Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

Abstract

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K–60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51–0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.

Genomic selection and its research progress in aquaculture breeding

“…For small reference populations, combining populations of the same breed or populations of related breeds could increase the accuracy of genomic prediction, as observed for Holstein populations in EuroGenomics 144 as well as North American consortia 145 and pig populations in China 71,143 . Similar study has also been reported for disease resistance in European sea bass ( Dicentrarchus labrax ) and the gilthead sea bream ( Sparus aurata ), in which with the increase of reference population size, the accuracy of genome prediction is improved 110 . Our previous study has also clarified that as the reference population size decreased, the accuracy of genomic prediction decreased in four aquaculture species (Atlantic salmon, common carp [ Cyprinus carpio ], sea bream and rainbow trout) 146 …”

“…71,143 Similar study has also been reported for disease resistance in European sea bass (Dicentrarchus labrax) and the gilthead sea bream (Sparus aurata), in which with the increase of reference population size, the accuracy of genome prediction is improved. 110 Our previous study has also clarified that as the reference population size decreased, the accuracy of genomic prediction decreased in four aquaculture species (Atlantic salmon, common carp [Cyprinus carpio], sea bream and rainbow trout). 146…”

Genetic discrimination of wild versus farmed gilthead sea bream Sparus aurata using microsatellite markers associated with candidate genes

“…The pace of genetic progress in species important for Mediterranean aquaculture has recently accelerated through the work of several European projects. The recently developed and publicly available tools for high-throughput genotyping of ~30 K SNPs for European seabass and gilthead seabream (Peñaloza et al, 2021) as well as those in Griot et al (2021) provide a new tool that can be used to assess the genetic contribution of aquaculture escapees to wild populations. Surprisingly, none of the regulatory frameworks for aquaculture in the Mediterranean includes the management of escaped fish, in contrast to countries with highly developed marine aquaculture such as Norway, Canada or Chile.…”

What Can Genetics Do for the Control of Infectious Diseases in Aquaculture?

“…For aquaculture species, the benefit of genomic selection is that it exploits family genetic variation for traits that cannot be measured directly on selection candidates [ 47 , 48 ]. Different applications of genomic selection have been described in salmonids.…”