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.

Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency.

BACKGROUND: Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI). RESULTS: We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines. CONCLUSIONS: In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs.

BACKGROUND: Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. RESULTS: Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. CONCLUSIONS: Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.