Front. Genet. Frontiers in Genetics Front. Genet. 1664-8021 Frontiers Media S.A. 754416 10.3389/fgene.2021.754416 Genetics Correction Corrigendum: 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) Griot et al. Corrigendum: Genomic Selection in Mediterranean Fishes Griot Ronan 1 2 3 Allal François 3 Phocas Florence 2 Brard-Fudulea Sophie 1 Morvezen Romain 1 Haffray Pierrick 1 François Yoannah 1 Morin Thierry 4 Bestin Anastasia 1 Bruant Jean-Sébastien 5 Cariou Sophie 5 Peyrou Bruno 6 Brunier Joseph 6 Vandeputte Marc 2 3 * SYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, France Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France MARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, France ANSES, Ploufragan-Plouzané-Niort Laboratory, Viral Fish Diseases Unit, National Reference Laboratory for Regulated Fish Diseases, Technopôle Brest-Iroise, Plouzané, France Ferme Marine du Douhet, La Brée Les Bains, France Ecloserie Marine de Gravelines-Ichtus, Gravelines, France

Edited and Reviewed by: Jesús Fernández, Instituto Nacional de Investigación y Tecnología Agroalimentaria (INIA), Spain

 *Correspondence: Marc Vandeputte, marc.vandeputte@inrae.fr

This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

 11 10 2021 2021 12 754416 06 08 2021 13 09 2021 Copyright © 2021 Griot, Allal, Phocas, Brard-Fudulea, Morvezen, Haffray, François, Morin, Bestin, Bruant, Cariou, Peyrou, Brunier and Vandeputte. 2021 Griot, Allal, Phocas, Brard-Fudulea, Morvezen, Haffray, François, Morin, Bestin, Bruant, Cariou, Peyrou, Brunier and Vandeputte

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 A Corrigendum on 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)by Griot, R., Allal, F., Phocas, F., Brard-Fudulea, S., Morvezen, R., Haffray, P., François, Y., Morin, T., Bestin, A., Bruant, J-S., Cariou, S., Peyrou, B., Brunier, J., and Vandeputte, M. (2021). Front. Genet. 12:754416. doi:10.3389/fgene.2021.665920 genomic selection dicentrarchus labrax sparus aurata disease resistance aquacultrure

In the original article, there was a mistake in Table 3 as published. Values in column “PBLUP” and “GBLUP_full” are inverted. The corrected Table 3 appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Prediction accuracy for VNN resistance in two European sea bass commercial cohorts (VNN_A and VNN_B), vibriosis resistance in one European sea bass commercial cohort (VIB) and pasteurellosis resistance in one gilthead sea bream commercial cohort (PAS) using different training population sizes and marker densities.

Data set Training population size PBLUP GBLUP_1K GBLUP_3K GBLUP_6K GBLUP_10K GBLUP_full
VNN_A 50 0.18 0.31 0.33 0.34 0.33 0.34
100 0.26 0.39 0.41 0.42 0.41 0.42
150 0.32 0.45 0.47 0.49 0.47 0.49
200 0.35 0.47 0.49 0.51 0.49 0.51
300 0.40 0.51 0.53 0.55 0.53 0.55
400 0.44 0.54 0.56 0.58 0.56 0.58
500 0.47 0.55 0.58 0.61 0.59 0.61
600 0.49 0.56 0.59 0.62 0.59 0.61
700 0.50 0.57 0.60 0.63 0.61 0.63
800 0.52 0.59 0.62 0.65 0.62 0.64
VNN_B 50 0.18 0.17 0.18 0.19 0.19 0.19
100 0.25 0.23 0.26 0.26 0.26 0.26
150 0.32 0.30 0.32 0.34 0.33 0.33
200 0.34 0.32 0.34 0.36 0.35 0.35
300 0.39 0.36 0.39 0.41 0.39 0.40
400 0.42 0.39 0.42 0.45 0.43 0.43
500 0.46 0.42 0.45 0.49 0.46 0.46
600 0.47 0.44 0.47 0.51 0.48 0.49
700 0.49 0.46 0.48 0.53 0.49 0.50
800 0.52 0.48 0.51 0.56 0.51 0.52
VIB 50 0.15 0.18 0.17 0.16 0.17 0.17
100 0.21 0.26 0.25 0.23 0.25 0.24
150 0.23 0.30 0.28 0.27 0.28 0.28
200 0.26 0.33 0.31 0.30 0.32 0.31
300 0.32 0.40 0.39 0.36 0.39 0.37
400 0.38 0.44 0.44 0.42 0.44 0.43
500 0.40 0.46 0.46 0.44 0.46 0.45
600 0.42 0.48 0.49 0.47 0.49 0.48
700 0.45 0.50 0.51 0.49 0.51 0.50
800 0.46 0.51 0.53 0.51 0.53 0.52
PAS 50 0.25 0.26 0.27 0.28 0.27 0.28
100 0.37 0.35 0.38 0.39 0.38 0.38
150 0.41 0.39 0.42 0.44 0.42 0.42
200 0.44 0.43 0.45 0.47 0.45 0.46
300 0.51 0.51 0.53 0.55 0.52 0.52
400 0.53 0.54 0.56 0.58 0.55 0.55
500 0.56 0.58 0.59 0.61 0.58 0.58
600 0.56 0.59 0.61 0.63 0.59 0.59
700 0.57 0.61 0.63 0.64 0.61 0.61

Prediction accuracy values are averaged over 100 replicates.

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