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ARS Home » Southeast Area » Baton Rouge, Louisiana » Honey Bee Lab » Research » Publications at this Location » Publication #395440

Research Project: Using Genetics to Improve the Breeding and Health of Honey Bees

Location: Honey Bee Breeding, Genetics, and Physiology Research

Title: Russian honey bee genotype identification through enhanced marker panel set

item Avalos, Arian
item Bilodeau, Lanie

Submitted to: Frontiers in Insect Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/14/2022
Publication Date: 11/21/2022
Citation: Avalos, A., Bilodeau, A.L. 2022. Russian honey bee genotype identification through enhanced marker panel set. Frontiers in Insect Science. 2:998310.

Interpretive Summary: Breeding programs such as the one developed for Russian Honey Bee (RHB), a Varroa-resistant breeding population, are improved by the use of accurate genotype identification tools. In this study we provide an update and expansion to the current genetic stock identification (GSI) panel used to discriminate individuals in the RHB breeding program. Our tool capitalizes on novel chemistry and analysis tools to provide a new panel set with higher-resolution and predictive accuracy. We expect this tool to be imminently useful to RHB breeders and look forward to its potential application in other genetically distinct stocks being developed for commercial production.

Technical Abstract: 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 original markers used in the GSI as well as novel 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 a highly accurate, cost effective, and high throughput assay. This novel approach provides an improved tool that can be readily incorporated into breeding decisions towards healthier more productive bees.