Abstract

Understanding how the myriad molecular impacts of mutation percolate to influence higher-order traits and ultimately fitness requires compressing a many-to-many mapping into something tractable. Decades of theoretical work suggest this may be possible because biological systems are modular: effects of perturbation are often funneled through particular pathways or subsystems rather than propagating freely through the organism. Yet few empirical systems have been able to demonstrate such modularity at scale. Prior low-dimensional models hinted that mutation–fitness relationships can collapse onto a small number of latent axes, but these studies relied on simpler datasets, leaving open whether such structure generalizes to more genetically and environmentally complex systems. Here we show that, even across 774 diverse yeast lineages, fitness variation across 12 drug environments is organized by a strikingly low-dimensional structure defined by only a few inferred phenotypic axes that capture the main patterns of variation. Consistent with these lineages having evolved under strong selection pressure, they reveal a striking asymmetry in the genotype–phenotype map: their mutations exhibit broad pleiotropy, affecting nearly all inferred phenotypic axes, yet fitness in any given drug depends on a much sparser subset of the phenotypic modules these axes reflect. This architecture aligns with central expectations of evolutionary theory. Strong selection often favors mutations with broad physiological effects, whereas long evolutionary history shapes organisms into modular systems in which only certain trait combinations matter to fitness in particular environments. By compressing many-to-many relationships, this low-dimensional framework exposes the modular fitness space that constrains the pleiotropic effects of adaptive mutants. It also lays the groundwork for identifying the key phenotypic modules that matter for fitness across different environments.