Modern animal breeding technologies combining statistics and genomics can provide tools for generating reliable genetic improvement in a wide range of traits but have not yet been applied to honey bees in Australia. Genetic improvement of honey bees will allow the continual identification and use of queens that will permanently and continuously improve traits that are important to the honey bee industry (such as pest and disease resistance, honey production, pollination performance, and temperament). Using genomic data within the context of a honey bee breeding program can identify genetic relationships between individuals to increase accuracy of breeding values. Genomic data can also provide information about managed honey bee populations, to estimate stratification and introgression levels and to understand complex population admixture events or identify signatures of natural and artificial selection. For the majority of livestock species genomic data are obtained through the use of commercially available Single Nucleotide Polymorphism (SNP) array chips, which can provide accurate and evenly distributed SNPs with known locations across the genome. Such chips have only recently become commercial for bees. SNP genotyping can be easily reproducible across different batches of samples and is tolerant of lower quality DNA extracts. In this study, we investigate using DNA extracted from pooled drone samples and imputed genotypes from low pass sequencing for genomic prediction and population structure analysis.