likelihood approaches to phylogenetic inference and coalescent theory, we develop a computationally tractable method of estimating effective population size from serially sampled viral data. We show that the variance of the maximum-likelihood estimator of effective population size depends on the serial sampling design only because internal node times on a coalescent genealogy can be better estimated with some designs than with others. Given the internal node times and the number of sequences sampled, the variance of the maximum-likelihood estimator is independent of the serial sampling design. We then estimate the effective size of the HIV-1 population within nine hosts. If we assume that the mutation rate is 2.5 x 10-5 substitutions/generation and is the same in all patients, estimated generation lengths vary from 0.73 to 2.43 days/generation and the mean (1.47) is similar to the generation lengths estimated by other researchers. If we assume that generation length is 1.47 days and is the same in all patients, mutation rate estimates vary from 1.52 x 10-5 to 5.02 x 10-5. Our results indicate that effective viral population size and evolutionary rate per year are negatively correlated among HIV-1 patients.





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