Genome-wide association study and genomic selection reveal the genetic architecture of glycogen content in autotetraploid Crassostrea gigas

Autotetraploid Crassostrea gigas is an economically important marine mollusk that has attracted considerable attention because of its ability to stably produce triploid C. gigas . Glycogen content is a key nutritional trait affecting the flavor and quality of C. gigas , and improving this trait has become an important breeding and commercial objective. In this study, we conducted genome-wide association study (GWAS) and genomic selection (GS) analyses of glycogen content in autotetraploid C. gigas for the first time. A total of four significant SNPs and 18 suggestive SNPs were identified, and the candidate genes were mainly involved in biological processes related to glycogen metabolism, including signal transduction, glucose transport, and protein phosphorylation. The effects of different SNP densities and marker selection strategies on prediction accuracy were systematically evaluated using six prediction models. The results indicated that a panel of 5 K SNPs was sufficient to achieve relatively high predictive accuracy for glycogen content within the present study population and across the evaluated population groups, and that incorporating significant SNPs further improved prediction accuracy. Notably, a panel of 1 K SNPs that incorporated significant SNPs and was evenly distributed across chromosomes showed relatively high predictive performance. In addition, RKHS showed a favorable balance between prediction accuracy and computational efficiency and may be an optimal prediction model for glycogen content in autotetraploid C. gigas . However, the prediction stability of the GWAS-GS strategy may be influenced by population genetic structure and requires validation in additional independent populations. This study lays a foundation for a deeper understanding of the genetic and metabolic mechanisms underlying glycogen content in autotetraploid C. gigas and provides theoretical support and empirical evidence for the development of low-density SNP arrays and the implementation of efficient and cost-effective breeding programs for autotetraploid C. gigas with high glycogen content.