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Background: Detecting a QTL is only the first step in genetic improvement programs. When a QTL with desirable characteristics is found, e.g. in a wild or unimproved population, it may be interesting to introgress the detected QTL into the commercial population. One approach to shorten the time needed for introgression is to combine both QTL identification and introgression, into a single step. This combines the strengths of fine mapping and backcrossing and paves the way for introgression of desirable but unknown QTL into recipient animal and plant lines. Methods: The method consisting in combining QTL mapping and gene introgression has been extended from inbred to outbred populations in which QTL allele frequencies vary both in recipient and donor lines in different scenarios and for which polygenic effects are included in order to model background genes. The effectiveness of the combined QTL detection and introgression procedure was evaluated by simulation through four backcross generations. Results: The allele substitution effect is underestimated when the favourable QTL allele is not fixed in the donor line. This underestimation is proportional to the frequency differences of the favourable QTL allele between the lines. In most scenarios, the estimates of the QTL location are unbiased and accurate. The retained donor chromosome segment and linkage drag are similar to expected values from other published studies. Conclusions: In general, our results show that it is possible to combine QTL detection and introgression even in outbred species. Separating QTL mapping and introgression processes is often thought to be longer and more costly. However, using a combined process saves at least one generation. With respect to the linkage drag and obligatory drag, the results of the combined detection and introgression scheme are very similar to those of traditional introgression schemes. [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]

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]