title: Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.
abstract: Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput "deep" sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different time points during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intrahost viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this article we develop a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different time points and missing data without extra computational difficulty. We apply our method to a data set of HIV-1, in which several hundred sequences were obtained from an infected individual at seven time points over 2 years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.
authors: Department of Statistics, University of Oxford, Oxford, United Kingdom.
authors: Kevin
authors: K
authors: Dialdestoro
authors: The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
authors: Jonas Andreas
authors: JA
authors: Sibbesen
authors: The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
authors: Lasse
authors: L
authors: Maretty
authors: Department of Zoology, University of Oxford, Oxford, United Kingdom.
authors: Jayna
authors: http://orcid.org/0000-0001-8538-4995
authors: J
authors: Raghwani
authors: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
authors: Astrid
authors: A
authors: Gall
authors: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom UCL/MRC Centre for Medical Molecular Virology, Division of Infection and Immunity, University College London, London, United Kingdom.
authors: Paul
authors: P
authors: Kellam
authors: Department of Zoology, University of Oxford, Oxford, United Kingdom.
authors: Oliver G
authors: OG
authors: Pybus
authors: Department of Statistics, University of Oxford, Oxford, United Kingdom.
authors: Jotun
authors: J
authors: Hein
authors: Department of Statistics, University of Warwick, Coventry, United Kingdom Department of Computer Science, University of Warwick, Coventry, United Kingdom p.jenkins@warwick.ac.uk.
authors: Paul A
authors: PA
authors: Jenkins
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D019143:Evolution, Molecular; D014644:Genetic Variation; D016679:Genome, Viral; D015658:HIV Infections / Q000821:virology*; D015497:HIV-1 / Q000502:physiology*; D006239:Haplotypes; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D012367:RNA, Viral; D011995:Recombination, Genetic; D012641:Selection, Genetic
chemical_list: D012367:RNA, Viral
keywords: HIV evolution; coalescent; conditional sampling distribution; importance sampling; recombination