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  • 1
    Online Resource
    Online Resource
    Wiley ; 2018
    In:  Journal of Animal Breeding and Genetics Vol. 135, No. 4 ( 2018-08), p. 323-332
    In: Journal of Animal Breeding and Genetics, Wiley, Vol. 135, No. 4 ( 2018-08), p. 323-332
    Abstract: The inbreeding coefficients are considered in breeding decisions, and the inverse numerator relationship matrix A −1 is a prerequisite for breeding value estimation. Polyandry and haploid males are among the specifics of relationships between honey bees. Brascamp and Bijma (2014) averaged out the manifold possible relationships among honey bees that appear to have the same parents in a pedigree and assigned a single entry in A to animals that behave as a unit, for example, the workers of a hive. Their methods of calculation connected full‐sibs in the variance matrix of the Mendelian sampling terms D , via nonzero off‐diagonal elements. This impedes the inversion of A and the closely connected calculation of inbreeding coefficients, because efficient algorithms for this task take D to be a diagonal matrix. Memory limitations necessitate their use for large data sets. We adapted the quickest of them to the block diagonal matrix D , that is postulated for the honey bee. To our knowledge, the presented algorithm is the first one that facilitates the method of Brascamp and Bijma (2014) on large data sets.
    Type of Medium: Online Resource
    ISSN: 0931-2668 , 1439-0388
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2020402-4
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Genetics Selection Evolution Vol. 53, No. 1 ( 2021-12)
    In: Genetics Selection Evolution, Springer Science and Business Media LLC, Vol. 53, No. 1 ( 2021-12)
    Abstract: 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 $$ 〉 0.95$$ 〉 0.95 in cases where many passive queens had dams from the breeding population. For self-sufficient passive populations, the coefficients of determination were lower ( $$\sim 0.8$$ ∼ 0.8 ) 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.
    Type of Medium: Online Resource
    ISSN: 1297-9686
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2012369-3
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Genetics Selection Evolution Vol. 53, No. 1 ( 2021-12)
    In: Genetics Selection Evolution, Springer Science and Business Media LLC, Vol. 53, No. 1 ( 2021-12)
    Abstract: An amendment to this paper has been published and can be accessed via the original article.
    Type of Medium: Online Resource
    ISSN: 1297-9686
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2012369-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Genetics Selection Evolution Vol. 53, No. 1 ( 2021-12)
    In: Genetics Selection Evolution, Springer Science and Business Media LLC, Vol. 53, No. 1 ( 2021-12)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1297-9686
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2012369-3
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Heredity Vol. 130, No. 5 ( 2023-05), p. 320-328
    In: Heredity, Springer Science and Business Media LLC, Vol. 130, No. 5 ( 2023-05), p. 320-328
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0018-067X , 1365-2540
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2006446-9
    detail.hit.zdb_id: 2423-5
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Insects Vol. 11, No. 7 ( 2020-06-30), p. 404-
    In: Insects, MDPI AG, Vol. 11, No. 7 ( 2020-06-30), p. 404-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2075-4450
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662247-6
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  • 7
    In: Insects, MDPI AG, Vol. 11, No. 11 ( 2020-11-07), p. 768-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2075-4450
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662247-6
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  G3 Genes|Genomes|Genetics Vol. 12, No. 2 ( 2022-02-04)
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 12, No. 2 ( 2022-02-04)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2629978-1
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  • 9
    In: Ecology and Evolution, Wiley, Vol. 10, No. 13 ( 2020-07), p. 6246-6256
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2045-7758 , 2045-7758
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2635675-2
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  • 10
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Heredity Vol. 126, No. 5 ( 2021-05), p. 733-747
    In: Heredity, Springer Science and Business Media LLC, Vol. 126, No. 5 ( 2021-05), p. 733-747
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0018-067X , 1365-2540
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2006446-9
    detail.hit.zdb_id: 2423-5
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