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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. [ABSTRACT FROM AUTHOR]

In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood‐based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user‐defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user‐set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (>200) are genotyped. We have demonstrated that APIS is an easy‐to‐use and reliable software for parentage assignment, even when up to 50% of sires are missing. [ABSTRACT FROM AUTHOR]

Viral Nervous Necrosis (VNN) is a major threat for the European sea bass (Dicentrarchus labrax) aquaculture industry. The improvement of disease resistance through selective breeding is a promising option to reduce outbreaks. With the development of high-throughput genotyping technologies, identification of genomic regions involved in the resistance could improve the efficiency of selective breeding. The aim of this study was to identify quantitative trait loci (QTL) involved in VNN resistance and to quantify their effect. Four experimental backcross families comprising 378, 454, 291 and 211 individuals and two commercial populations A and B comprising 1027 and 1042 individuals obtained from partial factorial crosses (59♂ x 20♀ for pop A; 39♂ x 14♀ for pop B) were submitted to a redspotted grouper nervous necrosis virus (RGNNV) challenge by bath. A high-density single nucleotide polymorphism (SNP) chip panel was designed to develop the ThermoFisher Axiom™ 57k SNP DlabChip, which was used for genotyping all individuals and building a high quality linkage map. In the backcross families, composite interval mapping was performed on 30,917, 23,592, 30,656 and 31,490 markers, respectively. In the commercial populations, 40,263 markers in pop A and 41,166 markers in pop B were used to perform genome-wide association studies (GWAS) using a GBLUP and a BayesCπ approach. One QTL was identified on chromosome LG12 in three of the four experimental backcross families, and one additional QTL on LG8 was detected in only one family. In commercial populations, QTL mapping revealed a total of seven QTLs, among which the previously mentioned QTL on LG12 was detected in both. This QTL, which was mapped to an interval of 3.45 cM, explained 9.21% of the total genetic variance in pop A, while other identified QTLs individually explained less than 1% of the total genetic variance. The identification of QTL regions involved in VNN resistance in European sea bass, with one having a strong effect, should have a great impact on the aquaculture industry. Future work could focus on the fine mapping of the causal mutation present on LG12 using whole genome sequencing. • Viral Nervous Necrosis is a major disease for European sea bass. • A novel SNP array for European sea bass was designed. • A total of nine QTL were detected. • One QTL, shared by five over six of the data sets and located on the LG12 explained 9.2% of the total genetic variance. [ABSTRACT FROM AUTHOR]