The limitations of phenotype prediction in metabolism

Phenotype prediction is at the core of many questions in biology. Prediction is frequently attained by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes causing the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We compute a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular environment and its evolutionary history, and is transportable to infer the phenotype across a range of environments. We also find that there exists an optimal genetic variation for predictability and demonstrate how the linear PGS can yet explain phenotypes generated by the underlying nonlinear biochemistry. Thus, the explicit model interprets the black-box statistical associations of the genotype-to-phenotype map and helps uncover what limits prediction in metabolism.