The number of drones to inseminate a queen with has little potential for optimization of honeybee breeding programs.

BACKGROUND: Mating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones' descent, the breeder's control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs. METHODS: By computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony. RESULTS: SDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed. CONCLUSION: The opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen's spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose.

Comparison of pooled semen insemination and single colony insemination as sustainable honeybee breeding strategies.

Instrumental insemination of honeybees allows for two opposing breeding strategies. In single colony insemination (SCI), all drones to inseminate a queen are taken from one colony. In pooled semen insemination (PSI), sperm of many genetically diverse drones is mixed and queens are fertilized from the resulting drone pool. While SCI allows for maximum pedigree control, proponents of PSI claim to reduce inbreeding and maintain genetic variance. Using stochastic simulation studies, we compared genetic progress and inbreeding rates in small honeybee populations under SCI and PSI. Four different selection criteria were covered: estimated breeding values (EBV), phenotypes, true breeding values (TBV) and random selection. Under EBV-based truncation selection, SCI yielded 9.0% to 44.4% higher genetic gain than PSI, but had vastly increased inbreeding rates. Under phenotypical or TBV selection, the gap between SCI and PSI in terms of genetic progress narrowed. Throughout, PSI yielded lower inbreeding rates than SCI, but the differences were only substantial under EBV truncation selection. As a result, PSI did not appear as a viable breeding strategy owing to its incompatibility with modern methods of genetic evaluation. Instead, SCI is to be preferred but instead of strict truncation selection, strategies to avoid inbreeding need to be installed.

The Potential of Instrumental Insemination for Sustainable Honeybee Breeding.

Mating control is crucial in honeybee breeding and commonly guaranteed by bringing virgin queens to isolated mating stations (IMS) for their nuptial flights. However, most breeding programs struggle to provide sufficiently many IMS. Research institutions routinely perform instrumental insemination of honeybees, but its potential to substitute IMS in breeding programs has not been sufficiently studied. We performed stochastic simulations to compare instrumental insemination strategies and mating on IMS in terms of genetic progress and inbreeding development. We focused on the role of paternal generation intervals, which can be shortened to two years with instrumental insemination in comparison to three years when using IMS. After 70 years, instrumental insemination yielded up to 42% higher genetic gain than IMS strategies-particularly with few available mating sites. Inbreeding rates with instrumental insemination and IMS were comparable. When the paternal generation interval in instrumental insemination was stretched to three years, the number of drone producers required for sustainable breeding was reduced substantially. In contrast, when shortening the interval to two years, it yielded the highest generational inbreeding rates (up to 2.28%). Overall, instrumental insemination with drones from a single colony appears as a viable strategy for honeybee breeding and a promising alternative to IMS.

First large-scale genomic prediction in the honey bee.

Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyzes the accuracy and bias of pedigree-based and genomic breeding values for honey yield, three workability traits, and two traits for resistance against the parasite Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the accuracy of pedigree-based estimated breeding values was 0.12 for honey yield, and ranged from 0.42 to 0.61 for the workability traits. The inclusion of genomic marker data improved these accuracies to 0.23 for honey yield, and a range from 0.44 to 0.65 for the workability traits. The inclusion of genomic data did not improve the accuracy of the disease-related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. For all traits except the Varroa resistance traits, the bias with genomic methods was on a similar level compared to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees.

Consequences of incorrect genetic parameter estimates for single-trait and multi-trait genetic evaluations in honeybees.

Genetic and residual variances of traits are important input parameters for best linear unbiased prediction (BLUP) breeding value estimation. In honeybees, estimates of these variances are often associated with large standard errors, entailing a risk to perform genetic evaluations under wrong premises. The consequences hereof have not been sufficiently studied. In particular, there are no adequate investigations on this topic accounting for multi-trait selection or genetic peculiarities of the honeybee. We performed simulation studies and explored the consequences of selection for honeybee populations with a broad range of true and assumed genetic parameters. We found that in single-trait evaluations, the response to selection was barely compromised by assuming erroneous parameters, so that reductions in genetic progress after 20 years never exceeded 21%. Phenotypic selection appeared inferior to BLUP selection, particularly under low heritabilities. Parameter choices for genetic evaluation had great effects on inbreeding development. By wrongly assuming high heritabilities, inbreeding rates were reduced by up to 74%. When parallel selection was performed for two traits, the right choice of genetic parameters appeared considerably more crucial as several incorrect premises yielded inadvertent negative selection for one of the traits. This phenomenon occurred in multiple constellations in which the selection traits expressed a negative genetic correlation. It was not reflected in the estimated breeding values. Our results indicate that breeding efforts heavily rely on detailed knowledge on genetic parameters, particularly when multi-trait selection is performed. Thus, considerable effort should be invested into precise parameter estimations.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations.

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Simulation studies to optimize genomic selection in honey bees.

BACKGROUND: With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. METHODS: Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. RESULTS: Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10-20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. CONCLUSIONS: This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

Short-term effects of controlled mating and selection on the genetic variance of honeybee populations.

Directional selection in a population yields reduced genetic variance due to the Bulmer effect. While this effect has been thoroughly investigated in mammals, it is poorly studied in social insects with biological peculiarities such as haplo-diploidy or the collective expression of traits. In addition to the natural adaptation to climate change, parasites, and pesticides, honeybees increasingly experience artificial selection pressure through modern breeding programs. Besides selection, many honeybee breeding schemes introduce controlled mating. We investigated which individual effects selection and controlled mating have on genetic variance. We derived formulas to describe short-term changes of genetic variance in honeybee populations and conducted computer simulations to confirm them. Thereby, we found that the changes in genetic variance depend on whether the variance is measured between queens (inheritance criterion), worker groups (selection criterion), or both (performance criterion). All three criteria showed reduced genetic variance under selection. In the selection and performance criteria, our formulas and simulations showed an increased genetic variance through controlled mating. This newly described effect counterbalanced and occasionally outweighed the Bulmer effect. It could not be observed in the inheritance criterion. A good understanding of the different notions of genetic variance in honeybees, therefore, appears crucial to interpreting population parameters correctly.

A theoretical derivation of response to selection with and without controlled mating in honeybees.

BACKGROUND: In recent years, the breeding of honeybees has gained significant scientific interest, and numerous theoretical and practical improvements have been made regarding the collection and processing of their performance data. It is now known that the selection of high-quality drone material is crucial for mid to long-term breeding success. However, there has been no conclusive mathematical theory to explain these findings. METHODS: We derived mathematical formulas to describe the response to selection of a breeding population and an unselected passive population of honeybees that benefits indirectly from genetic improvement in the breeding population via migration. This was done under the assumption of either controlled or uncontrolled mating of queens in the breeding population. RESULTS: Our model equations confirm what has been observed in simulation studies. In particular, we have proven that the breeding population and the passive population will show parallel genetic gain after some years and we were able to assess the responses to selection for different breeding strategies. Thus, we confirmed the crucial importance of controlled mating for successful honeybee breeding. When compared with data from simulation studies, the derived formulas showed high coefficients of determination [Formula: see text] in cases where many passive queens had dams from the breeding population. For self-sufficient passive populations, the coefficients of determination were lower ([Formula: see text]) if the breeding population was under controlled mating. This can be explained by the limited simulated time-frame and lower convergence rates. CONCLUSION: The presented theoretical derivations allow extrapolation of honeybee-specific simulation results for breeding programs to a wide range of population parameters. Furthermore, they provide general insights into the genetic dynamics of interdependent populations, not only for honeybees but also in a broader context.

Substantial Genetic Progress in the International Apis mellifera carnica Population Since the Implementation of Genetic Evaluation.

The Apis mellifera carnica subspecies of the honeybee has long been praised for its gentleness and good honey yield before systematic breeding efforts began in the early 20th century. However, before the introduction of modern techniques of genetic evaluation (best linear unbiased prediction, BLUP) and a computerized data management in the mid 1990s, genetic progress was slow. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed to characterize breeding progress and inbreeding. From about the year 2000 onward, the genetic progression accelerated and resulted in a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The prognostic quality of breeding values is demonstrated by a retrospective analysis. The success of A. m. carnica breeding shows the potential of BLUP-based breeding values and serves as an example for a large-scale breeding program.