Tag Archives: phylogenetics

Estimating the Date of Origin of An HIV-1 Circulating Recombinant Form

Virology. 2009 Apr 25;387(1):229-34. Epub 2009 Mar 9.
Tee KK, Pybus OG, Parker J, Ng KP, Kamarulzaman A, Takebe Y.

HIV is capable of frequent genetic exchange through recombination. Despite the pandemic spread of HIV-1 recombinants, their times of origin are not well understood. We investigate the epidemic history of a HIV-1 circulating recombinant form (CRF) by estimating the time of the recombination event that lead to the emergence of CRF33_01B, a recently described recombinant descended from CRF01_AE and subtype B. The gag, pol and env genes were analyzed using a combined coalescent and relaxed molecular clock model, implemented in a Bayesian Markov chain Monte Carlo framework. Using linked genealogical trees we calculated the time interval between the common ancestor of CRF33_01B and the ancestors it shares with closely related parental lineages. The recombination event that generated CRF33_01B (t(rec)) occurred sometime between 1991 and 1993, suggesting that recombination is common in the early evolutionary history of HIV-1. The proof-of-concept approach provides a new tool for the investigation of HIV molecular epidemiology and evolution.

Correlating Viral Phenotypes With Phylogeny: Accounting for Phylogenetic Uncertainty

Infect Genet Evol. 2008 May;8(3):239-46. Epub 2007 Aug 21.
Parker J, Rambaut A, Pybus OG.

Many recent studies have sought to quantify the degree to which viral phenotypic characters (such as epidemiological risk group, geographic location, cell tropism, drug resistance state, etc.) are correlated with shared ancestry, as represented by a viral phylogenetic tree. Here, we present a new Bayesian Markov-Chain Monte Carlo approach to the investigation of such phylogeny-trait correlations. This method accounts for uncertainty arising from phylogenetic error and provides a statistical significance test of the null hypothesis that traits are associated randomly with phylogeny tips. We perform extensive simulations to explore and compare the behaviour of three statistics of phylogeny-trait correlation. Finally, we re-analyse two existing published data sets as case studies. Our framework aims to provide an improvement over existing methods for this problem.