Genotype-fitness mapping of adaptive mutants reveals shifting low-dimensional structure across divergent environments.

A central goal in evolutionary biology is to predict the effect of a genetic mutation on fitness. This is a major challenge because it requires knowledge of both the phenotypic effects of a mutation and their importance in an arbitrary environment, which are high-dimensional quantities and difficult to guess a priori. Here, we address this problem by taking a top-down, data-driven approach to infer the mapping between genotypes, latent phenotypes, and fitness. We measure the fitness effects of a large collection of adaptive yeast mutants in many lab environments, from which we build low-dimensional, linear fitness landscapes. We find that these models are highly predictive of fitness variation for thousands of adaptive mutants, both in environments similar to where they evolved and also in divergent environments. This implies that the underlying genotype-phenotype-fitness maps for these adaptive mutants tend to be broadly low-dimensional. We further demonstrate that these maps only partially overlap across divergent environments, suggesting that the phenotypic determinants of fitness shift with the environment but remain low-dimensional. These results combine to emphasize the importance of environmental context in evolution, and suggest that top-down, low-dimensional fitness landscapes pave the way for evolutionary prediction.