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 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 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.

Four files are provided here:

1) Genotypes and sample metadata obtained from a Fluidigm 96.96 Dynamic ArrayTM characterizing RHB population. This data set forms the core sample set for the updated genotype stock identification approach outlined in the reference article.

2) An R script detailing the process by which to access and process (1) to arrive at a reproduction of the model used in the referenced study.

3 & 4) Output tuning and model files specific to the referenced study provided for scrutiny and contrasts.

Open source R and RStudio are the only required software to access data.