Gain-loss-duplication models on a phylogeny: exact algorithms for computing the likelihood and its gradient

Abstract: Gene gain-loss-duplication models are commonly based on continuous-time birth-death processes. Employed in a phylogenetic context, such models have been increasingly popular in studies of gene content evolution across multiple genomes. While the applications are becoming more varied and demanding, bioinformatics methods for probabilistic inference on copy numbers (or integer-valued evolutionary characters, in general) are scarce. We describe a flexible probabilistic framework for phylogenetic gene-loss-duplication models. The framework is based on a novel elementary representation by dependent random variables with well-characterized conditional distributions: binomial, P\'olya (negative binomial), and Poisson. The corresponding graphical model yields exact numerical procedures for computing the likelihood and the posterior distribution of ancestral copy numbers. The resulting algorithms take quadratic time in the total number of copies. In addition, we show how the likelihood gradient can be computed by a linear-time algorithm.