On the study of evolutionary predictability using historical reconstruction

Abstract

Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fishers geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.