Deciphering the genetic basis and genomic prediction of heat tolerance trait from whole-genome resequencing in spotted sea bass (Lateolabrax maculatus)

Spotted sea bass (Lateolabrax maculatus), widely farming in the China's coastal area, is a valuable fish species for aquaculture. Nevertheless, fluctuations in water temperature, particularly the significant increases during summer, threaten the survival and productivity of this species. Enhancing the heat tolerance of spotted sea bass is critical for ensuring the sustainable development of its aquaculture. In the present study, we undertook a genome-wide association study (GWAS) to explore the genetic basis underpinnings of heat tolerance in spotted sea bass. As shown in the results, 50 significantly associated genetic variants (31 SNPs and 19 InDels) were detected, distributed across multiple chromosomes, indicating that heat tolerance trait of spotted sea bass is governed by micro-effective multigene. Additionally, 236 candidate genes were also annotated, and hspa5, mrpl13 and ndufs8a were identified as hub genes by PPI analysis. GO and pathway enrichment analyses indicated that membrane category and membrane trafficking pathway play a major role in the heat stress response. Furthermore, comparative evaluation of ten various genomic selection (GS) models and two selection strategies (GWAS-p and Random) revealed that the rrBLUP and SVM are optimal GS models for SNP and InDel markers, respectively. Overall, our findings deepen the knowledge of the molecular mechanisms of heat tolerance and highlights the potential of GS to boost heat tolerance performance in spotted sea bass and related species. •31 SNPs and 19 InDels associated with heat tolerance traits were identified by GWAS.•236 candidate genes were considered to be related to heat tolerance traits.•Membrane trafficking pathway may play key roles for thermal response•Using GWAS-informative SNPs or InDels can significantly improve the predictive accuracy of breeding models.•rrBLUP and SVM were determined as the optimal models for SNP and InDel markers, respectively.