Location: Honey Bee Breeding, Genetics, and Physiology Research
Project Number: 6050-21000-016-004-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Apr 1, 2018
End Date: Mar 31, 2023
Objective:
The long-term goal of this project is to translate genomic information to tools that can be used in sustaining honey bee health. The overall objective of this application, which is the next step to attaining our long-term goal, is to develop genetic markers for widespread application in honey bee breeding by identifying the genetic regulatory variants that control Varroa Sensitive Hygiene (VSH). The rationale that underlies the proposed project is that traditional Genome-Wide Association Studies (GWAS) alone cannot identify variants that will be useful across diverse honey bee populations; however, the set of variants that directly regulate VSH behavior through molecular mechanisms is expected to be conserved across populations. We will accomplish the overall objective of this application by pursuing the following goals:
Goal 1: Identify genomic regulatory variants associated with VSH and validate those variants in diverse populations.
Goal 2: Develop data mining resources to accelerate genomics research in honey bee health, including research to develop genetic markers for resistance traits.
Approach:
Genome-Wide Association Studies (GWAS) will be performed to identify single nucleotide polymorphisms (SNPs) associated with varroa sensitive hygiene (VSH) and expression quantitative trait loci (eQTL) analysis to identify SNPs associated with gene expression in the brain and antennae. Combining the power of high coverage whole-genome sequencing (WGS) and eQTL analysis is more likely than previous honey bee mapping efforts to identify regulatory variants that have a high likelihood of being conserved across populations. Sets of 6 single patriline colonies will be established that maximize diversity in the VSH phenotype, using reciprocal crosses between high and low VSH lines. The high/low and low/high queen × drone colonies allow for possible parent of origin effects. All queens and drones will be selected from diverse breeding lines to maximize genetic divergence. WGS (30X coverage) of the mother queen, father drone and worker bees from each colony will be generated, accompanying ribonucleic acid (RNA)-seq for individual brains and antennae collected from the same worker bees. Variants and genotypes using the WGS data will be called and transcript expression using the RNA-seq data will be computed for input into GWAS and eQTL analysis. Collecting bees performing VSH behavior in the high VSH colonies will increase expression diversity for the eQTL analysis. We will look for eQTL SNPs within the set of SNPs associated with VSH to select a set of 200 SNPs for validation. Selected SNPs will be validated retrospectively by genotyping workers a large set of colonies across three locations, VSH-phenotyped colonies at the HBB-BR, and VSH-phenotyped colonies from two separate cooperator operations in South Carolina and North Dakota.
Our approach to create HymenopteraMine2 is to perform SNP and genotyping calling using honey bee genome sequencing data available from new genome sequencing datasets that will be provided by HBB-BR Co-PD, from WGS sequencing performed in specific aim 1 and all available Illumina data from the NCBI. After identifying variants, we will develop HymenopteraMine2 by extending HymenopteraMine, an existing data mining system based on InterMine. We will modify the data model and improve the web application to accommodate flexible searching of the variant data. We will also incorporate existing RNA-seq data downloaded from the Sequence Read Archive (SRA) by computing gene and transcript expression levels. HymenopteraMine2 development will be performed iteratively with feedback from stakeholders. In addition to training scientists at the HBB-BR lab, we will hold webinars following each HymenopteraMine2 release to train a wider group of users. The outcome will be a single data mining resource that combines public honey bee variant data with computed RNA-seq-based expression levels and external sources of gene annotation. In addition to serving as a resource for honey bee genetic diversity HymenopteraMine2 will facilitate integrative approaches such as pathway and GO overrepresentation analysis subsequent to GWAS or gene set enrichment analysis combined with SNP analysis (known as GSEA-SNP) to reveal modest-effect candidate genes.
