The limitations of phenotype prediction in metabolism

Abstract

Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated 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 setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.

Polygenic prediction and gene regulation networks

“…Similar to our findings, they observed that the degree of missing heritability, i.e., the fraction of phenotypic variance explained by genetic variance [9], is proportional to the complexity of the map. In addition, we examined a framework based on genome-scale metabolic reconstructions to address the issue of predictability [36]. These models are more realistic as they encompass all known metabolic reactions and the genes encoding each enzyme for a given organism [37].…”

“…We then compute the model output for all the input values across the sample matrices A, B, and C i , obtaining three vectors of model outputs of dimension and . From these vectors we can compute the first- and total-effect indices for a given weight [24]; see also supplement in [36]. The bars in Fig.…”