An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data.

ABSTRACT: Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. Our method CLUES treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency t

SUBMITTER: Stern AJ 

PROVIDER: S-EPMC6760815 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature