The genetic architecture of polygenic adaptation under a network-derived trait

The genetic architecture of adaptation varies across species, populations, and traits. While existing models capture aspects like the number of loci, effect sizes, and allele frequencies, they often overlook the molecular processes underlying trait expression. We investigated how gene regulatory networks influence quantitative variation during adaptation by examining the negative autoregulation (NAR) motif in two configurations: K+, with four evolving network components, and K-, with two components. Using forward-time simulations, we tracked populations adapting to a shifted phenotypic optimum under varying genetic architectures. We found that K+ populations maintained rapid adaptation despite low recombination, preserving high genetic variance through positive epistasis and stronger linkage disequilibrium. Under low recombination, K+ populations reached the optimum through diverse molecular configurations, while responses were more uniform under high recombination. In contrast, K- and additive models showed impaired adaptation under low recombination. Our findings demonstrate that network structure fundamentally influences the distribution of variation within the molecular architecture of traits, with certain networks providing unexpected robustness against low recombination rates. This suggests that molecular complexity may confer evolutionary advantages in natural populations.