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Host-parasite co-evolution history is lacking when parasites switch to novel hosts. This was the case for Western honey bees (Apis mellifera) when the ectoparasitic mite, Varroa destructor, switched hosts from Eastern honey bees (Apis cerana). This mite has since become the most severe biological threat to A. mellifera worldwide. However, some A. mellifera populations are known to survive infestations, largely by suppressing mite population growth. One known mechanism is suppressed mite reproduction (SMR), but the underlying genetics are poorly understood. Here, we take advantage of haploid drones, originating from one queen from the Netherlands that developed Varroa-resistance, whole exome sequencing and elastic-net regression to identify genetic variants associated with SMR in resistant honeybees. An eight variants model predicted 88% of the phenotypes correctly and identified six risk and two protective variants. Reproducing and non-reproducing mites could not be distinguished using DNA microsatellites, which is in agreement with the hypothesis that it is not the parasite but the host that adapted itself. Our results suggest that the brood pheromone-dependent mite oogenesis is disrupted in resistant hosts. The identified genetic markers have a considerable potential to contribute to a sustainable global apiculture.
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Honey bee predisposition of
resistance to ubiquitous mite
infestations
Bart J. G. Broeckx
1, Lina De Smet2, Tjeerd Blacquière3, Kevin Maebe4, Mikalaï Khalenkow5,
Mario Van Poucke1, Bjorn Dahle6,7, Peter Neumann8, Kim Bach Nguyen9, Guy Smagghe
4,
Dieter Deforce
10, Filip Van Nieuwerburgh
10, Luc Peelman1 & Dirk C. de Graaf
2,5
Host-parasite co-evolution history is lacking when parasites switch to novel hosts. This was the case
for Western honey bees (Apis mellifera) when the ectoparasitic mite, Varroa destructor, switched hosts
from Eastern honey bees (Apis cerana). This mite has since become the most severe biological threat
to A. mellifera worldwide. However, some A. mellifera populations are known to survive infestations,
largely by suppressing mite population growth. One known mechanism is suppressed mite reproduction
(SMR), but the underlying genetics are poorly understood. Here, we take advantage of haploid drones,
originating from one queen from the Netherlands that developed Varroa-resistance, whole exome
sequencing and elastic-net regression to identify genetic variants associated with SMR in resistant
honeybees. An eight variants model predicted 88% of the phenotypes correctly and identied six
risk and two protective variants. Reproducing and non-reproducing mites could not be distinguished
using DNA microsatellites, which is in agreement with the hypothesis that it is not the parasite but the
host that adapted itself. Our results suggest that the brood pheromone-dependent mite oogenesis is
disrupted in resistant hosts. The identied genetic markers have a considerable potential to contribute
to a sustainable global apiculture.
e ubiquitous ectoparasitic mite Varroa destructor, an invasive species from Asia, is the most important bio-
logical driver of global losses of honey bee, Apis mellifera, colonies1. A. mellifera is not the original host however
as this mite occurred rst in the Asian honey bee, Apis cerana1. Whereas A. cerana and V. destructor have a long
history of co-evolution, this is not the case for A. mellifera2. is short period of co-evolution has le A. mellifera
vulnerable2. To avoid colony collapse, treatment with acaricides seemed the best option and this is what mite con-
trol strategies have relied on for more than ve decades. However, scattered observations in aected regions show
that untreated honey bee colonies can survive mite infestations35. is prompted several initiatives aimed at
breeding these V. destructor-tolerant or -resistant bees (VR)68. ‘Tolerance’ is a defence strategy whereby the host
can limit the harm caused by a given parasite burden, whereas ‘resistance’ refers to the ability of the host to limit
the actual parasite burden itself9. Both social and individual traits have been implicated in the defence against
the V. destructor-mite. Varroa sensitive hygiene (VSH) is a social behaviour trait that consists of three compo-
nents (detection, opening and removal of infested and damaged pupae) that are each inherited independently9
and is expressed by the adult worker honey bees. It was formerly called ‘suppressed mite reproduction’ (SMR)
as reproductive mites were eliminated by hygienic behaviour. However, the term SMR is no longer used for the
social, hygienic behaviour as true SMR has been observed in several populations. As such, SMR is currently
dened as a trait where mites fail to produce ospring in honey bee pupae by a not yet identied mechanism. is
1Department of Nutrition, Genetics and Ethology, Ghent University, B-9820, Merelbeke, Belgium. 2Department of
Biochemistry and Microbiology, Ghent University, B-9000, Ghent, Belgium. 3Wageningen University & Research,
6708PB, Wageningen, The Netherlands. 4Department of Plants and Crops, Ghent University, B-9000, Ghent,
Belgium. 5Honeybee Valley, Ghent University, B-9000, Ghent, Belgium. 6Norwegian Beekeepers Association,
NO-2040, Kløfta, Norway. 7Department of Animal and Apicultural Sciences, Norwegian University of Science
and Technology, NO-1432, Ås, Norway. 8Institute of Bee Health, University of Bern, 3097, Bern, Switzerland.
9Department of Functional and Evolutionary Entomology, Gembloux Agro-Bio Tech, University of Liege, B-5030,
Gembloux, Belgium. 10Department of Pharmaceutics, Ghent University, B-9000, Ghent, Belgium. Bart J. G. Broeckx,
Lina De Smet, Luc Peelman and Dirk C. de Graaf contributed equally. Correspondence and requests for materials
should be addressed to B.J.G.B. (email: Bart.Broeckx@ugent.be)
Received: 11 January 2019
Accepted: 7 May 2019
Published: xx xx xxxx
OPEN
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phenomenon of true resistance emerged independently from natural selection on the island Gotland (Sweden5)
and in Avignon (France4), and there are subtle dierences between these two distinct bee populations in how they
succeed in reducing the reproductive success of the V. destructor mites. In more detail, these resistance pheno-
types are delayed oviposition and actual infertility of the mother mites, respectively10. SMR can be expressed both
in worker and drone brood, which are called worker and drone brood resistance (DBR), respectively. Whereas
both types of resistance are benecial, drone brood has a longer pupation time relative to worker brood, which
gives the V. destructor parasite more time to reproduce. As a consequence, any disturbance of mite reproduction
in drone brood will aect mite population dynamics signicantly9.
Given the burden on the honey bee population, unravelling the genetic architecture of DBR has recently
gained much interest for several reasons11. Firstly, identifying the exact molecular mechanisms might lead to a
better understanding of the host pathogen interaction and new eradication strategies. Secondly, the identication
of genetic markers associated with the phenotype can also immediately be used to selectively breed colonies that
are more resistant.
Here, we investigate the possibility to identify DBR-associated markers by comparing VR and
V. destructor-sensitive drones (VS). One of the diculties when performing these kind of case-control studies is
however the risk of spurious associations that might arise due to population stratication12,13. An ideal solution is
having access to one population where all cases and controls are equally related. As within a colony, it is one queen
that gives rise to many equally related drones, we aimed to create one colony containing both VR and VS drones
at equal frequencies by selective breeding.
When successful, both reproducing and non-reproducing mites will be identified in that colony. While
non-reproducing mites can be a true consequence of host adaptation, which is the desired mechanism,
non-reproduction might however also be a consequence of these mites being a distinct subgroup of V. destructor
parasites lacking the ability to reproduce. While we hypothesize that non-reproduction is a consequence of
host adaptation, this will rst be investigated using microsatellites of both reproducing and non-reproducing
V. destructor mites. Upon conrmation of DBR, the identication of markers can be initiated.
Previously, attempts to identify quantitative trait loci all relied on coarse (with at most 3000 markers) and
indirect (i.e. linkage disequilibrium-based) mapping strategies, oen with inconsistent results1416. e down-
side of these indirect approaches is that they always require further steps downstream (e.g. ne mapping, candi-
date gene sequencing). Contrary to this approach, whole exome sequencing (WES) has the potential to identify
disease-causing mutations directly if they fall within target regions. But even if causal mutations reside outside
target regions, WES variants discovered during sequencing can be used as tagvariants to identify regions asso-
ciated with the phenotype17. Based on previous experiences18,19, we hypothesize that WES will allow mapping at
a far higher resolution relative to previous approaches. As such, we aimed to develop and evaluate the rst WES
design for the honey bee and immediately assessed its performance to identify variants associated with DBR.
While WES is a technological step forward, association studies, including the coarse mapping studies already
performed for DBR, still oen use single variant association tests in their data analysis. Given the phenotype and
in line with previous studies, a multifactorial, complex inheritance mode was however expected for DBR1416. As
this involves the combination of an (unknown) number of loci and single-marker tests only analyze the marginal
eect between a phenotype and a variant, these single-marker tests ignore important information when multiple
variants are associated with complex phenotypes. In this study, we use a novel analysis method, called elastic-net
penalized regression, that allows joint modelling of variants and hypothesize it outperforms the standardly used
single-marker tests.
Finally, to allow a broad use of markers identied in one single colony, it is important that these markers seg-
regate in the general population. We investigate this hypothesis by performing a population screening.
Results
Creating a hybrid VR/VS colony. Virgin queens from honey bee stocks selected for VR were selected from
dierent locations in Europe. ree populations became VR aer several years being le untreated against the
V. destructor-mite in the Netherlands (Amsterdam Water Dunes20), France (Toulouse21) and Norway (Østlandet
Region22). From Belgium, virgin sister queens from a breeding queen with the highest recorded Varroa-index
for Belgium in 2014 were selected. is Varroa -index combines measurements of the V. destructor population
dynamics and hygienic behaviour (further explained in materials and methods)8.
ese queens were next crossed twice with local VS drones (Fig.1). In more detail, this implies single VS
drone articial insemination of VR queens in the parental generation (P), leading to hybrid VR/VS queens in
the rst lial generation (F1). ese F1 hybrid queens carrying both VR- and VS-associated alleles were then
mated naturally with local VS drones. As only the queens give rise to drones in the subsequent generation, this
allows segregation of both VR and VS alleles in the second lial (F2) haploid drone brood generation (Fig.1). To
eliminate the eect of environment as much as possible, all F2 colonies were reared at the same location. All ve
populations (the four VR hybrids and the control strain) were successfully bred and maintained.
Screening for the DBR phenotype. Individual host brood cells were opened at the adequate age and
assessed for V. destructor-mite infestation (as described in more detail in materials and methods). is screen-
ing revealed that the percentage of non-reproducing mites in these four hybrid VR populations was 51.0% (the
Netherlands), 37.5% (France), 31.6% (Norway) and 14.0% (Belgium), respectively, whereas this percentage
was 19.0% in a local VS control strain (TableS1). e colony from the Netherlands had the highest frequency
non-reproducing mites. is was also the only colony where this frequency was signicantly higher relative to
the local control strain (P = 0.01), which indicates that the DBR phenotype from this colony can be transmitted
to subsequent generations, i.e. it is suggestive for a genetically-based resistance mechanism. Furthermore, since
the queens of the F1 generation were VR/VS hybrids, the outcome of F2 DBR typing for this colony was even
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Figure 1. Overview of the breeding experiment and phenotypical screening. (A) Origin of the honey bee
populations used in this study. Numbers 1–4 were selected for Varroa destructor-resistance; number 5 was
the Varroa-sensitive control: 1. Østlanded Region, Norway; 2. Amsterdam Water Dunes, e Netherlands;
3. Kapellen, Belgium; 4. Toulouse, France; 5. Ghent, Belgium. (B) Crossing scheme to obtain hybrid Varroa-
resistance/Varroa -sensitive colonies. For one hypothetical locus associated with the phenotype, the allele
associated with drone brood resistance (DBR) is coloured green, while the opposite (undesired) allele is
coloured blue. e resulting F2 drones were phenotyped for DBR. In reality, the situation is more complex as
several variants, as mentioned in Table1, were found to be associated with DBR. P = parentalis; F = lialis;
Q = queen; D = drone. (C) Outcome of the screening for the DBR phenotype in the dierent crossed
populations. is graph depicts the percentage of non-reproducing mites for each of the 5 colonies. To assess
whether this DBR phenotype segregated at signicantly dierent frequencies relative to the control population
(blue), a Fisher exact test was performed. Only for the Amsterdam Water Dunes colony (green), this result was
signicant at the 0.01 level. Due to the very high DBR prevalence, the Amsterdam Water Dunes bee colony was
used in the subsequent exome sequencing. Other p-values can be found in TableS1. *P = 0.01.
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unexpectedly high (expected maximum score was 50%) and corresponds to what can be expected in a Mendelian
mode of inheritance, which makes it ideal for subsequent association studies. As such, this Dutch colony was
selected and used further downstream in the WES part of this study.
Varroa destructor-mite genotyping. In order to verify whether the observed DBR phenomenon is due to
host adaptations and not a parasite-mediated eect, 56 mother mite specimens originating from drone brood cells
with non-reproducing mites (=DBR phenotype) or with reproducing mites were genotyped at 13 microsatellite
loci (TableS2)23. All 13 loci amplied successfully, which led to a total of 23 dierent alleles, varying from one to
eight per locus (TableS2). Per colony, the eective number of alleles ranged from 1.145 to 1.502 (TableS3). e
observed heterozygosity HO values were very low ranging from 0.000 to 0.077. e mean expected heterozygosity
HE was 0.130, with values ranging from 0.075 to 0.186 within each colony. As expected, the inbreeding coecients
were very high (mean: 0.788) demonstrating clear evidence of inbreeding between the V. destructor-mites within
a colony (TableS3). Furthermore, no signicant dierences in these genetic parameters were found between
reproducing and non-reproducing mites from a colony (Paired t-tests, df = 7, P > 0.05), nor when comparing all
reproducing and non-reproducing mites (Independent t-tests, df = 6, P > 0.05). e Evanno method identied
ΔK = 2, which implies that K (or the number of populations) equals two or one (as ΔK cannot be used to distin-
guish those two values)24. Based on the results from the individual mites, K was set to one as each mite belonged
for approximately 50% to both groups (Fig.2). is implies that these mites could not be genetically distinguished
from each other. Based on these results, the observed DBR seems to represent the real and desired genetic resist-
ance phenotype and is not a consequence of dierences between mites that might mimic it.
Whole exome sequencing. To identify genetic variants associated with the DBR phenotype, a WES design
targeting all exons of the A. mellifera genome (Amel.4.5)25 was developed. is design contained 26,184,643 base
pairs (bp) divided over 81,571 regions. Sixty-four drones (32 DBR positive; 32 DBR negative), originating from
the colony from the Netherlands (i.e. from the colony that diered signicantly from the local control strain
in terms of the number of non-reproducing mites), were selected and Illumina sequenced. In these drones, a
Scaold Location Gene G ene name Variant RNA level Protein level Eect β% Present (n)
Group1.41 909712 GB54921 mucin-12 isoform X1 T > C GB54921-RA:r.144 A>GGB54921-PA:p.
A47 = (or Ala47=)Silent 0.23 91% (42/46)
Group1.41 909762 GB54921 mucin-12 isoform X1 C > T GB54921-RA:r.94 G>AGB54921-PA:p.
V32I (or Val32Ile) Missense 0.22 89% (41/46)
Group10.23 545027 GB48382 solute carrier family 22
member 21 C > T GB48382-RA:r.987 G>AGB48382-PA:p.
A328 = (or Ala328=)Silent 0.06 78% (36/46)
Group15.14 757132 GB50526 sodium-coupled
monocarboxylate
transporter 1 C > T GB50526-RA:r.1662G>AGB50526-PA:p.
P554 = (or Pro554=)Silent 0.15 9% (4/46)
Group15.19 133198 GB50114 dynein beta chain, ciliary T > C GB50114-RA:r.1662A>GGB50114-PA:p.
P3167 = (or Pro3167=)Silent 0.26 73% (34/46)
Group3.15 494900 GB47018 uncharacterized protein
LOC724886 isoform X2 G > A GB47018-RA:r.1824C>UGB47018-PA:p.
L608 = (or Leu608=)Silent 0.30 87% (40/46)
Group9.12 805359 GB53345 uncharacterized protein
LOC100578770 T > C GB53345-RA:r.37 A>GG GB53345-PA:p.
M13V (or Met13Val) Missense 0.02 98%(45/46)
Group9.12 888963 GB53340 spectrin beta chain
isoform X1 A > C GB53340-RA:r.4143U>GGB53340-PA:p.
V1381 = (or Val1381=)Silent 0.09 96% (44/46)
Table 1. Variant description together with the eect size (β) and allelic frequency analysis giving the number of
colonies where the mutations were found (from the total of 46 colonies that were sampled). e intercept of the
model equals 0.39 (model settings: α = 0.9, λ = 0.18).
Figure 2. Bayesian clustering of the Varroa destructor-mites originating from eight beehives. Each vertical line
stands for an individual specimen, while the colors are indicative for the proportion a specimen belongs to a
certain group.
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median coverage of 64.5x was obtained, while >97% of the 26 Mb target bp pairs were covered at 10x (Fig.S2;
TableS4). In terms of regions, the median number of regions that were entirely sequenced at 10x was 91.3%
(Fig.S3; TableS4). e sequencing reproducibility was high, with close to 75% of the regions entirely sequenced
in all samples. Similar to other WES designs, we found that sequencing performance was inversely correlated with
target region length and low complexity/highly repetitive nucleotide composition, whereas GC nucleotide com-
position of a target region and quality of the target region (reected by the proportion of ambiguous nucleotides
in the target region) aected sequencing performance far less (Fig.S4; TableS5)19. e aforementioned results
demonstrate that the novel WES design outperforms several WES designs in other species18,19.
Next, we looked at the number of variants that can be used for the association study. Overall, more than
140,000 variants with a call rate of at least 99% were discovered inside the exons. Both in terms of the number
of markers (50 times higher), as with respect to the resolution (the median and mean distance between markers
were 71 bp (rst quartile: 18 bp – third quartile: 342 bp) and 1562 bp, respectively (Fig.S5)), this is by far the most
dense study on DBR conducted up until now1416.
Single-marker tests and elastic-net penalized regression. Whereas single-marker tests are most
commonly used, previous studies on this phenotype have shown that these tests oen lack the necessary power
to obtain signicant results15,16. e same result was observed here as no single variant reached genome-wide sig-
nicance (Fig.S6). is was no surprise as for the proposed complex inheritance of the phenotype, single-marker
tests are far from ideal as these tests only analyze the eect of an individual variant on a phenotype, while ignoring
all the others2629.
A potential solution is to model all variants jointly. In genetic studies, including the one presented here,
there are however additional complications that have to be taken into account2630. Firstly, the number of vari-
ants (=parameters p) oen exceed the sample size n by far (oen referred to as “the small n, large p” problem).
Secondly, both due to the high dimensionality (i.e. the large number of variants) as the actual physical linkage
between variants, dierent variants can be correlated with each other, leading to the multicollinearity problem.
Where standard multiple regression cannot deal with these issues, the so-called elastic-net penalized regres-
sion that combines the strengths of LASSO in terms of parameter selection and ridge regression to deal with
correlated variants, solves all these problems2628. Based on the combination of cross-validation and stringent
cut-os, elastic-nets have shown to control the number of false positives (oen perfect, i.e. no single false positive
result) while at the same time identifying a large number of true associated variants28. In this study, the same strict
methodology was used, which resulted in eight variants (eight SNPs) in seven dierent genes that were found to
be associated with the DBR phenotype (Table1). From these eight mutations, two were missense, altering the
amino-acid composition, while the remaining six were silent mutations. Altogether, this eight variants model
predicted 88% of the initial sixty-four phenotypes correctly (56/64) (Table2) and identied six risk (i.e. associ-
ated with a lower resistance to V. destructor mites) and two protective (i.e. associated with a higher resistance to
V. destructor mites) variants.
So far, previous (coarse) mapping studies on DBR in the Swedish bee population identied loci at chromo-
some four, seven and nine15 and the chromosomes two, three and een, respectively16. In our study, SNPs at
chromosome nine and chromosome een were retained in the nal model. While we believe this substantiates
the results found in this study, at the same time, it is clear that, in line with the (partially diering) DBR pheno-
type, both populations (the Swedish one from the previous studies versus our Dutch population from this study)
also developed a unique way of dealing with the same parasite.
Allelic frequency analysis in the Belgian honey bee population. As a nal step, a stratied sampling
strategy was used to evaluate the allelic frequencies of the previously identied associated variants in the general
bee population in Belgium. Sanger sequencing, each time two bees per colony for a total of 46 colonies, revealed
a widespread distribution of all variants throughout the bee colonies (Table1). is indicates that these variants
are not colony specic, which facilitates their use in centrally coordinated population-wide selection programs.
Furthermore, on average, the risk mutations were found in more colonies (89%; 41/46 colonies) relative to the
protective mutations (43%; 20/46 colonies), which is no surprise given the widespread V. destructor-sensitivity in
the Belgian honey bee population. Finally, in agreement with the expectations for complex diseases, there was a
clear negative correlation (Spearmans correlation coecient = 0.38) between the prevalence of the mutations
in the population and the eect size (i.e. the absolute magnitude of the β coecients of the dierent variants as
depicted in Table1)31.
Hypothesized biological involvement of the identied genes. As mentioned and in agreement with
literature, the DBR phenotype was expected to segregate in a complex manner, requiring the involvement of sev-
eral genes1416. In our study, variants in seven genes (i.e. mucin-12 isoform X1, solute carrier family 22 member
Model
Truth
Control Aected
Control 29 5
Aected 3 27
Table 2. Contingency table comparing the predicted phenotypes based on the eight variant model (“Model”)
relative to the observed phenotypes (“Truth”) of the 64 drones used in the whole exome sequencing experiment.
Fiy-six out of 64 drones were correctly classied (88%).
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22, sodium-coupled monocarboxylate transporter-1, dynein beta chain, spectrin beta chain isoform X1 and two
uncharacterized proteins; Table1) were found and both synonymous and non-synonymous variants were iden-
tied. It goes without saying that these variants can both be actual phenotype-causing variants or might just be
markers associated with it. While traditionally non-synonymous mutations are oen focused on, recent studies,
however, strongly support the potential role that synonymous mutations on (complex) phenotypes might have32
34. In addition, the involvement of the dynein beta chain in the DBR phenotype was remarkable, as it represents
a cytoskeletal motor protein involved in intracellular retrograde transport along the microtubules of eukaryotic
cilia and agella35. Among insects, cilia/agella are present in the sperm tail (agellar) and in mechano- and che-
mosensory neurons (ciliary) only36,37. Moreover, the ciliary form of dynein that we identied here can exclusively
be related with sensory functions of the honey bee antenna, most probably through its olfactory neurons.
In honey bees, brood care is secured through a few behavioural sequences (feeding, brood cell capping, ther-
moregulation) that are initiated by brood pheromones38. e sensing of these cuticular hydrocarbon pheromones
occurs on the insects’ antennae, more in particularly by specialized olfactory sensillae39. It has been demon-
strated that the V. destructor-mite synchronizes its reproduction with the ontogenic development of the honeybee
larvae and that the mites oogenesis is triggered by volatiles of the larval cuticle40,41. We hypothesize that the
DBR phenotype was obtained by two phenomenons. Firstly, the variant of the dynein beta chain found in DBR
positive bee colonies causes a better pheromone sensing by an improved intracellular transport in the olfactory
neurons. Secondly, the other variants cause a diminished production of the cuticular hydrocarbon brood pher-
omones by a reduced general or tissue-targeted (integumentum) metabolism. Here, the dierent transporters
might play an important role42. Consequently, the pheromone release falls to a level that is no longer able to
initiate oogenesis in the mite, although it permits normal chemical communication - and thus brood care - by
the adult bees. e hypothesis links several of the genes from which a variant is associated with the DBR phe-
notype, but so far any experimental evidence is lacking. However, a similar strong involvement of the olfactory
sensing has been demonstrated by transcriptome studies4345 of the VSH phenotype that renders bees resistant to
V. destructor-mite infestations by hygienic behaviour. In that case again intracellular transport and vesicle traf-
cking in the antennae is one of the underlying biological mechanisms44,46. In addition, in the Gotland VR bees
a glucose-methanol-choline oxidoreductase was found to be putatively involved in changing volatiles emitted by
the bee larvae14.
Discussion
Selection for more resilient bees oers a sustainable solution for the main driver of global honey bee colony losses,
i.e. the infestation by the ectoparasitic V. destructor mite. Although the voluntary denial of any treatment has
already led several times to V. destructor-tolerant or -resistant bees, this approach is still dicult for the average
beekeeper because of the high losses that may go with it. at is why unravelling how natural selection shapes
resilient honey bee populations at both the phenotypic and genetic level, represents a crucial lever to support the
classical selection programs through breeding value estimation or marker-assisted selection. e present work
developed novel state-of-the-art methodologies for honey bees to discover genetic variants associated with the
given trait.
Firstly, we have developed a WES design for the honey bee. In terms of performance, this newly developed
WES design excels in comparison with WES designs from other species19. Furthermore, relative to for example
RESTseq16,47 and the 44 K GWAS SNP assay48, far more variants are found, which should improve the identica-
tion of disease-associated variants, while relative to whole genome sequencing, it is a more cost-ecient approach
until sequencing prices drop further. Focusing on exons, it is important to stress that variants outside the target
regions are potentially missed, especially for those located at a distance of exonic variants. Overall however, this
WES design fullled our expectations and we are condent that, for the time being, it will be a valuable tool for
further genetic studies of honey bees.
Secondly, we evaluated elastic-net regression and demonstrated that it outperforms single-marker association
tests. Combined with WES, it was capable of discovering variants associated with complex traits. To our knowl-
edge, this is the rst time that either WES or elastic-nets have been used in the honey bee.
In our approach, we specically chose to sequence drones derived from one colony for two main reasons.
By sequencing equally related bees, the problem of spurious associations due to population stratication was
avoided. In addition, while SMR has been reported to have developed naturally in several populations, the under-
lying mechanism is not entirely the same in every population10. As such, there is a high risk that the genetic con-
tributors dier as well, which, in turn, would have reduced the power to detect an association. is is avoided by
focusing on one population. A potential downside is that this might lead to a reduced generalizability, i.e. that (at
least) some variants associated with the phenotype are unique for that specic population. One option for future
studies is thus to redo this experiment by combining drones from dierent populations.
Also in terms of the population study, we deliberately chose to look for the variants in the Belgian honey bee
population. e main reason was a direct evaluation of the potential applicability of the results in that population.
Other options that might be pursued in the future are for example looking for the variants in public datasets49,50
and other VR populations to investigate how widespread these mutations are.
With respect to the phenotype, the present study describes a very promising trait, i.e. DBR, in an already
well-studied resilient honey bee population that was established in the Amsterdam Water Dunes in the
Netherlands by natural selection20. V. destructor has a strong preference for drone brood51, making drone
brood removal or drone brood ‘cutting’ a common intervention in beekeeping practice and part of a biotech-
nical V. destructor control strategy. As the post-capping developmental stage of drones lasts two days longer
than for worker bees (14 days instead of 12 days), more mature V. destructor-mites emerge from a drone brood
cell. e Cape honey bee (A. m. capensis) has an innate resistance against the V. destructor-mite by shortening
the post-capping developmental time to only 9 days on average52. e failure of mite reproduction in the DBR
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phenotype has a similar eect on the V. destructor population dynamics, and thus is an alternative way to get
resistance against the mite by avoiding massive mite reproduction in the male brood. Moreover, the costs of this
trait for the colony is relatively low when compared to hygienic behaviour, where up to 32.4% of the pupae are
removed from the brood53. As the identied variants associated with DBR were widespread in the natural bee
population, we believe the road to marker assisted selection is open.
Material and Methods
Honey bee populations and crossing. Virgin queens from honey bee stocks selected for VR originated
from dierent locations in Europe. From Belgium, we also took virgin sister queens from breeding queen with
code 57-584-11612-2012 from Guido Haagdorens, a participant of the Belgian branch of the Beebreed program
(Länderinstitut für Bienenkunde Hohen Neuendorf, Germany). is breeding queen was chosen because it gave
the highest recorded Varroa-index for Belgium in 2014. e Beebreed program relies on breeding value esti-
mation and the Varroa-index combines measurements of the V. destructor population dynamics and hygienic
behaviour8. It requires estimations of the daily mite-fall in spring, the size of the phoretic mite population by the
powdered sugar method in summer and the clearance of dead pupae by the pin-test54.
Six virgin queens of each honey bee stock were crossed twice with local VS drones. In the parental generation,
we performed articial insemination at a queen age of 9–10 days under CO2 treatment (this treatment was also
given 1 day earlier for 6 min). For each insemination fresh semen was taken from another drone from the same
colony (code EXP10) and injected in the queen’s main oviduct (1.5 µl semen + 0.5 µl dilution buer; dilution
buer contained 0.2 M NaCl, 5 mM glucose monohydrate, 0.67 mM L-lysine, 0.57 mM L-arginine, 0.68 mM
L-glutaminic acid, 0.02 M Trisma HCl, 0.03 M Trisma base and 2.5 mg/ml dihydrostreptomycine). Ten to twelve
days aer introducing the queens in 3-frame hives, we checked for oviposition and nine days later for brood cap-
ping. At that moment and when insemination was proven to be successful, we started queen rearing.
From each genetic stock only one colony was chosen for queen rearing. At least four one-day-old larvae from
each colony were graed to articial queen cells and then transferred to a cell building colony. Once the queen
cells were sealed they were transferred to an incubator. e emerging queens were introduced in small mating
nuclei colonies, that were treated with oxalic acid (V. destructor-treatment) prior to queen introduction. In this
rst lial generation virgin queens were allowed to mate naturally. Again we checked for oviposition and cell
capping. A second mite treament was done just before winter.
In spring, all colonies were moved to the same apiary (campus Sterre) in order to do the testing in the same
environment. Drone brood frames were introduced in two colonies per genetic stock. However, only one colony
was used for determination of the phenotype.
Determination of the phenotype. When drone brood cells were capped, the frames were transferred to
an incubator and kept at 34 °C. By doing so, we could avoid that our measurements were inuenced by hygienic
behaviour of the adult bees. Ten days later the drone brood and the V. destructor-mites were killed by freezing.
is simplies the determination of the phenotype as mites will no more escape when drone brood cells are
opened. We examined drone brood cells for the presence of a mother mite only (non-reproducing) or a mother
mite with her progeny (reproducing) in the presence of red-eyed drone pupae. Both drone pupae and mother
mites were stored at 80 °C for subsequent WES and V. destructor genotyping of selected samples, respectively.
A Fisher exact test was used to compare the frequency of the VR and the VS drones in the colonies derived from
the VR populations relative to the local VS control population. Signicance was set at α 0.05/4 (Bonferroni
correction for multiple testing). is was performed in R v3.4.2 (“Short Summer”).
Varroa destructor-mite genotyping. Fiy-six V. destructor specimens were genotyped with 13 micro-
satellite loci which already have proven to give reliable signals23 (TableS2). Ten loci were originally developed
for V. destructor: seven loci (VD146, VD163, VD001, VD151, VD015, VD112 and VD114) by Cornman et al.55,
and three loci (VD305, VD306 and VD307) by Solignac et al.56. e nal 3 loci (VJ275, VJ294, and VJ292) were
developed for V. jacobsoni57.
Individual DNA was obtained from whole V. destructor mites following a Chelex (InstaGene Matrix, BioRad)
DNA extraction method as described in Maebe et al.58. In short, 200 µl Chelex and 10 µl proteinase K was added
to an individual mite (sliced with a sterile blade) and incubated for 2 h at 56 °C. Aer a second incubation step of
15 min at 96 °C, the supernatants of 180 µl (DNA) was frozen in 20 °C until further use.
By multiplex PCR, these 13 microsatellites were amplified by HotstarTAq DNA Polymerase (QIAgen,
Belgium) in a total volume of 10 µl. e PCR mix consisted out of 1 µl template DNA, PCR buer (1x), 0.2 µM
dNTP’s, 0.1 µM forward primer, 0.5 µM reverse primer, 0.5 µM dierent labelled forward M13 primers and 1 unit
Taq polymerase. Fluorescent labelling of the PCR products was done using a tailed-primer approach59. In this
approach a universal M13-primer (=‘t a il’, 5-GAGTTTTCCCAGTCACGAC-3) was coupled to a VIC, 6-FAM,
PET or NED uorescent label (TableS2). To allow the incorporation of the tail during PCR, the same sequence as
the tail was built-in at the 5-end of all forward primers (see also60). Furthermore, the normal annealing temper-
ature of 60 °C was decreased with 2 °C to 58 °C. e other PCR conditions were: a rst denaturation step at 95 °C
for 15 min, then 30 cycles of 30 s denaturation at 95 °C, 30 s annealing at 58 °C and 30 s extension at 72 °C, followed
by a nal extension step of 10 min at 72 °C. Visualization of the PCR products was done by capillary electro-
phoreses on an ABI3730xl sequencer (Applied Biosystems) with help of a 500 LIZ standard (Genescan, Applied
Biosystems). e fragments were scored manually with the Peak Scanner v1.0 soware (Applied Biosystems). As
a quality control, the amplication of 16 randomly chosen samples was repeated.
For the mites originating from the same beehive, several genetic parameters were determined with the pro-
gram GenAlEx 6.561 including: Nei’s unbiased expected heterozygosity (HE), the observed heterozygosity (HO)
and the eective number of alleles (Ne) as parameters of genetic diversity, and also the inbreeding coecient (Fis).
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Paired t-tests were performed over all loci to search for dierences between reproducing and non-reproducing
mites within a colony, while possible dierences over all colonies between reproducing and non-reproducing
mites were tested with independent t-tests. ese tests were performed in SPSS.
Hence, the soware Structure v2.3.362 was used to perform a Bayesian approach to determine the number of
populations (or groups) within the dataset. In this analysis, the number of populations (K) was estimated from
1 to 8, and this was repeated 9 times. Each K-value was calculated with a burn in of 1,000,000 iterations and
500,000 MCMC data collecting steps. Hence, the free online program Structure Harvester v0.6.9363, was used to
determine the best value of K, and the program Distruct v1.1 soware was used for graphical visualization of the
population structure.
Development of exome design. e reference genome (Amel_4.5_scaolds.fa) and corresponding anno-
tation (amel_OGSv3.2.g3.gz) were obtained from the Hymenoptera Genome Database64. A bed le containing
all the exons (including the UTRs) was created with the bedr (v1.0.4) and seqinr (v3.4–5) R-packages. Our design
was processed by the Roche Nimblegen custom design group (Madison, USA). Using an SSAHA algorithm, cap-
turing baits were developed based on our design and the reference genome. Design settings for the baits allowed
ve or fewer single-base insertions, deletions or substitutions between the baits and the genome. Each bait itself
was allowed to match up to 20 locations in the genome. Regions under 100 bp were padded to 100 bp to increase
capturing eciency. Aer approval, the baits were generated and provided as SeqCap Developer Library.
DNA extraction. Sixty-four pupae (32 coming from a drone brood cell with a non-reproducing mite; 32
with a reproducing mite), all derived from one colony, were subsequently used for DNA extraction. Each pupae
was individually homogenized in a total of 1 ml RLTPlus buer by mechanical agitation in a TissueLyser for 90 s
at 30 Hz, in the presence of 4 metal beads and glass beads. One third of the sample was used to isolate DNA and
RNA with the ALLPREP DNA/RNA isolation kit from Qiagen following the manufacturers recommendations.
e RNA was eluated in 100 µl RNAse free water while the DNA was eluated in 80 µl EB buer.
Sample preparation and sequencing. Following DNA extraction, a picogreen assay was performed and
1 μg of every sample was subsequently treated with RNase I (ermoFisher Scientic). e DNA was next frag-
mented on a Covaris S2 System in a 130 μl volume (aim: 300 bp fragments, settings: duty cycle: 10%, intensity: 4,
cycles per burst: 200, time: 80 s). Depending on the yield aer DNA-extraction, between 500 ng and 1 μg of the
fragmented DNA was used as input for the library preparation. Samples were end repaired, A-tailed and ligated
with TruSeq adapters using the reagents from the KAPA library prep kit according to the manufacturer’s protocol.
Size selection was performed on a 2% E-Gel (Invitrogen Life Technologies) (G4010-02), fragments were selected
with an insert size around 200–700 bp. ereaer, the pre-capture LM-PCR was performed on the samples for 8
cycles as prescribed in the SeqCap EZ library protocol. e concentration of each PCR product was determined
using Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies). Sixteen times four samples were equimol-
arly pooled to obtain a total DNA input of 1250 ng. e pooled library was hybridized for 19 hours and 30 minutes
with the baits (SeqCap Developer Library). e hybridized library was washed and the captured and pooled
DNA was recovered. Aer a nal amplication (LM-PCR, 13 cycles), the quality of the library was checked using
the High Sensitivity DNA chip (Agilent). To check the fold enrichment aer capturing, a qPCR is performed
as a quality control step before sequencing. e used primers are shown in TableS6. An additional qPCR was
performed to determine the quantity of the library to ensure optimal cluster densities. Two times twenty-four
samples and one time sixteen samples were sequenced per lane on the NextSeq 500 PE 75 bp.
Sequencing data-analysis. The reads were aligned to the reference genome (Amel_4.5) using BWA
v0.7.1565. Duplicate reads were marked with Picard tools v2.1.1. Using the GATK v3.8-0, variants were called
according to the GATK Best Practices66.
Variant ltering. From the total list of putative variants, only those were retained that 1/passed the “hard”
quality lter suggested from the GATK Best Practices, 2/that had at least 2 dierent alleles segregating in the
population, 3/fell within the target regions and 4/with a call rate of 99%. Filtering was performed with VCFtools
v0.1.14 and custom R-scripts67. Retrieval of the gene function was done by BLAST searching.
Single-marker tests and elastic-net penalized regression. For the single marker tests, a Fisher exact
test was conducted for each variant. e Bonferroni correction for multiple testing was applied. Elastic-nets
penalized regression was next performed with the glmnet v2.0–12 R-package in R v3.4.2 (“Short Summer”)27.
For the model selection, potential parameters were the 140,151 variants and no covariates were added. To obtain
the optimal lambda (i.e. the penalty) and alpha (i.e. the balance between more “lasso”-like and “ridge”-regression
like behaviour), a leave-one-out cross-validation was performed for alpha ranging from 0 to 1 (in steps of 0.1).
Lambda was set at the stringent MSE+1SE threshold28.
Allelic frequency analysis in the Belgian honey bee population. Worker bees were sampled at the
apiaries from breeders involved in the Flemish beekeeping program. irty breeders sampled only a single col-
ony; two others sampled four and twelve colonies, respectively. e apiaries are distributed throughout Flanders,
the northern part of Belgium. Two individual worker bees from 46 dierent colonies were sampled from dierent
apiaries. Each single bee was homogenized in 0.5 ml of 100 mM NaCl; 20 mM Tris-HCl, pH 8; 25 mM EDTA, pH
8; 0.5% SDS by mechanical agitation in a TissueLyser for 90 s at 30 Hz, in the presence of metal beads and glass
beads. Aer homogenization 20 µl/ml Proteinase K (20 mg/ml) was added and incubated for 1 hour at 37 °C. First,
an equal volume of phenol:chloroform was added and centrifuged at 12,000 g for 10 min at 4 °C. e supernatant
was then extracted with an equal volume of chloroform:isoamyl alcohol (24:1) and centrifuged at 12,000 g for
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10 min at 4 °C. e DNA was precipitated using two volumes of ethanol and centrifuged at 12,000 g for 30 min at
4 °C. e precipitated DNA was nally washed with 0.5 ml 70% ethanol. e DNA was dissolved in 100 µl DNase/
RNase free water.
Primer pairs were designed with Primer-BLAST68. Primers were chosen in regions that were free of second-
ary structures (Mfold) and are listed in TableS769. PCR amplicons were analyzed via agarose gel electropho-
resis. Sequencing reactions were performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied
Biosystems, Foster City, CA, USA) with the individual PCR primers as sequencing primers and run at Eurons
Genomics (Ebersberg, Germany). Sequence analysis was performed with BioEdit v7.2.6.
e allele frequency was determined at the colony level, followed by a comparison of the average allele fre-
quency for risk and protective alleles and an evaluation of the correlation between the eect size and the allele
frequency (Spearman correlation).
Data Availability
All the data is available upon request.
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Acknowledgements
This work was supported by FOD Volksgezondheid, Veiligheid van de Voedselketen en Leefmilieu (Cel
Contractueel Onderzoek) RT13/4. e computational resources (Stevin Supercomputer Infrastructure) used for
the analysis of the WES data were provided by the VSC (Flemish Supercomputer Center), funded by Ghent
University, FWO and the Flemish Government – department EWI. We would like to thank Sarah De Keulenaer,
Ellen De Meester and Sylvie Decraene from NXT GNT (http://www.nxtgnt.com/) for performing the library
preparation and sequencing, Dominique Vander Donckt and Linda Impe for performing the PCR and Sanger
sequencing in the population study, Ruben Van Gansbeke for the administrative support, Marleen Brunain for
DBR phenotyping and all beekeepers who participated in this study, especially Guido Haagdorens. DCdG and
his team are grateful for the availability of locations for experimental apiaries oered by the municipality of
Merelbeke and the faculties of Sciences, Veterinary Medicine, Economics and Business Administration, and Arts
and Philosophy of Ghent University.
Author Contributions
D.C.d.G., L.P., B.J.G.B., L.D.S. conceived and designed the experiments. B.J.G.B., L.D.S., T.B., K.M., M.K., M.V.P.,
B.D., P.N., K.B.N., G.S., D.D., F.V.N., L.P. and D.C.d.G. performed the experiments, analyzed and interpreted the
data. All authors contributed, improved and approved the paper.
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... One of the key traits linked with varroa resistance is suppressed mite reproduction (SMR) which describes the nonreproduction of varroa mites in honey bee drone pupae cells [7,13]. The precise mechanisms behind SMR are still not fully understood. ...
... The precise mechanisms behind SMR are still not fully understood. Possible hypotheses are a suppression of the varroa reproduction cycle by lower levels of juvenile hormone [14], alterations in a gene from the ecdysone pathway [15] or diminished production of the brood pheromone [13]. Varroa reproduction may also be in uenced by variations in the genotype of the mite or in the physiological status of the brood cell [16,17]. ...
... Since the publication of the honey bee genome in 2006 [20] many studies identi ed quantitative trait loci or single nucleotide variants (SNV) associated with different varroa resistance traits [19]. For SMR, eight single-nucleotide variants were discovered by Broeckx and colleagues [13] using a novel whole exome sequencing design. Of the variants discovered six were risk associated variants and two were protective variants. ...
Preprint
Full-text available
Background: The varroa mite is one of the main causes of honey bee mortality. An important mechanism by which honey bees increase their resistance against this mite is the expression of suppressed mite reproduction. This trait describes the physiological inability of mites to produce viable offspring and was found associated with eight genomic variants in previous research. Results: This paper presents the development and validation of high-throughput qPCR assays with dual-labeled probes for discriminating these eight single-nucleotide variants. Within the sequenced samples, additional variants were detected in the primer/probe binding sites in four out of the eight variants of interest. As for two of these the additional variants interfered with the genotyping outcome supplementary primers and/or probes were developed. Inclusion of these primers and probes in the assay mixes allowed for the correct genotyping of all eight variants of interest. Conclusion: These outcomes underline the importance of checking for interfering variants in designing qPCR assays. Ultimately, the availability of this assay allows genotyping for the suppressed mite reproduction trait and paves the way for marker assisted selection in breeding programs.
... One of the key traits linked with varroa resistance is suppressed mite reproduction (SMR) which describes the non-reproduction of varroa mites in honey bee drone pupae cells [7,13]. The precise mechanisms behind SMR are still not fully understood. ...
... The precise mechanisms behind SMR are still not fully understood. Possible hypotheses are a suppression of the varroa reproduction cycle by lower levels of juvenile hormone [14], alterations in a gene from the ecdysone pathway [15] or diminished production of the brood pheromone [13]. Varroa reproduction may also be influenced by variations in the genotype of the mite or in the physiological status of the brood cell [16,17]. ...
... Since the publication of the honey bee genome in 2006 [20] many studies identified quantitative trait loci or single nucleotide variants (SNV) associated with different varroa resistance traits [19]. For SMR, eight single-nucleotide variants were discovered by Broeckx and colleagues [13] using a novel whole exome sequencing design. Of the variants discovered six were risk associated variants and two were protective variants. ...
Article
Full-text available
Background The varroa mite is one of the main causes of honey bee mortality. An important mechanism by which honey bees increase their resistance against this mite is the expression of suppressed mite reproduction. This trait describes the physiological inability of mites to produce viable offspring and was found associated with eight genomic variants in previous research. Results This paper presents the development and validation of high-throughput qPCR assays with dual-labeled probes for discriminating these eight single-nucleotide variants. Amplicon sequences used for assay validation revealed additional variants in the primer/probe binding sites in four out of the eight assays. As for two of these the additional variants interfered with the genotyping outcome supplementary primers and/or probes were developed. Inclusion of these primers and probes in the assay mixes allowed for the correct genotyping of all eight variants of interest within our bee population. Conclusion These outcomes underline the importance of checking for interfering variants in designing qPCR assays. Ultimately, the availability of this assay allows genotyping for the suppressed mite reproduction trait and paves the way for marker assisted selection in breeding programs.
... This can be achieved by repeating at multiple points in time fully-crossed experiments involving honey bees and mites, which coevolved under different selection scenarios. Here, we considered the case of the Dutch bees from the AWD selection [20,[39][40][41]. This is a population selected for survival in the absence of acaricide treatments following a Darwinian black box beekeeping protocol [42]. ...
... It thus seems safe to conclude that the confirmed reduced reproductive success of mites in the AWD bees can also not be explained by the removal of infested brood. Instead, other traits of resistance must explain the observed patterns of mite infestations, e.g., drone pupae of AWD bees seem to interfere with mite oogenesis [39]. A reduced mite oogenesis may also occur in infested worker brood because our data show that the number of offspring produced by both mite groups infesting selected bees was significantly lower than that of treated-associated mites infesting their hosts (Figure 3). ...
... However, the results of the present study show that the AWD bees removed significantly less infested brood cells in 2018 compared to 2015. Since non-removed pupae in VSH colonies are able to suppress mite reproduction [52] and worker and drone pupae of AWD bees may interfere with mite oogenesis ( [39]; our data), less costly brood resistance traits might have been favored [53,54]. In honey bee populations in which the removal of infested brood appears to be a major trait of resistance, its expression is known to be dependent on seasonal conditions and the availability of environmental resources (i.e., nectar) [38,55], as well as the proportion of infested brood cells [56], which results in the need to perform multiple measurements to reliably assess VSH expression [57]. ...
Article
Full-text available
Co-evolution is a major driving force shaping the outcome of host-parasite interactions over time. After host shifts, the lack of co-evolution can have a drastic impact on novel host populations. Nevertheless, it is known that Western honey bee (Apismellifera) populations can cope with host-shifted ectoparasitic mites (Varroa destructor) by means of natural selection. However, adaptive phenotypic traits of the parasites and temporal variations in host resistance behavior are poorly understood. Here, we show that mites made adaptive shifts in reproductive strategy when associated with resistant hosts and that host resistance traits can change over time. In a fully-crossed field experiment, worker brood cells of local adapted and non-adapted (control) A.mellifera host colonies were infested with mites originating from both types of host colonies. Then, mite reproduction as well as recapping of cells and removal of infested brood (i.e., Varroa Sensitive Hygiene, VSH) by host workers were investigated and compared to data from the same groups of host colonies three years earlier. The data suggest adaptive shifts in mite reproductive strategies, because mites from adapted hosts have higher probabilities of reproduction, but lower fecundity, when infesting their associated hosts than mites in treated colonies. The results confirm that adapted hosts can reduce mite reproductive success. However, neither recapping of cells nor VSH were significantly expressed, even though the latter was significantly expressed in this adapted population three years earlier. This suggests temporal variation in the expression of adaptive host traits. It also appears as if mechanisms not investigated here were responsible for the reduced mite reproduction in the adapted hosts. In conclusion, a holistic view including mite adaptations and studies of the same parasite/host populations over time appears overdue to finally understand the mechanisms enabling survival of V.destructor-infested honey bee host colonies.
... Table S2) of the Apis mellifera nuclear genome were selected for the genotyping by sequencing (GBS) analysis using the AgriSeq platform of Thermo Fisher Scientific Inc. (Waltham, MA, USA). SNPs included in the panel (i) can be useful to differentiate the honey bee subspecies and evolutionary lineages (97 ancestry informative SNPs; 27 ), (ii) are associated with calmness (3 SNPs; 51 ) and gentleness (3 SNPs; 51 ) of honey bees and (iii) are associated with resistance to Varroa destructor (18 SNPs;38,40,52 ). Based on the original positional information provided in the referred manuscripts (see above), location of the SNPs on the latest version of the Apis mellifera reference genome Amel_HAv3.1 (GCF_003254395.2; ...
... Two A. m. siciliana samples were positioned at the border of the C cluster, suggesting again that they might be SNPs associated to calmness, gentleness and resistance to Varroa destructor. The panel also included a total of 24 SNPs that, according to what was previously reported 38,40,51,52 , could be useful to provide genetic information on a few important traits of the managed honey bee colonies. Figure 3 reports a comparison of the average allele frequency of the alternative allele (according to the allele reported in the reference genome, usually associated in these cases to positive characteristics) between several A. mellifera subspecies, as obtained from WGS datasets retrieved from ENA, and the GBS results that we have obtained in all honeycomb samples from A. m. ligustica, the DNA pools from A. m. ligustica and the honey from A. m. siciliana. ...
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Full-text available
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.
... mellifera) in relation to traits that mediate resistance or tolerance to the Varroa destructor mite (172). Specific traits identified in honeybees, including removal of infected brood by worker bees, have likely been under selection since the global spread of V. destructor starting in the 1950s (173), and the genetic basis of some of these traits is beginning to be understood (174). However, parasite resistance in managed honeybee colonies likely occurs through different mechanisms than in wild solitary bees; therefore, we must understand more about adaptation to pathogens in other insects (175). ...
Article
Insects constitute vital components of ecosystems. There is alarming evidence for global declines in insect species diversity, abundance, and biomass caused by anthropogenic drivers such as habitat degradation or loss, agricultural practices, climate change, and environmental pollution. This raises important concerns about human food security and ecosystem functionality and calls for more research to assess insect population trends and identify threatened species and the causes of declines to inform conservation strategies. Analysis of genetic diversity is a powerful tool to address these goals, but so far animal conservation genetics research has focused strongly on endangered vertebrates, devoting less attention to invertebrates, such as insects, that constitute most biodiversity. Insects’ shorter generation times and larger population sizes likely necessitate different analytical methods and management strategies. The availability of high-quality reference genome assemblies enables population genomics to address several key issues. These include precise inference of past demographic fluctuations and recent declines, measurement of genetic load levels, delineation of evolutionarily significant units and cryptic species, and analysis of genetic adaptation to stressors. This enables identification of populations that are particularly vulnerable to future threats, considering their potential to adapt and evolve. We review the application of population genomics to insect conservation and the outlook for averting insect declines. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 11 is February 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Generally, wild populations of domesticated plants and animals are important reservoirs of genetic diversity [71] and this applies to wild and feral honey bees as well [27,39,72]. However, beekeepers are generally not willing to support such a course for economic reasons-expected high colony losses in initial period [43,73], but also the possibility of losing some of the desirable traits of managed bees [23]. For decades, breeding programs selected traits like greater honey production, less aggressive temperament, a lower tendency for swarming, high fecundity of queen, etc. [23,46,74,75]. ...
Article
Full-text available
It is assumed that wild honey bees have become largely extinct across Europe since the 1980s, following the introduction of exotic ectoparasitic mite (Varroa) and the associated spillover of various pathogens. However, several recent studies reported on unmanaged colonies that survived the Varroa mite infestation. Herewith, we present another case of unmanaged, free-living population of honey bees in SE Europe, a rare case of feral bees inhabiting a large and highly populated urban area: Belgrade, the capital of Serbia. We compiled a massive data-set derived from opportunistic citizen science (>1300 records) during the 2011–2017 period and investigated whether these honey bee colonies and the high incidence of swarms could be a result of a stable, self-sustaining feral population (i.e., not of regular inflow of swarms escaping from local managed apiaries), and discussed various explanations for its existence. We also present the possibilities and challenges associated with the detection and effective monitoring of feral/wild honey bees in urban settings, and the role of citizen science in such endeavors. Our results will underpin ongoing initiatives to better understand and support naturally selected resistance mechanisms against the Varroa mite, which should contribute to alleviating current threats and risks to global apiculture and food production security.
... The main reason is probably the genetic plasticity of resistance to Varroa (Traynor et al. 2020), which is also influenced by viruses and the environment. Research studies that identified QTLs, SNPs, and differentially expressed genes gave both exciting new evidence for complex genetic backgrounds as well as disappointingly inconsistent results between the studies (Le Conte et al. 2011;Navajas et al. 2008;Mondet et al. 2015;Behrens et al. 2011;Navajas et al. 2008;Mondet et al. 2015;Guarna et al. 2015;Parker et al. 2012;Gempe et al. 2012;Tsuruda et al. 2012;Arechavaleta-Velasco et al. 2012;Holloway et al. 2013;Spötter et al. 2016;Harpur et al. 2019;Conlon et al. 2019;Broeckx et al. 2019). However, Morfin et al. (2020) found that the expression of a gene associated with grooming behavior, AmNrx-1 (neurexin), was significantly higher in the selected stock (Indiana "mitebiter") than in colonies of unselected Italian bees. ...
Article
Full-text available
The beekeeping sector is facing many challenges. One of the greatest is maintaining healthy colonies that produce high-quality products without any residues of veterinary medicines and with low environmental impact. The main enemy is the ectoparasitic mite Varroa destructor, the most significant honeybee pest and a key factor in high colony losses worldwide. In the previous four decades, three pillars of Varroa control have crystallized to be essential for sustainable management: API technical measures, chemical treatments, and resistant stocks of honey bees. In the long term, the latter is probably the most sustainable as it is a step to self-sustaining populations of feral and managed colonies. We recognize the significance of progress in knowledge of all three pillars to conquer Varroa and of their successful usage in accordance with local and global conditions and capabilities. In this review, we present a possible integration of the components of the three pillars of Varroa control strategies in the light of sustainable beekeeping and provide their linkage to the production of high-quality and safe honeybee products and maintaining healthy colonies.
... VSH would produce SMR, if bees choose not to target cells where the Varroa foundress has laid fewer eggs, leaving only those "rejected" cells to be measured by scientists. There might be changes in the brood pheromonal signals (Broeckx et al., 2019), which the mites rely on almost exclusively to begin their egg-laying cycles (Garrido & Rosenkranz, 2003). A more rapid development of the young bees, i.e. a reduced post-capping period could also contribute to SMR . ...
Article
Full-text available
The invasive parasitic mite, Varroa destructor (Anderson and Trueman), is the major biotic threat to the survival of European honey bees, Apis mellifera L. To improve colony survival against V. destructor, the selection of resistant lineages against this parasite is considered a sustainable solution. Among selected traits, mite fertility and fecundity, often referred to as suppressed mite reproduction are increasingly used in breeding programmes. However, the current literature leaves some gaps in the assessment of the effectiveness of selecting these traits toward achieving resistance. In the population studied here, we show a low repeatability and re-producibility of mite fertility and fecundity phenotypes, as well as a low correlation of these traits with infestation rates of colonies. Phenotyping reliability could neither be improved by increasing the number of worker brood cells screened, nor by screening drone brood, which is highly attractive for the parasite and available early in the season, theoretically allowing a reduction of generation time and thus an acceleration of genetic progress in selected lineages. Our results provide an evaluation of the potential and limitations of selecting on decreased mite reproduction traits to obtain V. destructor-resistant honeybee colonies. To allow for a more precise implementation of such selection and output reporting, we propose a refined nomenclature by introducing the terms of decreased mite reproduction and reduced mite reproduction, depending on the extent of mite reproduction targeted. We also highlight the importance of ensuring accurate phenotyping ahead of initiating long-lasting selection programmes.
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Varroa destructor is arguably the most important threat to Apis mellifera honey bees. Despite the recentness of the invasion of Varroa, A. mellifera colonies naturally resistant to the mite are being observed in a growing number of populations across Europe, South Africa and Brazil. Appearing in concert with this resistance is an increase in the ability of workers to detect mite-infested cells, which is closely associated with the recapping of such cells. However, many non-infested cells are also uncapped and then recapped which would appear to be a waste of time and energy. In this study we looked at the spatial patterns of recapping and its association with Varroa infestation to understand in what way the uncapping of non-infested cells occurs. We found that recapping occurred in clusters consisting of infested cells and their surrounding non-infested cells. This helped explain our finding that a significant positive correlation existed between levels of recapped infested and non-infested cells. Furthermore, we found that bees responded to an artificial increase in the mite infestation level by increasing their recapping behavior. We confirmed that the recapped area of non-infested cells was significantly smaller, relative to the holes made in the infested cells. Given these findings we propose that recapping behavior is stimulated either by a diffuse signal emanating from the infested cell or that cursory checks are conducted in the vicinity of an infested cell.
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Social organisms combat pathogens through individual innate immune responses or through social immunity-allobehaviours that limit pathogen transmission within groups. While we have a relatively detailed understanding of the genetics and evolution of the innate immunity in animals, we know little about social immunity. Addressing this knowledge gap is crucial for understanding how life-history traits influence immunity, and if trade-offs exist between innate and social immunity. Hygienic behaviour in the Western honey bee, Apis mellifera, provides an excellent model for investigating the genetics and evolution of social immunity in animals. This heritable, colony-level behaviour is performed by nurse bees when they detect and remove infected or dead brood from the colony. We sequenced 125 haploid genomes from two artificially selected highly-hygienic populations and a baseline unselected population. Genomic contrasts allowed us to identify a minimum of 73 genes associated with hygienic behaviour. Many genes were within previously mapped genomic loci associated with hygienic behaviour, and were predictive of hygienic behaviour within the unselected population. These genes were often involved in neuronal development and sensory perception in solitary insects. We found that genes associated with hygienic behaviour have evidence of positive selection within honey bees (Apis), supporting the hypothesis that social immunity contributes to fitness. Our results indicate that genes influencing neurobiology and behaviour in solitary insects may have been co-opted to give rise to a novel and adaptive social immune phenotype in honey bees.
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