ARTIFICIAL SELECTION REVEALS NEURAL REGULATORY MECHANISMS UNDERLYING SOCIAL BEHAVIOUR IN ZEBRAFISH

Social behaviour is a complex cognitive trait emerging from coordinated genetic and neural processes, however their evolutionary dynamics remain poorly understood. Here, we investigated how social behaviour evolves through genomic and brain-level mechanisms using an artificial selection experiment in zebrafish. Individuals were phenotyped using a social preference paradigm contrasting visual exposure to a mixed-sex conspecific shoal versus a non-social control stimulus, and individual preference was quantified. By the third generation, a high-sociality line emerged, displaying a significant and reproducible increase in social preference, a response that was maintained across subsequent generations. To investigate the genetic architecture underlying this rapid phenotypic adaptation, we analyzed genome-wide allele frequency dynamics using single nucleotide polymorphisms (SNPs), comparing the founder population (F0) with an evolved population after six generations (F6). Wright–Fisher simulations were used to model neutral allele frequency trajectories and isolate selection-driven changes, complemented by phenotype-association analyses identifying variants correlated with increased sociality. This integrative approach identified 170,621 SNPs, providing a highly polygenic but localized response, consistent with complex circuit-level changes. To assess how these genomic changes translate to neural mechanisms, we examined gene expression and gene networks across brain regions implicated in sensory processing and social decision-making. Artificial selection for sociality induced phenotype-specific transcriptomic changes, with enrichment of genes involved in synaptic transmission, neuronal signaling and communication. Together, these findings suggest that sociality evolves through refinement in neural regulatory networks and synaptic architecture at a systemic level, rather than by focusing on single causal driver-genes.