Oh Brother, Where Art Thou? A Bayes Factor Test for Recombination with Uncertain Heritage

Authors: Suchard M.A.1; Weiss R.E.2; Dorman K.S.3; Sinsheimer J.S.1

Source: Systematic Biology, Volume 51, Number 5, 1 September 2002 , pp. 715-728(14)

Publisher: Taylor and Francis Ltd

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Abstract:

Current methods to identify recombination between subtypes of human immunodeficiency virus 1 (HIV-1) fall into a sequential testing trap, in which significance is assessed conditional on parental representative sequences and crossover points (COPs) that maximize the same test statistic. We overcame this shortfall by testing for recombination while inferring parental heritage and COPs using an extended Bayesian multiple change-point model. The model assumes that aligned molecular sequence data consist of an unknown number of contiguous segments that may support alternative topologies or varying evolutionary pressures. We allowed for heterogeneity in the substitution process and specifically tested for intersubtype recombination using Bayes factors. We also developed a new class of priors to assess significance across a wide range of support for recombination in the data. We applied our method to three putative gag gene recombinants. HIV-1 isolate RW024 decisively supported recombination with an inferred parental heritage of AD and a COP 95% Bayesian credible interval of (1152, 1178) using the HXB2 numbering scheme. HIV-1 isolate VI557 barely supported recombination. HIV-1 isolate RF decisively rejected recombination as expected, given that the sequence is commonly used as a reference sequence for subtype B. We employed scaled regeneration quantile plots to assess convergence and found this approach convenient to use even for our variable dimensional model parameter space.

Keywords: BAYES FACTOR; MCMC; MULTIPLE CHANGE-POINT MODEL; PHYLOGENETICS

Document Type: Research article

Affiliations: 1: Department of Biomathematics, School of Medicine, University of California, Los Angeles, California 90095-1766, USA 2: Department of Biostatistics, School of Public Health, University of California, Los Angeles, California 90095-1772, USA 3: Department of Statistics, Iowa State University, Ames, Iowa 50011, USA

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