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.For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. This work was supported by a grant from the CB Dennis British Beekeepers’ Research Trust awarded to MB and DW, and through strategic investment funding to the Roslin Institute from the Biotechnology and Biological Sciences Research Council (BBS/E/D/30002276). MP was supported by a Basque Government grant (IT1233-19)

Population genetic diversity and dynamics of the honey bee brood pathogen Melissococcus plutonius in a region with high prevalence.

European foulbrood (EFB) is a honey bee brood disease caused by the bacterium Melissococcus plutonius. Large-scale EFB outbreaks have been reported in several countries in recent decades, which entail costly sanitation measures of affected apiaries to restrict the spread of this contagious pathogen. To mitigate its impact, a better understanding of the population dynamics of the etiological agent is required. We here used multi-locus sequence typing (MLST) to infer the genetic diversity and geographical distribution of 160M. plutonius isolates collected from EFB symptomatic honey bee colonies seven years apart. Isolates belonged to three clonal complexes (CCs) known worldwide and to 12 sequence types (STs), of which five were novel. Phylogenetic and clustering analyses showed that some of these novel sequence types have likely evolved locally during a period of outbreak, but most disappeared again. We further screened the isolates for melissotoxin A (mtxA), a putative virulence gene. The prevalence of STs in which mtxA was frequent increased over time, suggesting that this gene promotes spread. Despite the increased frequency of this gene in the population, the total number of cases decreased, which could be due to stricter control measures implemented before the second sampling period. Our results provide a better understanding of M. plutonius population dynamics and help identify knowledge gaps that limit efficient control of this emerging disease.This research was funded by the Swiss Federal Food Safety and Veterinary Office grant number 1.12.15, the University of Lausanne and Agroscope. EGH was funded by a BBSRC CASE studentship in partnership with Bee Disease Insurance and the National Bee Unit. GEB was funded jointly by a grant from BBSRC, Defra, NERC, the Scottish Government and the Wellcome Trust, under the Insect Pollinator Initiative (BB/I000801/1)

A short exposure to a semi-natural habitat alleviates the honey bee hive microbial imbalance caused by agricultural stress

Honeybee health and the species' gut microbiota are interconnected. Also noteworthy are the multiple niches present within hives, each with distinct microbiotas and all coexisting, which we termed "apibiome". External stressors (e.g. anthropization) can compromise microbial balance and bee resilience. We hypothesised that (1) the bacterial communities of hives located in areas with different degrees of anthropization differ in composition, and (2) due to interactions between the multiple microbiomes within the apibiome, changes in the community of a niche would impact the bacteria present in other hive sections. We characterised the bacterial consortia of different niches (bee gut, bee bread, hive entrance and internal hive air) of 43 hives from 3 different environments (agricultural, semi-natural and natural) through 16S rRNA amplicon sequencing. Agricultural samples presented lower community evenness, depletion of beneficial bacteria, and increased recruitment of stress related pathways (predicted via PICRUSt2). The taxonomic and functional composition of gut and hive entrance followed an environmental gradient. Arsenophonus emerged as a possible indicator of anthropization, gradually decreasing in abundance from agriculture to the natural environment in multiple niches. Importantly, after 16 days of exposure to a semi-natural landscape hives showed intermediate profiles, suggesting alleviation of microbial dysbiosis through reduction of anthropization.This work was funded by the Dept. of Economic Development and Competitiveness of the Basque Government (Gobierno Vasco/Eusko Jaurlaritza), R&D&I grants for the agricultural, food and fishing sectors of the Basque Autonomous Community (37-2017-00044), and the Research Group IT1233-19 of the Basque University System. JG was supported by the Department of Agriculture, Fisheries and Food of the Basque Government (Gobierno Vasco/Eusko Jaurlaritza) through a subsidy programme of training aid and support. These funding bodies provided the financial support to the research, but did not participate in the design of the study, analysis and interpretation of data, and writing of the manuscript

Population Structure and Diversity in European Honey Bees (Apis mellifera L.)-An Empirical Comparison of Pool and Individual Whole-Genome Sequencing

Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.This work was supported by National Natural Science Foundation of China (Grant No. 31902219) and Modern Agro-industry Technology Research System (Grant No. CARDS-45-KXJ1). The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960). MP and J.L were supported by the Applied Genomics and Bioinformatics research group (IT1233-19) funded by the Basque Government grant IT1233-19. Additionally, JL was funded by the grant PRE_2017_2_0169 from the Department of Education of the Basque Government

Virus Prevalence in Egg Samples Collected from Naturally Selected and Traditionally Managed Honey Bee Colonies across Europe

Monitoring virus infections can be an important selection tool in honey bee breeding. A recent study pointed towards an association between the virus-free status of eggs and an increased virus resistance to deformed wing virus (DWV) at the colony level. In this study, eggs from both naturally surviving and traditionally managed colonies from across Europe were screened for the prevalence of different viruses. Screenings were performed using the phenotyping protocol of the ‘suppressed in ovo virus infection’ trait but with qPCR instead of end-point PCR and a primer set that covers all DWV genotypes. Of the 213 screened samples, 109 were infected with DWV, 54 were infected with black queen cell virus (BQCV), 3 were infected with the sacbrood virus, and 2 were infected with the acute bee paralyses virus. It was demonstrated that incidences of the vertical transmission of DWV were more frequent in naturally surviving than in traditionally managed colonies, although the virus loads in the eggs remained the same. When comparing virus infections with queen age, older queens showed significantly lower infection loads of DWV in both traditionally managed and naturally surviving colonies, as well as reduced DWV infection frequencies in traditionally managed colonies. We determined that the detection frequencies of DWV and BQCV in honey bee eggs were lower in samples obtained in the spring than in those collected in the summer, indicating that vertical transmission may be lower in spring. Together, these patterns in vertical transmission show that honey bee queens have the potential to reduce the degree of vertical transmission over time

Comparison of two alternative store formats using a Malmquist-type index

This paper explores the differences in performance between two groups of retailing stores that operate with different formats. The study uses a Malmquist-type index to distinguish internal inefficiencies from those associated with the group (or format) characteristics. A fundamental characteristic of the new index is to compare groups in a static setting. The study described in this paper combines the use of the Malmquist index with statistical tests. The Malmquist-type index is decomposed into sub-indexes for comparing the efficiency spread between groups and the productivity differences between the best-practice frontiers of the groups. The hypothesis tests are used to verify if the differences between groups captured by the Malmquist-type index and its components are statistically significant. There are several methods based on DEA for comparing the performance of two groups, such as the program efficiency method and the comparison of efficiency distributions using statistical hypothesis tests. The method used in this paper is compared with the existing approaches to highlight its strengths and weaknesses. The applicability of the method is illustrated with a case study that compares the performance of heavy bazaar stores (that sell electrical appliances and consumer electronics) with different formats (megastores versus superstores). The study showed that the overall performance of megastores is better due to the effect of a more productive frontier. However, the efficiency spread is larger in megastores than in superstores meaning that there is scope for efficiency improvements

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript