Whole-genome sequencing identifies interferon-induced protein IFI6/IFI27-like as a strong candidate gene for VNN resistance in European sea bass.

BACKGROUND: Viral nervous necrosis (VNN) is a major disease that affects European sea bass, and understanding the biological mechanisms that underlie VNN resistance is important for the welfare of farmed fish and sustainability of production systems. The aim of this study was to identify genomic regions and genes that are associated with VNN resistance in sea bass. RESULTS: We generated a dataset of 838,451 single nucleotide polymorphisms (SNPs) identified from whole-genome sequencing (WGS) in the parental generation of two commercial populations (A: 2371 individuals and B: 3428 individuals) of European sea bass with phenotypic records for binary survival in a VNN challenge. For each population, three cohorts were submitted to a red-spotted grouper nervous necrosis virus (RGNNV) challenge by immersion and genotyped on a 57K SNP chip. After imputation of WGS SNPs from their parents, quantitative trait loci (QTL) were mapped using a Bayesian sparse linear mixed model (BSLMM). We found several QTL regions that were specific to one of the populations on different linkage groups (LG), and one 127-kb QTL region on LG12 that was shared by both populations and included the genes ZDHHC14, which encodes a palmitoyltransferase, and IFI6/IFI27-like, which encodes an interferon-alpha induced protein. The most significant SNP in this QTL region was only 1.9 kb downstream of the coding sequence of the IFI6/IFI27-like gene. An unrelated population of four large families was used to validate the effect of the QTL. Survival rates of susceptible genotypes were 40.6% and 45.4% in populations A and B, respectively, while that of the resistant genotype was 66.2% in population B and 78% in population A. CONCLUSIONS: We have identified a genomic region that carries a major QTL for resistance to VNN and includes the ZDHHC14 and IFI6/IFI27-like genes. The potential involvement of the interferon pathway, a well-known anti-viral defense mechanism in several organisms (chicken, human, or fish), in survival to VNN infection is of particular interest. Our results can lead to major improvements for sea bass breeding programs through marker-assisted genomic selection to obtain more resistant fish.

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).

[This corrects the article DOI: 10.3389/fgene.2021.665920.].

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).

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.