Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies.

ABSTRACT: Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the paramet

SUBMITTER: Palacios JA 

PROVIDER: S-EPMC4566269 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature