A genetic stock certification assay was developed to distinguish Russian honey bees from other European (Apis mellifera L.) stocks that are commercially produced in the United States. In total, 11 microsatellite and five single-nucleotide polymorphism loci were used. Loci were selected for relatively high levels of homogeneity within each group and for differences in allele frequencies between groups. A baseline sample consisted of the 18 lines of Russian honey bees released to the Russian Bee Breeders Association and bees from 34 queen breeders representing commercially produced European honey bee stocks. Suitability tests of the baseline sample pool showed high levels of accuracy. The probability of correct assignment was 94.2% for non-Russian bees and 93.3% for Russian bees. A neighbor-joining phenogram representing genetic distance data showed clear distinction of Russian and non-Russian honey bee stocks. Furthermore, a test of appropriate sample size showed a sample of eight bees per colony maximizes accuracy and consistency of the results. An additional 34 samples were tested as blind samples (origin unknown to those collecting data) to determine accuracy of individual assignment tests. Only one of these samples was incorrectly assigned. The 18 current breeding lines were represented among the 2009 blind sampling, demonstrating temporal stability of the genetic stock identification assay. The certification assay will be used through services provided by a service laboratory, by the Russian Bee Breeders Association to genetically certify their stock. The genetic certification will be used in conjunction with continued selection for favorable traits, such as honey production and varroa and tracheal mite resistance.

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Background: The small hive beetle, Aethina tumida, is a rapidly emerging global pest of honey bee colonies. Small hive beetle infestation can be extremely destructive, which may cause honey bees to abscond and render colony infrastructure unusable. Due to the impacts small hive beetles have on honey bees, a wide variety of physical, cultural, and chemical control measures have been implemented to manage small hive beetle infestations. The use of insecticides to control small hive beetle populations is an emerging management tactic. Currently, very little genomic information exists on insecticide target sites in the small hive beetle. Therefore, the objective of this study is to utilize focused in silico comparative genomics approaches to identify and assess the potential insecticide sensitivity of the major insecticide target sites in the small hive beetle genome. Results: No previously described resistance mutations were identified in any orthologs of insecticide target sites. Alternative exon use and A-to-I RNA editing were absent in AtumSC1. The ryanodine receptor in small hive beetle (Atum_Ryr) was highly conserved and no previously described resistance mutations were identified. A total of 12 nAChR subunits were identified with similar alternative exon use in other insects. Alternative exon use and critical structural features of the GABA-gated chloride channel subunits (Atum_RDL, Atum_GRD, and Atum_LCCH3) were conserved. Five splice variants were found for the glutamate-gated chloride channel subunit. Exon 3c of Atum_ GluCl may be a beetle-specific alternative exon. The co-occurrence of exons 9a and 9b in the pH-sensitive chloride channel (Atum_pHCl) is a unique combination that introduces sites of post-translational modification. The repertoire and alternative exon use for histamine-gated chloride channels (Atum-HisCl), octopamine (Atum_OctR) and tyramine receptors (Atum_TAR) were conserved. Conclusions: The recently published small hive beetle genome likely serves as a reference for insecticidesusceptible versions of insecticide target sites. These comparative in silico studies are the first step in discovering targets that can be exploited for small hive beetle-specific control as well as tracking changes in the frequency of resistance alleles as part of a resistance monitoring program. Comparative toxicity alongside honey bees is required to verify these in silico predictions.