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We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.


Journal article



Publication Date





1429 - 1437


Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom.


Humans, HIV-1, HIV Infections, Likelihood Functions, Logistic Models, Statistical Distributions, Predictive Value of Tests, Pedigree, Genetics, Population, Disease Outbreaks, Phylogeny, Genes, env, Genes, gag, Models, Genetic, Computer Simulation, Africa South of the Sahara, Europe, Genetic Variation