Predicting evolution from the shape of genealogical trees

Preprint
Publisher Website
Google Scholar
Abstract
Given a sample of genome sequences from an asexual population, can one predict its evolutionary future? Here we demonstrate that the branching pattern of reconstructed genealogical trees contains information about the relative fitness of the sampled sequences and that this information can be used to infer the closest extant relative of future populations. Our approach is based on the assumption that evolution proceeds predominantly by accumulation of small effect mutations and does not require any species specific input. Hence, the resulting inference algorithm 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 makes informative predictions in 16 out of 18 years. Beyond providing a practical tool for prediction, our results suggest that continuous adaptation by small effect mutations is a major component of influenza virus evolution.

This publication has 0 references indexed in Scilit: