Development of near-infrared reflectance spectroscopy (NIRS) model and genome-wide association study for glycogen and protein content in Pacific abalone

Abstract

Quality traits have recently become the key objective in aquaculture selection breeding recently. The Pacific abalone (Haliotis discus hannai) is the most widely farmed abalone species in China. In this study, we investigated the potential of the near-infrared reflectance spectroscopy (NIRS) model to predict the glycogen and protein content of Pacific abalone. Additionally, we performed a genome-wide association study (GWAS) analysis to identify related SNPs and candidate genes for these two quality traits. Our results showed that the prediction accuracy of the NIRS model was 0.9929 and 0.9808 for glycogen and protein content, respectively. Moreover, the external cross-validation showed an accuracy above 0.95, indicating the reliability and robustness of the models. A total of 274 individuals were predicted by the NIRS model and genotyped with 69,530 high quality SNPs. Fourteen and fifteen genes were identified for glycogen and protein content, respectively, including slit2, fgfr2, gria4, hnrnpm, and megf11. These genes may affect glycogen and protein traits through biological processes such as energy metabolism, transmembrane transport, immune regulation, and signal transduction. This study not only constructed a rapid and reliable NIRS prediction model, but also helped us better understand the genetics of glycogen and protein content of Pacific abalone, which will improve the quality of Pacific abalone.