Genetic Differentiation and Selection Signatures Revealed by Two Successive Genomic Selection of Large Yellow Croaker Against Parasite .

The large yellow croaker is one of the most important marine aquaculture species in China, yet its intensive farming industry faces challenges from various pathogens, particularly white spot disease caused by Cryptocaryon irritans. This study aimed to address the issue of white spot disease through genetic breeding. We implemented two consecutive generations of genomic selection (GS) of large yellow croaker against , resulting in three continuous generations for subsequent analyses. Challenge tests demonstrated significantly higher 96-h survival rates in the selected generations compared to corresponding controls, with increases of 18.5% and 79.7%, respectively. Survival analysis confirmed that the two selected generations exhibited significantly stronger resistance to . By merging the genotype files across generations, a comprehensive dataset containing 1844 individuals and 28,637 SNPs was created. Genomic Estimated Breeding Values (GEBVs) showed steady increases across the three consecutive generations, while genetic structure analysis revealed progressive population differentiation resulting from the two rounds of GS. Through genome-wide selection signature scanning, we identified five positive selection regions (PSRs) distributed across four chromosomes. These regions were enriched for multiple biological pathways related to energy metabolism, immune response, and cell death, including the HIF-1 signaling pathway, NOD-like receptor signaling pathway, and apoptosis. Within these pathways, we identified key candidate genes, including crebbp in the HIF-1 signaling pathway and traf2 involved in immune regulation, both significantly associated with resistance to . Our results validate the effectiveness of GS in selective breeding of large yellow croaker against and demonstrate that just two consecutive generations of GS can induce substantial differentiation in genetic structure. This approach facilitates the identification of candidate genes and biological pathways associated with disease resistance.