In 2019, fish accounted for 17.3% of global animal protein consumption. Fisheries production has risen by 137% since 1980, whereas aquaculture production has soared by 1815%. As a result, aquaculture is expected to imminently surpass fisheries as the primary source of fish. However, the integration of genomic technologies into breeding programs has been slow, with only 10% of animals produced descending from genetically improved ones. There is a need for additional strategies to implement genomic tools into fish breeding programs to boost production.
The objective for this project is to develop a digital twin (computer simulation) platform of a Barramundi breeding program. The aim of the digital twin is to determine the highest benefit/cost ratio in the implementation of genomic selection and other genomic technologies, such as parental selection. The accuracy of the digital twin will be maximised by using real genotypes currently available through MainStream Aquaculture’s breeding program.
We aim to use data from the breeding program to ensure accuracy and calibrate the digital twin. We will begin by incorporating genotype data from founders and reference populations to establish trait heritabilities and the patterns of linkage and linkage disequilibrium, which are crucial parameters affecting genomic selection accuracy. We will ensure that the digital twin is adaptable to accommodate the reproductive biology characteristics of Barramundi. With this simulation we will be able to model maximum gains for important traits, while maintaining genetic diversity and informing the optimal breeding program for various environments.