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

Population-wide modelling reveals prospects of marker-assisted selection for parasitic mite resistance in honey bees

In 2019, a joint eight-variant model was published in which eight single nucleotide polymorphisms (SNPs) in seven Apis mellifera genes were associated with Varroa destructor drone brood resistance (DBR, i.e. mite non-reproduction in drone brood). As this model was derived from only one Darwinian Black Bee Box colony, it could not directly be applied on a population-overarching scale in the northern part of Belgium (Flanders), where beekeepers prefer the carnica subspecies. To determine whether these eight SNPs remained associated with the DBR trait on a Flemish colony-broad scope, we performed population-wide modelling through sampling of various A. mellifera carnica colonies, DBR scoring of Varroa-infested drone brood and variant genotyping. Novel eight-variant modelling was performed and the classification performance of the eight SNPs was evaluated. Besides, we built a reduced three-variant model retaining only three genetic variants and found that this model classified 76% of the phenotyped drones correctly. To examine the spread of beneficial alleles and predict the DBR probability distribution in Flanders, we determined the allelic frequencies of the three variants in 292 A. mellifera carnica queens. As such, this research reveals prospects of marker-assisted selection for Varroa drone brood resistance in honeybees.

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.

Emerging threats and opportunities to managed bee species in European agricultural systems: a horizon scan

Managed bee species provide essential pollination services that contribute to food security worldwide. However, managed bees face a diverse array of threats and anticipating these, and potential opportunities to reduce risks, is essential for the sustainable management of pollination services. We conducted a horizon scanning exercise with 20 experts from across Europe to identify emerging threats and opportunities for managed bees in European agricultural systems. An initial 63 issues were identified, and this was shortlisted to 21 issues through the horizon scanning process. These ranged from local landscape-level management to geopolitical issues on a continental and global scale across seven broad themes-Pesticides & pollutants, Technology, Management practices, Predators & parasites, Environmental stressors, Crop modification, and Political & trade influences. While we conducted this horizon scan within a European context, the opportunities and threats identified will likely be relevant to other regions. A renewed research and policy focus, especially on the highest-ranking issues, is required to maximise the value of these opportunities and mitigate threats to maintain sustainable and healthy managed bee pollinators within agricultural systems.

AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap

Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen's alleles and none from the many drones she mated with. Thus the ordered combination or 'phase' of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses.

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.

Cutting corners: The impact of storage and DNA extraction on quality and quantity of DNA in honeybee (Apis mellifera) spermatheca

The purpose of our study was to investigate methods of short-term storage that allow preservation, transport and retrieval of genetic information contained in honeybee queen's spermatheca. Genotyping of the honeybee colony requires well ahead planned sample collection, depending on the type of data to be acquired. Sampling and genotyping of spermatheca's content instead of individual offspring is timesaving, allowing answers to the questions related to patriline composition immediately after mating. Such procedure is also cheaper and less error prone. For preservation either Allprotect Tissue Reagent (Qiagen) or absolute ethanol were used. Conditions during transportation were simulated by keeping samples 6-8 days at room temperature. Six different storing conditions of spermathecas were tested, complemented with two DNA extraction methods. We have analysed the concentration of DNA, RNA, and proteins in DNA extracts. We also analysed how strongly the DNA is subjected to fragmentation (through amplification of genetic markers ANT2 and tRNA^leu-COX2) and whether the quality of the extracted DNA is suitable for microsatellite (MS) analysis. Then, we tested the usage of spermatheca as a source of patriline composition in an experiment with three instrumentally inseminated virgin queens and performed MS analysis of the extracted DNA from each spermatheca, as well as queens' and drones' tissue. Our results show that median DNA concentration from spermathecas excised prior the storage, regardless of the storing condition and DNA extraction method, were generally lower than median DNA concentration obtained from spermathecas dissected from the whole queens after the storage. Despite the differences in DNA yield from the samples subjected to different storing conditions there was no significant effect of storage method or the DNA extraction method on the amplification success, although fewer samples stored in EtOH amplified successfully in comparison to ATR storing reagent. However, we recommend EtOH as a storing reagent due to its availability, low price, simplicity in usage in the field and in the laboratory, and capability of good preservation of the samples for DNA analysis during transport at room temperature.

A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

Reconstructing queen genotypes by pool sequencing colonies in eusocial insects: Statistical Methods and their application to honeybee

Eusocial insects are crucial to many ecosystems, and particularly the honeybee (Apis mellifera). One approach to facilitate their study in molecular genetics, is to consider whole-colony genotyping by combining DNA of multiple individuals in a single pool sequencing experiment. Cheap and fast, this technique comes with the drawback of producing data requiring dedicated methods to be fully exploited. Despite this limitation, pool sequencing data have been shown to be informative and cost-effective when working on random mating populations. Here, we present new statistical methods for exploiting pool sequencing of eusocial colonies in order to reconstruct the genotypes of the queen of such colony. This leverages the possibility to monitor genetic diversity, perform genomic-based studies or implement selective breeding. Using simulations and honeybee real data, we show that the new methods allow for a fast and accurate estimation of the queen's genetic ancestry, with correlations of about 0.9 to that obtained from individual genotyping. Also, it allows for an accurate reconstruction of the queen genotypes, with about 2% genotyping error. We further validate these inferences using experimental data on colonies with both pool sequencing and individual genotyping of drones. In brief, in this study we present statistical models to accurately estimate the genetic ancestry and reconstruct the genotypes of the queen from pool sequencing data from workers of an eusocial colony. Such information allows to exploit pool sequencing for traditional population genetics analyses, association studies and for selective breeding. While validated in Apis mellifera, these methods are applicable to other eusocial hymenopterans.

Complex population structure and haplotype patterns in the Western European honey bee from sequencing a large panel of haploid drones

Honey bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower-density data set generated by an SNP-chip or by low-pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00654-x.

A SNP assay for assessing diversity in immune genes in the honey bee (Apis mellifera L.)

With a growing number of parasites and pathogens experiencing large-scale range expansions, monitoring diversity in immune genes of host populations has never been so important because it can inform on the adaptive potential to resist the invaders. Population surveys of immune genes are becoming common in many organisms, yet they are missing in the honey bee (Apis mellifera L.), a key managed pollinator species that has been severely affected by biological invasions. To fill the gap, here we identified single nucleotide polymorphisms (SNPs) in a wide range of honey bee immune genes and developed a medium-density assay targeting a subset of these genes. Using a discovery panel of 123 whole-genomes, representing seven A. mellifera subspecies and three evolutionary lineages, 180 immune genes were scanned for SNPs in exons, introns (< 4 bp from exons), 3' and 5´UTR, and < 1 kb upstream of the transcription start site. After application of multiple filtering criteria and validation, the final medium-density assay combines 91 quality-proved functional SNPs marking 89 innate immune genes and these can be readily typed using the high-sample-throughput iPLEX MassARRAY system. This medium-density-SNP assay was applied to 156 samples from four countries and the admixture analysis clustered the samples according to their lineage and subspecies, suggesting that honey bee ancestry can be delineated from functional variation. In addition to allowing analysis of immunogenetic variation, this newly-developed SNP assay can be used for inferring genetic structure and admixture in the honey bee.

Distribution of the Main Apis mellifera Mitochondrial DNA Lineages in Italy Assessed Using an Environmental DNA Approach

Growing interest has been emerging on the need to monitor the genetic integrity of the European Apis mellifera subspecies that could be threatened by the human-mediated dispersion of non-native populations and lines. Mitochondrial DNA (mtDNA) lineages can provide useful information for this purpose. In this study, we took advantage of the environmental DNA (eDNA) contained in the honey, which can be analyzed to detect the main groups of mitotypes of the honey bees that produced it. In this study, we applied this eDNA to produce a distribution map all over the Italian peninsula and the two major islands (Sicily and Sardinia) of the following three honey bee mtDNA lineages: A, C and M. A total of 607 georeferenced honey samples, produced in all Italian regions, was analyzed to detect these lineages. The A lineage was widespread in Sicily, as expected, considering that A. m. siciliana carries the African lineage. Surprisingly, this lineage was also reported in about 14% of all other samples produced in almost all continental regions, and in Sardinia. The applied method obtained an updated distribution map of honey bee mtDNA lineages that could be useful to design policies for the conservation of Italian honey bee genetic resources.

Identification of quantitative trait loci associated with calmness and gentleness in honey bees using whole-genome sequences

The identification of quantitative trait loci (QTL) through genome-wide association studies (GWAS) is a powerful method for unravelling the genetic background of selected traits and improving early-stage predictions. In honey bees (Apis mellifera), past genetic analyses have particularly focused on individual queens and workers. In this study, we used pooled whole-genome sequences to ascertain the genetic variation of the entire colony. In total, we sampled 216 Apis mellifera mellifera and 28 Apis mellifera carnica colonies. Different experts subjectively assessed the gentleness and calmness of the colonies using a standardised protocol. Conducting a GWAS for calmness on 211 purebred A. m. mellifera colonies, we identified three QTL, on chromosomes 8, 6, and 12. The two first QTL correspond to LOC409692 gene, coding for a disintegrin and metalloproteinase domain-containing protein 10, and to Abscam gene, coding for a Dscam family member Abscam protein, respectively. The last gene has been reported to be involved in the domestication of A. mellifera. The third QTL is located 13 kb upstream of LOC102655631, coding for a trehalose transporter. For gentleness, two QTL were identified on chromosomes 4 and 3. They are located within gene LOC413669, coding for a lap4 protein, and gene LOC413416, coding for a bicaudal C homolog 1-B protein, respectively. The identified positional candidate genes of both traits mainly affect the olfaction and nervous system of honey bees. Further research is needed to confirm the results and to better understand the genetic and phenotypic basis of calmness and gentleness.

Non-Destructive Genotyping of Honeybee Queens to Support Selection and Breeding

In traditional bee breeding, the honeybee queen is chosen for breeding based on the performance of the colony produced by its mother. However, we cannot be entirely certain that a specific queen will produce offspring with desirable traits until we observe the young queen's new colony. Collecting the queen's genetic material enables quick and reliable determination of the relevant information. We sampled exuviae, feces, and wingtips for DNA extraction to avoid fatally injuring the queen when using tissue samples. Quantity and purity of extracted DNA were measured. Two mitochondrial markers were used to determine the lineage affiliation and exclude possible contamination of DNA extracts with non-honeybee DNA. dCAPS (derived Cleaved Amplified Polymorphic Sequences) markers allowed detection of single nucleotide polymorphisms (SNPs) in nuclear DNA regions presumably associated with Varroa sensitive hygiene and set the example of successful development of genotyping protocol from non-destructive DNA sources. One of the logical future steps in honeybee breeding is introducing genomic selection and non-destructive sampling methods of genetic material may be the prerequisite for successful genotyping. Our results demonstrate that the extraction of DNA from feces and exuviae can be introduced into practice. The advantage of these two sources over wingtips is reducing the time window for processing the samples, thus enabling genotyping directly after the queen's emergence.

Geographical Distribution and Selection of European Honey Bees Resistant to Varroa destructor

Developing resistance to the varroa mite in honey bees is a major goal for apicultural science and practice, the development of selection strategies and the availability of resistant stock. Here we present an extended literature review and survey of resistant populations and selection programs in the EU and elsewhere, including expert interviews. We illustrate the practical experiences of scientists, beekeepers, and breeders in search of resistant bees. We describe numerous resistant populations surviving without acaricide treatments, most of which developed under natural infestation pressure. Their common characteristics: reduced brood development; limited mite population growth; and low mite reproduction, may cause conflict with the interests of commercial beekeeping. Since environmental factors affect varroa mite resistance, particular honey bee strains must be evaluated under different local conditions and colony management. The resistance traits of grooming, hygienic behavior and mite reproduction, together with simple testing of mite population development and colony survival, are significant in recent selection programs. Advanced breeding techniques and genetic and physiological selection tools will be essential in the future. Despite huge demand, there is no well-established market for resistant stock in Europe. Moreover, reliable experience or experimental evidence regarding the resistance of stocks under different environmental and management conditions is still lacking.

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

Simple Summary

The Apis mellifera carnica subspecies of the honeybee is known for its gentleness and good honey yield. In the early 20th century, systematic breeding efforts began. Breeding progress was slow before the introduction of modern techniques of genetic evaluation in the mid 1990s. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed. From about 2000 onward, breeding progress accelerated. The result is a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The success of A. m. carnica breeding shows the potential of genetic evaluation.

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.

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.