Tracking the Genetic Stability of a Honey Bee (Hymenoptera: Apidae) Breeding Program With Genetic Markers

Prospects, challenges and perspectives in harnessing natural selection to solve the ‘varroa problem’ of honey bees

Honey bees, Apis mellifera, of European origin are major pollinators of crops and wild flora. Their endemic and exported populations are threatened by a variety of abiotic and biotic factors. Among the latter, the ectoparasitic mite Varroa destructor is the most important single cause behind colony mortality. The selection of mite resistance in honey bee populations has been deemed a more sustainable solution to its control than varroacidal treatments. Because natural selection has led to the survival of some European and African honey bee populations to V. destructor infestations, harnessing its principles has recently been highlighted as a more efficient way to provide honey bee lineages that survive infestations when compared with conventional selection on resistance traits against the parasite. However, the challenges and drawbacks of harnessing natural selection to solve the varroa problem have only been minimally addressed. We argue that failing to consider these issues could lead to counterproductive results, such as increased mite virulence, loss of genetic diversity reducing host resilience, population collapses or poor acceptance by beekeepers. Therefore, it appears timely to evaluate the prospects for the success of such programmes and the qualities of the populations obtained. After reviewing the approaches proposed in the literature and their outcomes, we consider their advantages and drawbacks and propose perspectives to overcome their limitations. In these considerations, we not only reflect on the theoretical aspects of host-parasite relationships but also on the currently largely neglected practical constraints, that is, the requirements for productive beekeeping, conservation or rewilding objectives. To optimize natural selection-based programmes towards these objectives, we suggest designs based on a combination of nature-driven phenotypic differentiation and human-directed selection of traits. Such a dual strategy aims at allowing field-realistic evolutionary approaches towards the survival of V. destructor infestations and the improvement of honey bee health.

Russian honey bee genotype identification through enhanced marker panel set

Russian honey bees (RHB) are a breeding population developed by USDA-ARS as an effort to provide Varroa-resistant honey bees to beekeepers. The selection strategy for this breeding population was the first in honey bees to incorporate genetic stock identification (GSI). The original GSI approach has been in use for over a decade, and though effective, novel technologies and analytical approaches recently developed provide an opportunity for improvement. Here we outline a novel genotyping assay that capitalizes on the markers used in the GSI as well as new loci recently identified in a whole genome pooled study of commercial honey bee stocks. Our approach utilizes a microfluidic platform and machine learning analyses to arrive at an accurate, high throughput assay. This novel approach provides an improved tool that can be readily incorporated into breeding decisions towards healthier more productive bees.

Genetic Diversity and Structure in a Closed Breeding System of Russian Honey Bees

High levels of genetic diversity are critical to the success of breeding programs. Russian honey bees are a selected stock that undergoes breeding in a closed block-based mating system. Given its established history, Russian stock has longitudinal measures of genetic integrity (i.e., genetically based stock identity) and diversity. Assessments using genetic stock identification (GSI) have shown that genetic diversity parameters were assessed across breeding blocks for three generations, spanning 6 yr (2014, 2017, and 2020), showing levels comparable to those when the stock was initially released in 2008. Allelic richness (mean number of alleles) and gene diversity levels were consistent over time and among blocks. Blocks were generally not differentiated from one another within each year. However, the 2020 population was distinct when compared with bees sampled in both 2014 and 2017. Genetic distance relationships supported differentiation of the 2020 population by distinct clustering. The patterns observed here are consistent with historical changes made to the breeding program to increase stringency of the selection criteria.

Genome-wide patterns of differentiation within and among U.S. commercial honey bee stocks

BACKGROUND

The population genetics of U.S. honey bee stocks remain poorly characterized despite the agricultural importance of Apis mellifera as the major crop pollinator. Commercial and research-based breeding programs have made significant improvements of favorable genetic traits (e.g. production and disease resistance). The variety of bees produced by artificial selection provides an opportunity to characterize the genetic diversity and regions of the genome undergoing selection in commonly managed stocks.

RESULTS

Pooled sequencing of eight honey bee stocks found strong genetic similarity among six of the stocks. Two stocks, Pol-line and Hilo, showed significant differentiation likely due to their intense and largely closed breeding for resistance to the parasitic Varroa mite. Few variants were identified as being specific to any one stock, indicating potential admixture among the sequenced stocks. Juxtaposing the underlying genetic variation of stocks selected for disease- and parasite-resistance behavior, we identified genes and candidate regions putatively associated with resistance regulated by hygienic behavior.

CONCLUSION

This study provides important insights into the distinct genetic characteristics and population diversity of honey bee stocks used in the United States, and provides further evidence of high levels of admixture in commercially managed honey bee stocks. Furthermore, breeding efforts to enhance parasite resistance in honey bees may have created unique genetic profiles. Genomic regions of interest have been highlighted for potential future work related to developing genetic markers for selection of disease and parasite resistance traits. Due to the vast genomic similarities found among stocks in general, our findings suggest that additional data regarding gene expression, epigenetic and regulatory information are needed to more fully determine how stock phenotypic diversity is regulated.