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.

Heritability of Apis mellifera recapping behavior and suppressed mite reproduction as resistance traits towards Varroa destructor.

The selection of honeybee strains resistant to the ectoparasitic mite Varroa destructor is generally considered as one of the most sustainable ways of coping with this major bee parasite. Thus, breeding efforts increasingly focus on resistance parameters in addition to common beekeeping traits like honey yield and gentleness. In every breeding effort, the success strongly depends on the quantifiability and heritability of the traits accounted. To find the most suitable traits among the manifold variants to assess Varroa resistance, it is necessary to evaluate how easily a trait can be measured (i.e., testing effort) in relation to the underlying heritability (i.e., expected transfer to the following generation). Various possible selection traits are described as beneficial for colony survival in the presence of Varroa destructor and therefore are measured in breeding stocks around the globe. Two of them in particular, suppressed mite reproduction (SMR, sensu lato any reproductive failure of mother mites) and recapping of already sealed brood cells have recently gained increasing attention among the breeders because they closely resemble resistance mechanisms of some Varroa-surviving honeybee populations. However, it was still unknown whether the genetic background of the trait is sufficient for targeted selection. We therefore investigated the heritabilities and genetic correlations for SMR and REC, distinguishing between recapping of infested cells (RECinf) and all cells (RECall), on an extensive dataset of Buckfast and Carniolan stock in Germany. With an accessible h² of 0.18 and 0.44 for SMR and an accessible h² of 0.44 and 0.40 for RECinf, both traits turned out to be very promising for further selection in the Buckfast and Carnica breeding population, respectively.

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.

ECM degradation in the Drosophila abdominal epidermis initiates tissue growth that ceases with rapid cell-cycle exit.

During development, multicellular organisms undergo stereotypical patterns of tissue growth in space and time. How developmental growth is orchestrated remains unclear, largely due to the difficulty of observing and quantitating this process in a living organism. Drosophila histoblast nests are small clusters of progenitor epithelial cells that undergo extensive growth to give rise to the adult abdominal epidermis and are amenable to live imaging. Our quantitative analysis of histoblast proliferation and tissue mechanics reveals that tissue growth is driven by cell divisions initiated through basal extracellular matrix degradation by matrix metalloproteases secreted by the neighboring larval epidermal cells. Laser ablations and computational simulations show that tissue mechanical tension does not decrease as the histoblasts fill the abdominal epidermal surface. During tissue growth, the histoblasts display oscillatory cell division rates until growth termination occurs through the rapid emergence of G0/G1 arrested cells, rather than a gradual increase in cell-cycle time as observed in other systems such as the Drosophila wing and mouse postnatal epidermis. Different developing tissues can therefore achieve their final size using distinct growth termination strategies. Thus, adult abdominal epidermal development is characterized by changes in the tissue microenvironment and a rapid exit from the cell cycle.

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.

Evaluation of Traits for the Selection of Apis Mellifera for Resistance against Varroa Destructor.

Infestation with Varroa destructor is a serious cause of bee colony (Apis mellifera) losses on a global level. However, the presence of untreated survivor populations in many different regions supports the idea that selection for resistance can be successful. As colony survival is difficult or impossible to measure, differences in mite infestation levels and tests for specific behavioral traits are used for selective breeding for Varroa resistance. In this paper we looked into different definitions of mite infestation and linked these with brood hygiene (pin test), brood recapping and suppressed mite reproduction. We based our analyses on datasets of Apis mellifera carnica from three countries: Austria (147 records), Croatia (135) and Germany (207). We concluded that bee infestation in summer, adjusted for the level of natural mite fall in spring, is a suitable trait in the breeding objective, and also suggested including brood infestation rate and the increase rate of bee infestation in summer. Repeatability for bee infestation rate was about 0.55, for cells opened in pin test about 0.33, for recapping 0.35 and for suppressed mite reproduction (SMR) virtually zero. Although in most cases we observed correlations with the expected sign between infestation parameters and behavioral traits, the values were generally low (<0.2) and often not significantly different from zero.

Tool for genomic selection and breeding to evolutionary adaptation: Development of a 100K single nucleotide polymorphism array for the honey bee.

High-throughput high-density genotyping arrays continue to be a fast, accurate, and cost-effective method for genotyping thousands of polymorphisms in high numbers of individuals. Here, we have developed a new high-density SNP genotyping array (103,270 SNPs) for honey bees, one of the most ecologically and economically important pollinators worldwide. SNPs were detected by conducting whole-genome resequencing of 61 honey bee drones (haploid males) from throughout Europe. Selection of SNPs for the chip was done in multiple steps using several criteria. The majority of SNPs were selected based on their location within known candidate regions or genes underlying a range of honey bee traits, including hygienic behavior against pathogens, foraging, and subspecies. Additionally, markers from a GWAS of hygienic behavior against the major honey bee parasite Varroa destructor were brought over. The chip also includes SNPs associated with each of three major breeding objectives-honey yield, gentleness, and Varroa resistance. We validated the chip and make recommendations for its use by determining error rates in repeat genotypings, examining the genotyping performance of different tissues, and by testing how well different sample types represent the queen's genotype. The latter is a key test because it is highly beneficial to be able to determine the queen's genotype by nonlethal means. The array is now publicly available and we suggest it will be a useful tool in genomic selection and honey bee breeding, as well as for GWAS of different traits, and for population genomic, adaptation, and conservation questions.

Long-Term Evaluation of Breeding Scheme Alternatives for Endangered Honeybee Subspecies.

Modern breeding structures are emerging for European honeybee populations. However, while genetic evaluations of honeybees are becoming increasingly well understood, little is known about how selection decisions shape the populations' genetic structures. We performed simulations evaluating 100 different selection schemes, defined by selection rates for dams and sires, in populations of 200, 500, or 1000 colonies per year and considering four different quantitative traits, reflecting different genetic parameters and numbers of influential loci. Focusing on sustainability, we evaluated genetic progress over 100 years and related it to inbreeding developments. While all populations allowed for sustainable breeding with generational inbreeding rates below 1% per generation, optimal selection rates differed and sustainable selection was harder to achieve in smaller populations and for stronger negative correlations of maternal and direct effects in the selection trait. In small populations, a third or a fourth of all candidate queens should be selected as dams, whereas this number declined to a sixth for larger population sizes. Furthermore, our simulations indicated that, particularly in small populations, as many sires as possible should be provided. We conclude that carefully applied breeding provides good prospects for currently endangered honeybee subspecies, since sustainable genetic progress improves their attractiveness to beekeepers.

The importance of controlled mating in honeybee breeding.

BACKGROUND: Controlled mating procedures are widely accepted as a key aspect of successful breeding in almost all animal species. In honeybees, however, controlled mating is hard to achieve. Therefore, there have been several attempts to breed honeybees using free-mated queens. In such breeding schemes, selection occurs only on the maternal path since the drone sires are random samples of the population. The success rates of breeding approaches without controlled mating have so far not been investigated on a theoretical or simulation-based level. METHODS: Stochastic simulation studies were carried out to examine the chances of success in honeybee breeding with and without controlled mating. We investigated the influence of different sizes of breeding populations (500, 1000, 2000 colonies per year) and unselected passive populations (0, 500, 1000, 2000, infinitely many colonies per year) on selection for a maternally (queen) and directly (worker group) influenced trait with moderate ([Formula: see text]) or strong ([Formula: see text]) negative correlation between the two effects. The simulations described 20 years of selection. RESULTS: Our simulations showed a reduction of breeding success between 47 and 99% if mating was not controlled. In the most drastic cases, practically no genetic gain could be generated without controlled mating. We observed that in the trade-off between selection for direct or maternal effects, the absence of mating control leads to a shift in favor of maternal effects. Moreover, we describe the implications of different breeding strategies on the unselected passive population that benefits only indirectly via the transfer of queens or drones from the breeding population. We show that genetic gain in the passive population develops parallel to that of the breeding population. However, we found a genetic lag that became significantly smaller as more breeding queens served as dams of queens in the passive population. CONCLUSIONS: We conclude that even when unwanted admixture of subspecies can be excluded in natural matings, controlled mating is imperative for successful breeding efforts. This is especially highlighted by the strong positive impact that controlled mating in the breeding population has on the unselected passive population.

Tissue Fluidity Promotes Epithelial Wound Healing.

The collective behaviour of cells in epithelial tissues is dependent on their mechanical properties. However, the contribution of tissue mechanics to wound healing in vivo remains poorly understood. Here we investigate the relationship between tissue mechanics and wound healing in live Drosophila wing imaginal discs and show that by tuning epithelial cell junctional tension, we can systematically alter the rate of wound healing. Coincident with the contraction of an actomyosin purse string, we observe cells flowing past each other at the wound edge by intercalating, reminiscent of molecules in a fluid, resulting in seamless wound closure. Using a cell-based physical model, we predict that a reduction in junctional tension fluidises the tissue through an increase in intercalation rate and corresponding reduction in bulk viscosity, in the manner of an unjamming transition. The resultant fluidisation of the tissue accelerates wound healing. Accordingly, when we experimentally reduce tissue tension in wing discs, intercalation rate increases and wounds repair in less time.

Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees.

Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of [Formula: see text], direct heritability of [Formula: see text], and a negative correlation between the effects of rmd = - 0.18. Another set of simulations was run with parameters [Formula: see text], [Formula: see text], and rmd = - 0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In the long-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.

Apical and Basal Matrix Remodeling Control Epithelial Morphogenesis.

Epithelial tissues can elongate in two dimensions by polarized cell intercalation, oriented cell division, or cell shape change, owing to local or global actomyosin contractile forces acting in the plane of the tissue. In addition, epithelia can undergo morphogenetic change in three dimensions. We show that elongation of the wings and legs of Drosophila involves a columnar-to-cuboidal cell shape change that reduces cell height and expands cell width. Remodeling of the apical extracellular matrix by the Stubble protease and basal matrix by MMP1/2 proteases induces wing and leg elongation. Matrix remodeling does not occur in the haltere, a limb that fails to elongate. Limb elongation is made anisotropic by planar polarized Myosin-II, which drives convergent extension along the proximal-distal axis. Subsequently, Myosin-II relocalizes to lateral membranes to accelerate columnar-to-cuboidal transition and isotropic tissue expansion. Thus, matrix remodeling induces dynamic changes in actomyosin contractility to drive epithelial morphogenesis in three dimensions.

EpiTools: An Open-Source Image Analysis Toolkit for Quantifying Epithelial Growth Dynamics.

Epithelia grow and undergo extensive rearrangements to achieve their final size and shape. Imaging the dynamics of tissue growth and morphogenesis is now possible with advances in time-lapse microscopy, but a true understanding of their complexities is limited by automated image analysis tools to extract quantitative data. To overcome such limitations, we have designed a new open-source image analysis toolkit called EpiTools. It provides user-friendly graphical user interfaces for accurately segmenting and tracking the contours of cell membrane signals obtained from 4D confocal imaging. It is designed for a broad audience, especially biologists with no computer-science background. Quantitative data extraction is integrated into a larger bioimaging platform, Icy, to increase the visibility and usability of our tools. We demonstrate the usefulness of EpiTools by analyzing Drosophila wing imaginal disc growth, revealing previously overlooked properties of this dynamic tissue, such as the patterns of cellular rearrangements.