PSXII-4 - Genomic improvement of disease resistance using two breeding strategies in a population of Penaeus vannamei (Pacific white shrimp): A simulation study

Abstract: This study aimed to investigate the impact of SNP density chip and genotyping strategies on the accuracy of genomic estimated breeding values (EBV) in a simulated population mimicking a commercial Pacific white shrimp (Penaeus vannamei) breeding program. The chromosome number and length mimicked the P. vannamei genome. To create an initial linkage disequilibrium (LD), a historical population of 5000 animals was randomly bred for 1000 generations and included a bottleneck on generation 750. In the last generation of the historical population, animals were randomly selected to create a breeding population with an effective population size (Ne) of 40. From the breeding population, a total of 40,000 animals were simulated over 20 generations. Phenotypes for a trait with a heritability of 0.1 was simulated, and genetic variance was 100% explained by 1000 QTLs. Phenotypes and genotypes were saved for all animals in the last 10 generations, while pedigree from all 20 generations was used. To mimic the selection of young animals, a forward-in-time prediction was implemented by masking the phenotypes from the 20^th generation in the data analysis. The accuracy of prediction was the correlation between the simulated true breeding value and the EBV in the last generation. Genomic information consisted of SNP chips with 6K, 11K, 22K, 45K, and 90K markers evenly spaced across the genome. The two breeding strategies considered in this study were; 1) 25 families with 40 animals each and 2) 50 families with 20 animals each. Additionally, the number of generations with phenotypes varied from nine to one. This was done to identify the minimum number of generations to collect data, as phenotyping individuals may be challenging in aquaculture. In all scenarios, genomic selection outperformed family selection. Results indicated that genotyping more individuals within families, instead of more families with fewer individuals, improved the accuracy of predictions. Furthermore, the maximum accuracy was achieved with 45K and 90K SNPs in both scenarios, showing that SNP density will reach a plateau. Finally, maximum accuracy plateaued at five generations. Finally, having one generation with phenotypes and genotypes yielded higher accuracies than nine generations with only phenotypes. Overall, the results from the present study suggest that genomic selection using the single-step genomic best linear unbiased prediction (ssGBLUP) methodology is a feasible tool to improve P. vannamei commercial populations, even for traits with low heritability. Moreover, it is recommended to collect data on more individuals per family than increasing the number of families, and data collection should be continued for at least four generations. Tests with real data will provide further insights into the ideal number of animals and SNP density necessary for maximum accuracy.