An integrated framework for the inference of viral population history fromreconstructed genealogies

We describe a unified set of methods for the inference of demographic histo ry using genealogies reconstructed from gene sequence data. We introduce th e skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hy pothesis testing. Simulations are carried out to investigate the statistica l properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model es timates 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 sequenc e data and find strong evidence that subtypes A and B have different demogr aphic histories. We also provide the first (albeit tentative) genetic evide nce for a recent decrease in the growth rate of subtype B.