Abstract
Barramundi (Lates calcarifer) is a tropical species of increasing aquaculture interest. In efforts to accumulate genetic gains through selection, the species is now subject to several pedigree-based breeding programs globally; however, these breeding programs would further benefit from the implementation of genomic selection methodologies. Here we make genomic predictions for polygenic growth and body shape traits and compare expected genetic gains from genomic selection across several breeding strategies to those expected from pedigree-based on-farm selective breeding programs.A commercial scale cohort of barramundi (24 half-sib families) was grow-out at two farm locations, a brackish-water pond-based system in Queensland, and a freshwater recirculating aquaculture system (RAS) in Victoria. A total of 2139 fish were genotyped using a custom 70 k Axiom™ myDesign™ SNP array (ThermoFisher Scientific™) and phenotyped at harvest for whole fish weight (WFW), standard fish length (SL), body depth (BD), Fulton's condition factor (K) and body shape index (BS). Fillet weight (FW) was also recorded on a subset of Victorian fish. Heritabilities based on genomic relationships were estimated across barramundi from both farm sites for WFW (0.33 ± 0.06 to 0.35 ± 0.04), SL (0.27 ± 0.05 to 0.35 ± 0.04), BD (0.29 ± 0.06 to 0.30 ± 0.04), and K (0.14 ± 0.04 to 0.21 ± 0.04), and for FW (0.35 ± 0.16) in the Victorian site only. Genotype-by-environment interactions were also detected for all traits (GBLUP rg between locations ranging from 0.41 for K to 0.61 for SL).To optomise the design of a breeding program using genomic information, three possible genomic selection strategies were explored: MULTIPLE, separate breeding programs at each farm site; GENERAL, a single breeding program using the general effects from an interaction model; and SINGLE, a single breeding program based on the performance of individuals within one main farm site. The accuracy of GBLUP breeding value prediction for the WFW, SL and BD traits was higher than PBLUP for all scenarios (10–33% and 15–49% improved prediction accuracy in the Victoria and Queensland environments, respectively). Estimates of genetic gain were also consistently higher when GBLUP was applied (19–31% for WFW in VIC) compared to PBLUP (14–23% for WFW in VIC).Under the SINGLE scenario, genetic gains for the target farm site were the same as for MULTIPLE, but only 48–66% of the MULTIPLE gains were predicted for the non-target farm site. In comparison, GENERAL, which operates at half the cost of MULTIPLE, achieved 72–97% of the expected gains of MULTIPLE across farm sites, suggesting that it would be the most appropriate scenario for genomic selection in the production environments evaluated.
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