The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying GW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values. Copyright © 2011. American Society of Animal Science .

This review paper provides an overview of the role of breeding objectives in livestock industries. The analyst developing a breeding objective must use the many and varied tools available in an appropriate theoretical context to quantify the benefits of alternative trait changes. The development in tools for deriving economic values has been from simple profit equations to detailed bio-economic models simulating the whole production system or industry. The importance of including the perspective of the stakeholders is also stressed. Stakeholder perspectives can be considered by using stated preference methods such as choice experiments. Also desired gain index or selection indices can be used in combination with profit equations or bio-economic models to adjust for undesirable genetic changes in breeding objective traits. This review shows that defining appropriate breeding objectives to account for the perspectives of different key stakeholders requires a multi-disciplinary approach.

The aim of the study was to estimate genetic parameters for direct and social genetic effects (SGE) for growth and welfare traits in farmed Atlantic cod (Gadus morhua). A SGE refers to the effect of an individual’s genes on trait performance of its social partners. In total, 2100 individually tagged juveniles from 100 families at an average age of 222 days post-hatching were used. Each family was separated into three groups of seven fish, and were randomly assigned to 100 experimental tanks, together with fish from two other families. Body weight and length of the first, second and third dorsal fin and the caudal fin measured by digital image analysis were measured at the start of the experiment, after two weeks, and after six weeks. Fin erosion was scored subjectively after six weeks. Variance components estimated using a conventional animal model were compared to those of an animal model including a SGE.

We studied genotype by environment interaction (G × E) for body weight (BW) of Atlantic cod (Gadus morhua L.) from the National cod breeding program in Norway. Records of 13,811 fish in a nucleus farm (NUC) and two test farms (PE_North, PE_South) in year-class (YC) 2007, and for 9149 fish in NUC and one test farm in YC 2010 were available. Heterogeneity of variances and heritabilities ( ) were estimated using a univariate animal model with environmental effects common to full-sibs (full-model). Genetic correlations ( ) between farms were estimated using a multivariate full-model and a reduced-model (without ) for each YC. Heterogeneity of was observed in both YC 2007 (0.10 to 0.16) and YC 2010 (0.08 to 0.26). The estimates of between NUC and test farms were relatively high for both models (0.81 ± 0.19 to 0.96 ± 0.17) and (0.81 ± 0.08 to 0.86 ± 0.04), suggesting low re-ranking of genotypes. Strong re-ranking of genotypes between PE_South and PE_North may be less important because most cod producers are situated close to the breeding nucleus. In conclusion, G × E between NUC and test farms were low and at present there is no need for separate breeding programs for BW in cod.