The Univesity of Aarhus (AU), Denmark
AU is a state University with agricultural research departments. During the past decade the Section “Honeybee diseases, genetics and pollination” (Bee team) has focused its research on applying genetic tools to honeybee biology. Monitoring the conservation of the Læsø honeybees is a Bee team duty. Population genetics tools are used to assist Danish beekeepers and authorities to safeguard the genetic diversity of the Danish A. m. mellifera. Recent projects include viral diseases linked to Varroa mites and pollination studies. Every year AU is educating hundreds of beekeepers to prevent honeybee diseases. More than 40 appointed bee inspectors assist in controlling disease outbreaks.
The Section of Molecular Genetics and Systems Biology (MGS) has a strong record in gene mapping, identification and characterization of QTL. Laboratory facilities are equipped for large scale genetics and genomics and instrumentation includes robotics platform as well as NGS sequencers (Illumina GAIIx and HiSeq2000). 4 years ago the MGS group adopted the honeybee as a model organism for studying basic characteristics of quantitative traits and presently maintains more than 120 families mainly used for QTL-identification, studies of disease resistance as well as studies of QTL-interactions within drone populations.
The main tasks of AU in the SmartBees project are:
- Using pool-based sequencing of Varroa/virus resistant and susceptible colonies of honeybees, we aim to identify genetic variants and genes associated with resistance traits (including hygienic behavior). In addition, transcriptome analysis (RNA-seq study) of samples from selected groups of bees will be performed to identify differentially expressed genes and nc-RNAs.
- Comparative transcriptome analyses between infected and naive bee cells to identify the key genes involved in innate immunity in the honeybees. This information will be provided for inclusion in the genetic selection for bees with increased resistance against virus infections
- Characterize the mutant cloud of DWV isolates using next generation sequencing to determine viral sequence signatures relevant for diagnosis of virus virulence and adaptation.