The tambaqui (Colossoma macropomum) is the most important native species for aquaculture in South America. Infections by the endoparasite Neoechinorhynchus buttnerae have been reported in recent years, particularly in the North and Northeast regions of Brazil, causing significant economic losses in the production of this species. A promising alternative to address this issue is the implementation of genomic selection programs, which can promote sustainable development in tambaqui farming. Additionally, the use of low-density SNP panels, optimized through genotype imputation, emerges as a cost-effective tool for implementing genomic selection on a commercial scale. The aim of this study was to evaluate the accuracy of genomic prediction for resistance (RTct), final resilience (RSf) and total resilience (RSt) traits using data from a low-density panel (1K), compared with the imputed genotypes from a high-density SNP array (12K), and contrast these results with the traditional pedigree-based method.
Resistance (RTct), final resilience (RSf) and total resilience (RSt) traits were determined from an experimental challenge conducted with 70 full-sibling families. The challenge was successfully performed in three replicated tanks, using parasite eggs present in the ichthyoplankton as a natural infection route, which was induced through the oral filtration of plankton. Genomic prediction analyses were conducted using genotypes from a medium-density SNP array (30K Affymetrix® Axiom® SerraSNP) as reference and genotypes from a low-density SNP panel (1K) for imputation. Approximately 12K SNPs were imputed with accuracy (r²) above 0.8. The accuracies of genomic approaches using different methods and densities were compared with the pedigree-based method (PBLUP) to evaluate the benefits of integrating genomic information. Overall, the use of genomic information increased the accuracy of genomic prediction from 17.1 to 48.1% in comparison to PBLUP depending on the scenario and trait (Table 1). The effect of genotype imputation was modest most likely due to the relatively low density of imputation (from 1K to 12K.) Genomic selection, with genotype imputation to higher marker densities, emerges as a viable and cost-effective approach to increase resistance to N. buttnerae infection in tambaqui, reducing genotyping costs without compromising the accuracy of genomic prediction.
Funding: CNPq (312250/2021-5), CAPES (Code 001), FAPESP (2020/07959-5) and FAPEAM (2020/07959-5)