title: A framework for inferring fitness landscapes of patient-derived viruses using quasispecies theory.
abstract: Fitness is a central quantity in evolutionary models of viruses. However, it remains difficult to determine viral fitness experimentally, and existing in vitro assays can be poor predictors of in vivo fitness of viral populations within their hosts. Next-generation sequencing can nowadays provide snapshots of evolving virus populations, and these data offer new opportunities for inferring viral fitness. Using the equilibrium distribution of the quasispecies model, an established model of intrahost viral evolution, we linked fitness parameters to the composition of the virus population, which can be estimated by next-generation sequencing. For inference, we developed a Bayesian Markov chain Monte Carlo method to sample from the posterior distribution of fitness values. The sampler can overcome situations where no maximum-likelihood estimator exists, and it can adaptively learn the posterior distribution of highly correlated fitness landscapes without prior knowledge of their shape. We tested our approach on simulated data and applied it to clinical human immunodeficiency virus 1 samples to estimate their fitness landscapes in vivo. The posterior fitness distributions allowed for differentiating viral haplotypes from each other, for determining neutral haplotype networks, in which no haplotype is more or less credibly fit than any other, and for detecting epistasis in fitness landscapes. Our implemented approach, called QuasiFit, is available at http://www.cbg.ethz.ch/software/quasifit.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland Swiss Institute of Bioinformatics, Basel 4058, Switzerland.
authors.forename: David
authors.initials: D
authors.lastname: Seifert
authors.affiliation: Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland.
authors.forename: Francesca
authors.initials: F
authors.lastname: Di Giallonardo
authors.affiliation: Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland.
authors.forename: Karin J
authors.initials: KJ
authors.lastname: Metzner
authors.affiliation: Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland.
authors.forename: Huldrych F
authors.initials: HF
authors.lastname: Günthard
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland Swiss Institute of Bioinformatics, Basel 4058, Switzerland niko.beerenwinkel@bsse.ethz.ch.
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
mesh_terms: D004843:Epistasis, Genetic; D056084:Genetic Fitness; D015497:HIV-1 / Q000235:genetics*; D008957:Models, Genetic; D012984:Software
keywords: Bayesian inference; epistasis; fitness landscapes; next-generation sequencing; quasi-species