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)

Type Article
Date 2021-07
Language English
Author(s) Griot Ronan1, 2, 3, Allal FrancoisORCID3, Phocas Florence2, Brard-Fudulea Sophie1, Morvezen Romain1, Haffray Pierrick1, François Yoannah1, Morin Thierry4, Bestin Anastasia1, Bruant Jean-Sébastien5, Cariou Sophie5, Peyrou Bruno6, Brunier Joseph6, Vandeputte MarcORCID2, 7
Affiliation(s) 1 : SYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, France
2 : Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
3 : MARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, France
4 : ANSES, Ploufragan-Plouzané-Niort Laboratory, Viral Fish Diseases Unit, National Reference Laboratory for Regulated Fish Diseases, Technopôle Brest-Iroise, Plouzané, France
5 : Ferme Marine du Douhet, La Brée Les Bains, France
6 : Ecloserie Marine de Gravelines-Ichtus, Gravelines, France
7 : MARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, France
Source Frontiers In Genetics (1664-8021) (Frontiers Media SA), 2021-07 , Vol. 12 , P. 665920 (14p.)
DOI 10.3389/fgene.2021.665920
WOS© Times Cited 11
Keyword(s) genomic selection, dicentrarchus labrax, Sparus aurata, disease resistance, aquaculture

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.

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Griot Ronan, Allal Francois, Phocas Florence, Brard-Fudulea Sophie, Morvezen Romain, Haffray Pierrick, François Yoannah, Morin Thierry, Bestin Anastasia, Bruant Jean-Sébastien, Cariou Sophie, Peyrou Bruno, Brunier Joseph, Vandeputte Marc (2021). 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). Frontiers In Genetics, 12, 665920 (14p.). Publisher's official version : https://doi.org/10.3389/fgene.2021.665920 , Open Access version : https://archimer.ifremer.fr/doc/00718/83039/