OPTIMIZING A RAINBOW TROUT BREEDING PROGRAM WITH GENOMIC SELECTION

Introduction

Traditional breeding programs use pedigree-based estimated breeding values for selective breeding to improve the performance of farmed fish species, however genomic selection can increase genetic gain. Genomic selection is routine in Atlantic Salmon, Tilapia and shrimp (Ødegård et al., 2014; Yoshida et al., 2019; Zenger et al., 2019), but has not been routinely implemented in breeding programs for important European cultured species such as; rainbow trout, Gilthead seabream and European seabass. Breeding companies that are interested in implementing genomic selection must optimize other parameters of the breeding programs, for example the family structure (Sonesson & Ødegard 2016) to maximize genetic gain. Other parameters to be optimized include selection intensity, mating strategy and the number and ratio of fish used for sib-testing and as selection candidates in the nucleus. In this study we aim to optimize these parameters as well as the selective genotyping strategy for a rainbow trout breeding program under restricted inbreeding to assess the potential impact of implementing genomic selection, using stochastic simulations.

Materials and Methods

A breeding program for rainbow trout was designed based on answers to questionnaires send to breeders. We used this breeding program design as the reference design. With this reference design 10 generations of selection were implemented using stochastic simulation in R software. The simulated traits were tagging weight, weight in nucleus, weight at sea, visceral percentage, fillet percentage and survival at sea. The traits weight at sea, visceral percentage, fillet percentage and survival at sea were measured on sibs of the selection candidates, which were kept in the production environment. The reference design consisted of 200 full sib families of 100 individuals produced each generation from 100 male and 100 female parents in a 2:2 mating design. Twenty five fish per full sib family were pre-selected for the nucleus and 15 fish for the production environment. Selection was based on a multi-trait selection index of traditional, pedigree-based estimated breeding values (EBVs). We also simulated this reference design with genomically estimated EBVs (GEBVs). To optimize the reference design with genomic selection, we simulated scenarios that varying 1) numbers of selected sires and dams, 2) mating ratios and 3) ratios between pre-selected fish for the nucleus and production. Additionally, we simulated different genotyping strategies, namely genotyping with lower density SNP panels and genotyping only a part of the selection candidates. Twenty replicates were simulated of each scenario.

Results

Table 1 reports the genetic gains and rate of inbreeding for different designs. Genomic selection resulted in higher genetic gains for all traits and a lower rate of inbreeding.

Discussion and Conclusion

Applying genomic selection while keeping the design the same resulted in higher genetic gains because genomic selection exploits both the between and within family variance whereas pedigree-based selection does not exploit within-family Mendelian sampling. Optimizing the number of selected sires and dams, the mating ratio and the ratio between pre-selected fish for the nucleus and production environment leads to important increases in genetic gains and can be achieved while keeping a restricted rate of inbreeding. Additionally, optimizing the genotyping strategy is important to reduce the cost of genotyping while the cost in genetic progress is limited.

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 818367 - AquaIMPACT.

References

Ødegård, J., Moen, T., Santi, N., Korsvoll, S. A., Kjøglum, S., & Meuwissen, T. H. (2014). Genomic prediction in an admixed population of Atlantic salmon (Salmo salar). Frontiers in genetics, 5, 402.

Sonesson, A. K., & Ødegård, J. (2016). Mating structures for genomic selection breeding programs in aquaculture. Genetics Selection Evolution, 48(1), 46.

Yoshida, G. M., Lhorente, J. P., Correa, K., Soto, J., Salas, D., & Yáñez, J. M. (2019). Genome-wide association study and cost-efficient genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus). G3: Genes, Genomes, Genetics, 9(8), 2597-2607.

Zenger, K. R., Khatkar, M. S., Jones, D. B., Khalilisamani, N., Jerry, D. R., & Raadsma, H. W. (2019). Genomic selection in aquaculture: application, limitations and opportunities with special reference to marine shrimp and pearl oysters. Frontiers in genetics, 9, 693.