BackgroundIn breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. This non-genomic reference scenario was compared to two genomic breeding programs to determine the best strategy for implementing genomic information in layer breeding programs.ResultsIn scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs.ConclusionsWhile genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

Due to consistent increases of inbreeding of on average 0.95% per generation in layer populations, selection tools should consider both genetic gain and genetic relationships in the long term. The optimum genetic contribution theory using official estimated breeding values for egg production was applied for 3 different lines of a layer breeding program to find the optimal allocations of hens and sires. Constraints in different scenarios encompassed restrictions related to additive genetic relationships, the increase of inbreeding, the number of selected sires and hens, and the number of selected offspring per mating. All these constraints enabled higher genetic gain up to 10.9% at the same level of additive genetic relationships or in lower relationships at the same gain when compared with conventional selection schemes ignoring relationships. Increases of inbreeding and genetic gain were associated with the number of selected sires. For the lowest level of the allowed average relationship at 10%, the optimal number of sires was 70 and the estimated breeding value for egg production of the selected group was 127.9. At the highest relationship constraint (16%), the optimal number of sires decreased to 15, and the average genetic value increased to 139.7. Contributions from selected sires and hens were used to develop specific mating plans to minimize inbreeding in the following generation by applying a simulated annealing algorithm. The additional reduction of average additive genetic relationships for matings was up to 44.9%. An innovative deterministic approach to estimate kinship coefficients between and within defined selection groups based on gene flow theory was applied to compare increases of inbreeding from random matings with layer populations undergoing selection. Large differences in rates of inbreeding were found, and they underline the necessity to establish selection tools controlling long-term relationships. Furthermore, it was suggested to use optimum genetic contribution theory for conservation schemes or, for example, the experimental line in our study.

In many livestock breeding programmes, the development of inbreeding is of critical importance. Thus, the assessment of the expected development of inbreeding should be an essential element in the design of breeding programmes. We propose a new method to deterministically predict the rate of inbreeding based on the gene-flow method in well-defined complex and dynamic breeding programmes. In the suggested approach, a breeding programme has to be structured in homogeneous age-sex-groups, so called cohorts, with a defined origin of genes. Starting from an initial setup (usually an unrelated and non-inbred base population), transition rules to calculate the kinship within and between cohorts originating from reproduction or ageing, respectively, are defined. Using this approach recursively provides the expected development of kinship within and between all cohorts over time, which can be combined into average kinships for the whole population or defined subsets. From these quantities, relevant parameters like the inbreeding rate or the effective population size are easily derived. We illustrate the method with a simple static example breeding programme in sheep. Based on this reference breeding programme, we demonstrate the use of our approach for dynamic breeding programmes, in which cohort sizes or vectors of gene origin change over time: here, we model the situation of exponential population growth and a bottleneck situation, respectively. The suggested approach does not account for the effect of selection on the development of inbreeding, but ideas to overcome this limitation are discussed.

A reference horse-breeding programme with 13500 foals each year was modelled with ZPLAN+. This new software for the optimization of the structures in breeding programmes is based on ZPLAN. In two scenarios, the implementation of a rigorous selection of mares was implemented. In scenario I, the mare performance test was the point of selection, while in scenario II, further information on 20 competitions in two more years is available. These selected mares were used for embryo transfer (ET), partly in combination with multiple ovulation (MOET). The selection intensity and the number of foals out of (MO)ET were varied in both scenarios. It was expected that 250, 500 and 1000 mares are available for selecting 20, 50, 100 or 200 donor mares each year. The number of foals out of (MO)ET was varied between one and six foals per donor mare and year. Donor mares were used for ET for 4 years. It became clear that with high selection intensities of donor mares and high reproduction rates of them, the yearly genetic gain in a horse-breeding programme could increase over a large range. In scenario II, the additional information on 20 competitions increased the accuracy of the selection index to 0.85. With 200 selected donor mares of 1000 available mares and six foals per year, the genetic gain could almost be doubled compared to the reference scenario. The implementation of ET and a related higher usage of few selected mares entails rising costs and a reduction in the genetic variance. In the most extreme MOET scenario, the effective population size was reduced by 19% relative to the reference scenario. Only if the increase in genetic gain can be converted into higher return for the breeders, the implementation of (MO)ET schemes is a realistic and sensible option for horse-breeding programmes.

In many livestock breeding programmes the development of inbreeding is of critical importance. Thus, the assessment of the expected development of inbreeding should be an essential element in the design of breeding programmes. We propose a new method to deterministically predict the rate of inbreeding based on the gene-flow method in well-defined complex and dynamic breeding programmes. In the suggested approach a breeding programme has to be structured in homogeneous age-sex-groups, so called cohorts, with a defined origin of genes. Starting from an initial setup (usually an unrelated and non-inbred base population) transition rules to calculate the kinship within and between cohorts originating from reproduction or aging, respectively, are defined. Using this approach recursively provides the expected development of kinship within and between all cohorts over time, which can be combined into average kinships for the whole population or defined subsets.From these quantities relevant parameters like the inbreeding rate or the effective population size are easily derived. We illustrate the method with a simple static example breeding programme in sheep. Based on this reference breeding programme we demonstrate the use of our approach for dynamic breeding programmes, in which cohort sizes or vectors of gene origin change over time: here we model the situation of exponential population growth and a bottleneck situation, respectively. The suggested approach does not account for the effect of selection on the development of inbreeding, but ideas to overcome this limitation are discussed.