Predicting evolution from the shape of genealogical trees.
Given a sample of genome sequences from an asexual population, can one predict its evolutionary future? Here we demonstrate that the branching patterns of reconstructed genealogical trees contains information about the relative fitness of the sampled sequences and that this information can be used to predict successful strains. Our approach is based on the assumption that evolution proceeds by accumulation of small effect mutations, does not require species specific input and can be applied to any asexual population under persistent selection pressure. We demonstrate its performance using historical data on seasonal influenza A/H3N2 virus. We predict the progenitor lineage of the upcoming influenza season with near optimal performance in 30% of cases and make informative predictions in 16 out of 19 years. Beyond providing a tool for prediction, our ability to make informative predictions implies persistent fitness variation among circulating influenza A/H3N2 viruses.
Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Tübingen, Germany.
Richard A
http://orcid.org/0000-0003-2525-1407
RA
Neher
Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Colin A
CA
Russell
Kavli Institute for Theoretical Physics, University of California, Santa Barbara, Santa Barbara, United States.
Boris I
BI
Shraiman
D000465:Algorithms; D001483:Base Sequence; D019143:Evolution, Molecular; D005544:Forecasting; D051456:Gene Flow; D056084:Genetic Fitness; D014644:Genetic Variation; D006801:Humans; D053122:Influenza A Virus, H3N2 Subtype / Q000145:classification / Q000235:genetics*; D007251:Influenza, Human / Q000453:epidemiology / Q000821:virology; D040582:Inheritance Patterns; D008957:Models, Genetic; D010802:Phylogeny; D012621:Seasons; D017422:Sequence Analysis, DNA
adaptive evolution; evolutionary biology; genomics; population genetics; vaccine strain selection; viruses