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Background: Simulation studies have shown that accuracy and genetic gain are increased in genomic selection schemes compared to traditional aquaculture sib-based schemes. In genomic selection, accuracy of selection can be maximized by increasing the precision of the estimation of SNP effects and by maximizing the relationships between test sibs and candidate sibs. Another means of increasing the accuracy of the estimation of SNP effects is to create individuals in the test population with extreme genotypes. The latter approach was studied here with creation of double haploids and use of non-random mating designs., Methods: Six alternative breeding schemes were simulated in which the design of the test population was varied: test sibs inherited maternal (Mat), paternal (Pat) or a mixture of maternal and paternal (MatPat) double haploid genomes or test sibs were obtained by maximum coancestry mating (MaxC), minimum coancestry mating (MinC), or random (RAND) mating. Three thousand test sibs and 3000 candidate sibs were genotyped. The test sibs were recorded for a trait that could not be measured on the candidates and were used to estimate SNP effects. Selection was done by truncation on genome-wide estimated breeding values and 100 individuals were selected as parents each generation, equally divided between both sexes., Results: Results showed a 7 to 19% increase in selection accuracy and a 6 to 22% increase in genetic gain in the MatPat scheme compared to the RAND scheme. These increases were greater with lower heritabilities. Among all other scenarios, i.e. Mat, Pat, MaxC, and MinC, no substantial differences in selection accuracy and genetic gain were observed., Conclusions: In conclusion, a test population designed with a mixture of paternal and maternal double haploids, i.e. the MatPat scheme, increases substantially the accuracy of selection and genetic gain. This will be particularly interesting for traits that cannot be recorded on the selection candidates and require the use of sib tests, such as disease resistance and meat quality.

Background: Traditional family-based aquaculture breeding programs, in which families are kept separately until individual tagging and most traits are measured on the sibs of the candidates, are costly and require a high level of reproductive control. The most widely used alternative is a selection scheme, where families are reared communally and the candidates are selected based on their own individual measurements of the traits under selection. However, in the latter selection schemes, inclusion of new traits depends on the availability of non-invasive techniques to measure the traits on selection candidates. This is a severe limitation of these schemes, especially for disease resistance and fillet quality traits., Methods: Here, we present a new selection scheme, which was validated using computer simulations comprising 100 families, among which 1, 10 or 100 were reared communally in groups. Pooling of the DNA from 2000, 20000 or 50000 test individuals with the highest and lowest phenotypes was used to estimate 500, 5000 or 10000 marker effects. One thousand or 2000 out of 20000 candidates were preselected for a growth-like trait. These pre-selected candidates were genotyped, and they were selected on their genome-wide breeding values for a trait that could not be measured on the candidates., Results: A high accuracy of selection, i.e. 0.60-0.88 was obtained with 20000-50000 test individuals but it was reduced when only 2000 test individuals were used. This shows the importance of having large numbers of phenotypic records to accurately estimate marker effects. The accuracy of selection decreased with increasing numbers of families per group., Conclusions: This new selection scheme combines communal rearing of families, pre-selection of candidates, DNA pooling and genomic selection and makes multi-trait selection possible in aquaculture selection schemes without keeping families separately until individual tagging is possible. The new scheme can also be used for other farmed species, for which the cost of genotyping test individuals may be high, e.g. if trait heritability is low.

Background: Selection schemes aiming at introgressing genetic material from a donor into a recipient line may be performed by backcross-breeding programs combined with selection to preserve the favourable characteristics of the donor population. This stochastic simulation study investigated whether genomic selection can be effective in preserving a major quantitative trait locus (QTL) allele from a donor line during the backcrossing phase., Methods: In a simulation study, two fish populations were generated: a recipient line selected for a production trait and a donor line characterized by an enhanced level of disease resistance. Both traits were polygenic, but one major QTL affecting disease resistance was segregating only within the donor line. Backcrossing was combined with three types of selection (for total merit index) among the crossbred individuals: classical selection, genomic selection using genome-wide dense marker maps, and gene-assisted genomic selection. It was assumed that production could be observed directly on the selection candidates, while disease resistance had to be inferred from tested sibs of the selection candidates., Results: Classical selection was inefficient in preserving the target QTL through the backcrossing phase. In contrast, genomic selection (without specific knowledge of the target QTL) was usually effective in preserving the target QTL, and had higher genetic response to selection, especially for disease resistance. Compared with pure genomic selection, gene-assisted selection had an advantage with respect to disease resistance (28-40% increase in genetic gain) and acted as an extra precaution against loss of the target QTL. However, for total merit index the advantage of gene-assisted genomic selection over genomic selection was lower (4-5% increase in genetic gain)., Conclusion: Substantial differences between introgression programs using classical and genomic selection were observed, and the former was generally inferior with respect to both genetic gain and the ability to preserve the target QTL. Combining genomic selection with gene-assisted selection for the target QTL acted as an extra precaution against loss of the target QTL and gave additional genetic gain for disease resistance. However, the effect on total merit index was limited.

The aim of this paper was to study the performance of a novel fish breeding scheme, which is a combination of walk-back and optimum contribution selection using stochastic simulation. In this walk-back selection scheme, batches of different sizes (50, 100, 1,000, 5,000 and 10,000) with the phenotypically superior fish from one tank with mixed families were genotyped to set up the pedigree. BLUP estimated breeding values were calculated. The optimum contribution selection method was used with the rate of inbreeding (DeltaF) constrained to 0.005 or 0.01 per generation. If the constraint on DeltaF could not be held, a second batch of fish was genotyped etc. Compared with the genotyping of all selection candidates (1000, 5,000 or 10,000), the use of batches saves genotyping costs. The results show that two batches of 50 fish were often necessary. With a batch size of 100, genetic level was 76-92% of the genetic level achieved for schemes with all fish being genotyped and thus candidates for the optimum contribution selection step. More parents were selected for schemes with larger batches, resulting in a higher genetic gain, especially when all selection candidates were genotyped. There was little extra genetic gain in genotyping of 1,000 fish instead of 100 for the larger schemes of 5,000 and 10,000 candidates. The accuracy of breeding values was similar for all batch sizes (approximately 0.30), but higher (approximately 0.5) when all candidates were included. Since only the phenotypically most superior fish were genotyped, BLUP-EBV were biased. Compared with genotyping of all selection candidates, the use of batches saves genotyping costs, while simultaneously maintaining high genetic gains.

Abstract: Traceability schemes for aquaculture species are essential for tracing market product to farm of origin in the event of detection of disease or toxins in the market fish. DNA markers have been proposed as a tool for traceability. These markers can be used to genotype fish by taking a sample from live fish or fish product at any stage along the production chain. In this paper, we consider three alternate traceability schemes using DNA markers. The example of the Norwegian farmed Atlantic salmon industry was used. This industry, like many aquaculture industries, has three tiers, the nucleus, multiplier and commercial tiers. The nucleus individuals are grandparents of the commercial fish, and the multiplier individuals are the parents of the commercial fish. The traceability strategies we considered were: (1) FS, assignment of market place fish to full sib families based on the marker information (this strategy assumes all individuals from a full sib family are allocated to a single farm and a limited number of fish, representing all full sib families on that farm, are genotyped); (2) PAR, assignment of market place fish to parents (multiplier individuals) and (3) GRAND, assignment of market place fish to grandparents (nucleus individuals). Using simulation, we determined the number of DNA markers required to achieve 95% of correct assignment decisions for each strategy. The simulation included a wild population which contributed to market place fish. The wild fish were correctly assigned if they were excluded from belonging to the farmed population in each strategy, otherwise they were incorrectly assigned. Both microsatellite markers or single nucleotide polymorphism markers were considered. Seventy five, 15, and 50 microsatellites were required to achieve 95% correct assignment decisions for FS, PAR and GRAND, respectively. Four hundred, 75 and 200 SNPs were required to achieve 95% correct assignment decisions for FS, PAR and GRAND, respectively. If the cost of genotyping microsatellites is assumed to be five times as high as genotyping a SNP, GRAND using SNP markers is the cheapest strategy. The logistics of implementing each strategy are discussed. GRAND in particular and PAR in some industries requires complicated logistics. The most suitable and cost effective traceability strategy for a particular industry will depend heavily on the organisation of that industry, for example the degree of recording transfer of fish, eggs and larvae between tiers. Even if complicated logistics prevent the adoption of marker based schemes by some industries, traceability with DNA markers may still be important for verification of labelling-based schemes. [Copyright &y& Elsevier]

Abstract: The simulation study examined the effect in optimal contribution selection of specifying an additive genetic relationship matrix (A-matrix) deviating from the true A-matrix of the base population. A base population of 160 males and 160 females was sampled evenly from 4 or 8 randomly chosen, drift-differentiated, wild subpopulations. Wild fish had true inbreeding coefficient of F wild, either 0.05 or 0.20, giving additive genetic relationships of 2F wild within, and 0 between subpopulations. Selection based upon phenotypes was simulated on one continuous trait of heritability, h wild 2, either 0.1 or 0.3. Base animals were mated either within subpopulations, or at random, so that the first round of selection occurred either on purebred or crossbred fish respectively. Optimum contributions was implemented from generation 1 for a desired rate of inbreeding of 0.5% per generation, using an assumed F wild ⁎ which varied between 0.0 and 0.3. Assuming F wild ⁎ =0.0 compared to 0.3, resulted in steeper regressions of the fractional contributions of subpopulations on average breeding value, higher true inbreeding and consequently reduced additive genetic variance, albeit with larger genetic gain from the first round of selection. When the first round of selection followed random mating the regression of the fractional contributions of the subpopulations was also less steep. These effects persisted, but originated from the misspecification of F wild ⁎ and so will not accumulate further in the medium-to long-term. Consequently, assuming F wild ⁎ was less than the true value led to sub-optimal use of subpopulations with an increased risk of loss of alleles of direct and strategic relevance to the breeding program. In conclusion, assuming F wild ⁎ ≈0.0 should be avoided and F wild ⁎ should be estimated with procedures such as marker estimated kinships, Q ST estimates or empirical observations of clines. [Copyright &y& Elsevier]

Abstract: Guidelines for constructing base populations in fish were examined using a drift model. A large number of drift differentiated, wild subpopulations, with inbreeding F wild (F wild ∈[0.05–0.20]), and a trait with heritability h tot 2 (h tot 2 ∈[0.10–0.30]), were assumed. A base population was generated by sampling 2N fish from each of nsub subpopulations (nsub ∈[1–8]), and then mated to produce 160 or 240 families per generation. The matings were either at random, in a structured diallel-cross, or within subpopulations to produce generation 1. Optimal contribution selection on phenotype was applied from generation 1 to11, with a rate of inbreeding of 0.5% per generation. Genetic gain (Δg) improved with nsub, the improvement being largest when going from one to two subpopulations, with diminishing returns. Most of the potential improvement was obtained with nsub =4, and was well predicted by . Initial matings across subpopulations improved Δg from the second round of selection compared to within. Consequently the effect of the mating strategy were first observed 3 generation after decisions were made and persisted over subsequent generations, but with minimal effect on the accumulated additive genetic gain in generation 11. It was concluded that sampling from at least four subpopulations was beneficial and that mating across subpopulations should start as early as possible. [Copyright &y& Elsevier]