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The identification of genetic markers for traits of interest for aquaculture, such as growth, is an important step for the establishment of breeding programmes. As more genomic information becomes available the possibility of applying comparative genomics to identify and refine quantitative trait locus (QTLs) and potentially identify candidate genes responsible for the QTL effect may accelerate genetic improvement in established and new aquaculture species. Here we report such an approach on growth related traits in the European sea bass (Dicentrarchus labrax), an important species for European aquaculture. A genetic map was generated with markers targeted to previously identified QTL for growth which reduced distance and improved resolution in these regions. A total of 36 significant QTLs were identified when morphometric traits were considered individually in maternal half sibs, paternal half sibs and sib-pair analysis. Twenty seven new markers targeted to the growth QTLs, obtained by comparative mapping, reduced the average distance between markers from 23.4, 9.1, and 5.8 cM in the previous map to 3.4, 2.2, and 5.2 cM, on linkage group (LG) LG4, LG6 and LG15 respectively. Lists of genes embedded in the QTL – 591 genes in LG4, 234 genes in LG6 and 450 genes in LG15 – were obtained from the European sea bass genome. Comparative mapping revealed conserved gene synteny across teleost fishes. Functional protein association network analysis with the gene products of the 3 linkage groups revealed a large global association network including 42 gene products. Strikingly the association network was populated with genes of known biological importance for growth and body weight in terrestrial farm animals, such as elements of the signaling pathways for Jak–STAT, MAPK, adipocytokine and insulin, growth hormone, IGFI and II. This study demonstrates the feasibility of a comparative genomics combined with functional gene annotation to refine the resolution of QTL and the establishment of hypothesis to accelerate discovery of putative responsible genes.

Text: Better models of genetic evaluation for mastitis can be developed through longitudinal analysis of somatic cell count (SCC) which usually is used as a proxy for mastitis. Mastitis and recovery data with weekly observations of SCC were simulated for daughter groups of 60 and 240 per sire. Data were created to define cases: 1 if SCC was above a pre-specified boundary, else 0. A transition from below to above the boundary indicates probability to contract mastitis, and the other way indicates recovery. The MCMCglmm package was used to estimate breeding values. In the 60 daughters group, accuracies ranged from 0.53 to 0.54 for mastitis and 0.22 to 0.23 for recovery. Whereas, in the 240 daughters group accuracies ranged from 0.83 to 0.85 for mastitis and 0.57 to 0.65 for recovery. Reasonable accuracies can be achieved from SCC based estimates.

BackgroundWe applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the QTLMAS2009 workshop to derive a comprehensive set of results. A Gompertz curve was modelled on the yield data and showed good predictive properties. QTL analyses were done on the raw measurements and on the individual parameters of the Gompertz curve and its predicted growth for each interval. Half-sib and variance component linkage analysis revealed QTL with different modes of inheritance but with low resolution. This was complemented by association studies using single markers or haplotypes, and additive, dominance, parent-of-origin and epistatic QTL effects. All association analyses were done on phenotypes pre-corrected for pedigree effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Two-locus interaction analysis detected no epistatic pairs of QTL. Overall, using stringent thresholds we identified QTL regions using linkage analyses, corroborated by 6 individual SNPs with significant effects as well as two putatively imprinted SNPs.ConclusionsWe obtained consistent results across a combination of intra- and inter- family based methods using flexible linear models to evaluate a variety of models. The Gompertz curve fitted the data really well, and provided complementary information on the detected QTL. Retrospective comparisons of the results with actual data simulated showed that best results were obtained by including both yield and the parameters from the Gompertz curve despite the data being simulated using a logistic function.

A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.

Effects of genetic variation in porcine adipocyte and heart fatty acid-binding protein genes, A-FABP and H-FABP, respectively, on intramuscular fat (IMF) content and backfat thickness (BFT) were examined in F2 crossbreds of Meishan and Western pigs. The involvement of each FABP gene in IMF accretion was studied to confirm previous results for Duroc pigs. The F2 crossbred pigs were genotyped for various markers including microsatellite sequences situated within both FABP genes. Linkage analysis assigned the A-FABP and H-FABP genes to marker intervals S0001-S0217 (20 cM) on SSC4 and Sw316-S0003 (16.6 cM) on SSC6, respectively, refining previous chromosomal assignments. Next, the role of both chromosome regions/genes on genetic variation in IMF content and BFT was studied by 1) screening SSC4 and SSC6 for QTL affecting both traits by performing a line-cross analysis and 2) estimation of the effect of individual A-FABP and H-FABP alleles on both traits. In the first analysis, suggestive and chromosome-wise significant evidence for a QTL affecting IMF was detected on SSC6. The H-FABP gene is a candidate gene for this effect because it resides within the large region containing this putative QTL. The second analysis showed a considerable but nonsignificant effect of H-FABP microsatellite alleles on IMF content. Suggestive evidence for a QTL affecting BFT was found on SSC6, but H-FABP was excluded as a candidate gene. In conclusion, present and previous results support involvement of H-FABP gene polymorphisms in IMF accretion independently from BFT in pigs. Therefore, implementation of these polymorphisms in marker-assisted selection to control IMF content independently from BFT may be considered. In contrast to previous findings for Duroc pigs, no evidence was found for an effect of the A-FABP gene on IMF or BFT in this population.