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Background: Genomic selection can increase genetic gain within aquaculture breeding programs, but the high costs related to high-density genotyping of a large number of individuals would make the breeding program expensive. In this study, a low-cost method using low-density genotyping of pre-selected candidates and their sibs was evaluated by stochastic simulation., Methods: A breeding scheme with selection for two traits, one measured on candidates and one on sibs was simulated. Genomic breeding values were estimated within families and combined with conventional family breeding values for candidates that were pre-selected based on conventional BLUP breeding values. This strategy was compared with a conventional breeding scheme and a full genomic selection program for which genomic breeding values were estimated across the whole population. The effects of marker density, level of pre-selection and number of sibs tested and genotyped for the sib-trait were studied., Results: Within-family genomic breeding values increased genetic gain by 15% and reduced rate of inbreeding by 15%. Genetic gain was robust to a reduction in marker density, with only moderate reductions, even for very low densities. Pre-selection of candidates down to approximately 10% of the candidates before genotyping also had minor effects on genetic gain, but depended somewhat on marker density. The number of test-individuals, i.e. individuals tested for the sib-trait, affected genetic gain, but the fraction of the test-individuals genotyped only affected the relative contribution of each trait to genetic gain., Conclusions: A combination of genomic within-family breeding values, based on low-density genotyping, and conventional BLUP family breeding values was shown to be a possible low marker density implementation of genomic selection for species with large full-sib families for which the costs of genotyping must be kept low without compromising the effect of genomic selection on genetic gain.

Background: When estimating marker effects in genomic selection, estimates of marker effects may simply act as a proxy for pedigree, i.e. their effect may partially be attributed to their association with superior parents and not be linked to any causative QTL. Hence, these markers mainly explain polygenic effects rather than QTL effects. However, if a polygenic effect is included in a Bayesian model, it is expected that the estimated effect of these markers will be more persistent over generations without having to re-estimate the marker effects every generation and will result in increased accuracy and reduced bias., Methods: Genomic selection using the Bayesian method, 'BayesB' was evaluated for different marker densities when a polygenic effect is included (GWpEBV) and not included (GWEBV) in the model. Linkage disequilibrium and a mutation drift balance were obtained by simulating a population with a Ne of 100 over 1,000 generations., Results: Accuracy of selection was slightly higher for the model including a polygenic effect than for the model not including a polygenic effect whatever the marker density. The accuracy decreased in later generations, and this reduction was stronger for lower marker densities. However, no significant difference in accuracy was observed between the two models. The linear regression of TBV on GWEBV and GWpEBV was used as a measure of bias. The regression coefficient was more stable over generations when a polygenic effect was included in the model, and was always between 0.98 and 1.00 for the highest marker density. The regression coefficient decreased more quickly with decreasing marker density., Conclusions: Including a polygenic effect had no impact on the selection accuracy, but showed reduced bias, which is especially important when estimates of genome-wide markers are used to estimate breeding values over more than one generation.

A within-family marker-assisted selection scheme was designed for typical aquaculture breeding schemes, where most traits are recorded on sibs of the candidates. Here, sibs of candidates were tested for the trait and genotyped to establish genetic marker effects on the trait. BLUP breeding values were calculated, including information of the markers (MAS) or not (NONMAS). These breeding values were identical for all family members in the NONMAS schemes, but differed between family members in the MAS schemes, making within-family selection possible. MAS had up to twice the total genetic gain of the corresponding NONMAS scheme. MAS was somewhat less effective when heritability increased from 0.06 to 0.12 or when the frequency of the positive allele was <0.5. The relative efficiency of MAS was higher for schemes with more candidates, because of larger fullsib family sizes. MAS was also more efficient when male:female mating ratio changed from 1:1 to 1:5 or when the QTL explained more of the total genetic variation. Four instead of two markers linked to the QTL increased genetic gain somewhat. There was no significant difference in polygenic genetic gain between MAS and NONMAS for most schemes. The rates of inbreeding were lower for MAS than NON-MAS schemes, because fewer full-sibs were selected by MAS.

Abstract 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, 1000, 5000 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 (ΔF) constrained to 0.005 or 0.01 per generation. If the constraint on ΔF could not be held, a second batch of fish was genotyped etc. Compared with the genotyping of all selection candidates (1000, 5000 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 1000 fish instead of 100 for the larger schemes of 5000 and 10 000 candidates. The accuracy of breeding values was similar for all batch sizes (~0.30), but higher (~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.

Background: Genomic selection is a selection method where effects of dense genetic markers are first estimated in a test population and later used to predict breeding values of selection candidates. The aim of this paper was to investigate genetic gains, inbreeding and the accuracy of selection in a general genomic selection scheme for aquaculture, where the test population consists of sibs of the candidates. Methods: The selection scheme started after simulating 4000 generations in a Fisher-Wright population with a size of 1000 to create a founder population. The basic scheme had 3000 selection candidates, 3000 tested sibs of the candidates, 100 full-sib families, a trait heritability of 0.4 and a marker density of 0.5Ne/M. Variants of this scheme were also analysed. Results: The accuracy of selection in generation 5 was 0.823 for the basic scheme when the sibtesting was performed every generation. The accuracy was hardly reduced by selection, probably because the increased frequency of favourable alleles compensated for the Bulmer effect. When sib-testing was performed only in the first generation, in order to reduce costs, accuracy of selection in generation 5 dropped to 0.304, the main reduction occurring in the first generation. The genetic level in generation 5 was 6.35σa when sib-testing was performed every generation, which was 72%, 12% and 9% higher than when sib-testing was performed only in the first generation, only in the first three generations or every second generation, respectively. A marker density above 0.5Ne/M hardly increased accuracy of selection further. For the basic scheme, rates of inbreeding were reduced by 81% in these schemes compared to traditional selection schemes, due to within-family selection. Increasing the number of sibs to 6000 hardly affected the accuracy of selection, and increasing the number of candidates to 6000 increased genetic gain by 10%, mainly because of increased selection intensity. Conclusion: Various strategies were evaluated to reduce the amount of sib-testing and genotyping, but all resulted in loss of selection accuracy and thus of genetic gain. Rates of inbreeding were reduced by 81% in genomic selection schemes compared to traditional selection schemes for the parameters of the basic scheme, due to within-family selection. [ABSTRACT FROM AUTHOR]

Abstract: This study compared the accuracy for genome-wide breeding values (GWEBV) with traditional BLUP breeding values estimated from phenotypic performance of sibs (BLUPEBV) in a typical sib based aquaculture breeding scheme. The breeding schemes were closed nuclei with 1000 selection candidates from 100 families in each generation G1 to G5. Phenotypes were recorded on sibs of the selection candidates. Both selection candidates and their recorded sibs were genotyped using dense SNP genotyping technology and marker effects estimated from the sibs were used to predict GWEBV of the selection candidates. With 40 sibs, accuracy in G3 was 0.54 for BLUPEBV compared to 0.78 for GWEBV (marker density 0.5N e /M), i.e. a relative increase of 44%. When the number of sibs for the GWEBV scheme decreased from 40 to 20 sibs, accuracy decreased by 7% in G3. Both for BLUPEBV and GWEBV breeding schemes, the effect of heritability on accuracy was small and accuracy increased around 4% when increasing heritability from 0.2 to 0.4. Contrary to expectations differences in accuracy between BLUPEBV and GWEBV were the same for the low (0.2) and high (0.4) heritability, which may be due to the large number of phenotypic records used. When records were only available in generation G1, accuracy of GWEBV decreased over generations and was 0.66 in G1, 0.57 in G2 and 0.51 in G3 with 40 sibs and marker density 0.5N e /M. Accuracy increased with increased marker density for GWEBV, but the increase was small when marker density increased from 0.5 to 1.0N e /M except when the test group size was increased from 20 to 40, indicating that with a higher marker density a higher number of phenotypes is needed. Increasing the size of the genome (1000 cM vs. 2000 cM) decreased the accuracy for the GWEBV breeding values but increased the accuracy for the BLUPEBV breeding values. Overall, accuracy was up to 33% higher for GWEBV than for BLUPEBV for the studied scenarios because both between and within family genetic variances are utilized when estimating genome-wide breeding values. The results indicate that aquaculture breeding companies can increase the accuracy of selection when using genomic selection in a typical sib based aquaculture breeding scheme. [Copyright &y& Elsevier]

Partial least square regression (PLSR) and principal component regression (PCR) are methods designed for situations where the number of predictors is larger than the number of records. The aim was to compare the accuracy of genome-wide breeding values (EBV) produced using PLSR and PCR with a Bayesian method, 'BayesB'. Marker densities of 1, 2, 4 and 8 Ne markers/Morgan were evaluated when the effective population size (Ne) was 100. The correlation between true breeding value and estimated breeding value increased with density from 0.611 to 0.681 and 0.604 to 0.658 using PLSR and PCR respectively, with an overall advantage to PLSR of 0.016 (s.e = 0.008). Both methods gave a lower accuracy compared to the 'BayesB', for which accuracy increased from 0.690 to 0.860. PLSR and PCR appeared less responsive to increased marker density with the advantage of 'BayesB' increasing by 17% from a marker density of 1 to 8Ne/M. PCR and PLSR showed greater bias than 'BayesB' in predicting breeding values at all densities. Although, the PLSR and PCR were computationally faster and simpler, these advantages do not outweigh the reduction in accuracy, and there is a benefit in obtaining relevant prior information from the distribution of gene effects. [ABSTRACT FROM AUTHOR]

Background: Infectious pancreatic necrosis (IPN) is one of the most prevalent and economically devastating diseases in Atlantic salmon (Salmo salar) farming worldwide. The disease causes large mortalities at both the fry- and post-smolt stages. Family selection for increased IPN resistance is performed through the use of controlled challenge tests, where survival rates of sib-groups are recorded. However, since challenge-tested animals cannot be used as breeding candidates, within-family selection is not performed and only half of the genetic variation for IPN resistance is being exploited. DNA markers linked to quantitative trait loci (QTL) affecting IPN resistance would therefore be a powerful selection tool. The aim of this study was to identify and fine-map QTL for IPN-resistance in Atlantic salmon, for use in marker-assisted selection to increase the rate of genetic improvement for this trait. Results: A genome scan was carried out using 10 large full-sib families of challenge-tested Atlantic salmon post-smolts and microsatellite markers distributed across the genome. One major QTL for IPN-resistance was detected, explaining 29% and 83% of the phenotypic and genetic variances, respectively. This QTL mapped to the same location as a QTL recently detected in a Scottish Atlantic salmon population. The QTL was found to be segregating in 10 out of 20 mapping parents, and subsequent fine-mapping with additional markers narrowed the QTL peak to a 4 cM region on linkage group 21. Challenge-tested fry were used to show that the QTL had the same effect on fry as on post-smolt, with the confidence interval for QTL position in fry overlapping the confidence interval found in post-smolts. A total of 178 parents were tested for segregation of the QTL, identifying 72 QTL-heterozygous parents. Genotypes at QTL-heterozygous parents were used to determine linkage phases between alleles at the underlying DNA polymorphism and alleles at single markers or multi-marker haplotypes. One four-marker haplotype was found to be the best predictor of QTL alleles, and was successfully used to deduce genotypes of the underlying polymorphism in 72% of the parents of the next generation within a breeding nucleus. A highly significant population-level correlation was found between deduced alleles at the underlying polymorphism and survival of offspring groups in the fry challenge test, parents with the three deduced genotypes (QQ, Qq, qq) having mean offspring mortality rates of 0.13, 0.32, and 0.49, respectively. The frequency of the high-resistance allele (Q) in the population was estimated to be 0.30. Apart from this major QTL, one other experiment-wise significant QTL for IPN-resistance was detected, located on linkage group 4. Conclusion: The QTL confirmed in this study represents a case of a major gene explaining the bulk of genetic variation for a presumed complex trait. QTL genotypes were deduced within most parents of the 2005 generation of a major breeding company, providing a solid framework for linkage-based MAS within the whole population in subsequent generations. Since haplotype-trait associations valid at the population level were found, there is also a potential for MAS based on linkage disequilibrium (LD). However, in order to use MAS across many generations without reassessment of linkage phases between markers and the underlying polymorphism, the QTL needs to be positioned with even greater accuracy. This will require higher marker densities than are currently available. [ABSTRACT FROM AUTHOR]

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]

The main aim of the current study was to compare the accuracy of selection for muscle content of fat and the health-promoting omega-3 fatty acids EPA and DHA in Atlantic salmon, by varying the sources of genetic information used in the estimation of breeding values. The following genetic information sources were compared: pedigree, SNP-chip markers and allele-specific expression markers. The results showed that differences between information sources were in general small, and different genetic information performed best for different traits. SNP-chip performed best for DHA, and pedigree performed best for EPA. Knowledge from gene expression analysis of a few individuals can be utilized to select a small panel of markers that perform relatively well. Genetic markers of allele-specific expression were able to capture a lot of genetic variation for DHA, but did not give significantly higher accuracies when combined with SNP-chip or pedigree information. The cross-validation accuracies for selection for DHA and EPA were moderate and offer possibilities for selection for these traits, especially if one extends the reference data set to a much bigger population, with more sibs per selection candidate. • We reveal the possibilities and accuracies of selection for the health promoting fatty acids EPA and DHA in Atlantic salmon. • The following genetic information sources were compared: pedigree, SNP-chip markers, and allele-specific expression markers. • A new discovery of this paper is that that different genetic information sources performs best for different traits. • Markers of variation in gene expression explain large fractions of the total genetic variability for DHA in Atlantic salmon. • Selection accuracies for DHA and EPA were moderate and show potential for selective breeding for these traits. [ABSTRACT FROM AUTHOR]