Advancing fish breeding in aquaculture through genome functional annotation

International audience

Inglés. Genomics is increasingly applied in breeding programmes for farmed fish and shellfish species around the world. However, current applications do not include information on genome functional activity, which can enhance opportunities to predict relationships between genotypes and phenotypes and hence increase the accuracy of selection. Here, we review prospects for improving aquaculture breeding practises through the uptake of functional genomics data in light of the EU Horizon 2020 project AQUA-FAANG: ‘Advancing European Aquaculture by Genome Functional Annotation’. This consortium targeted the six major farmed fish species in European aquaculture, producing thousands of functional genomic datasets from samples representing embryos to mature adults of both sexes, and following immunological stimulation. This data was used to catalogue functional activity across the genome of each species, revealing transcribed regions, distinct chromatin states and regulatory elements impacting gene expression. These functional annotations were shared as open data through the Ensembl genome browser using the latest reference genomes for each species. AQUA-FAANG data offers novel opportunities to identify and prioritize causative genetic variants responsible for diverse traits including disease resistance, which can be exploited to enhance selective breeding. Such knowledge and associated resources have the potential to improve sustainability and boost production in aquaculture by accelerating genetic gain for health and robustness to infection, whilst reducing the requirement for animal testing. We further outline directions to advance and leverage genome functional annotation beyond the AQUA-FAANG project. Given the diversity of aquaculture sectors and businesses, the incorporation of functional genomic information into breeding decisions will depend on technological readiness level and scale of operation, with cost-benefit analysis necessary to determine the most profitable approach for each species and production system.