Source: PURDUE UNIVERSITY submitted to NRP
HOW TO BREED THE BEST BEES: OPTIMIZING GENOMIC STUDIES, SELECTION, AND MANAGEMENT OF GENETIC DIVERSITY
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
Reporting Frequency
Annual
Accession No.
1029730
Grant No.
2023-67013-38997
Cumulative Award Amt.
$749,611.00
Proposal No.
2022-08475
Multistate No.
(N/A)
Project Start Date
Jan 15, 2023
Project End Date
Jan 14, 2027
Grant Year
2023
Program Code
[A1113]- Pollinator Health: Research and Application
Recipient Organization
PURDUE UNIVERSITY
(N/A)
WEST LAFAYETTE,IN 47907
Performing Department
(N/A)
Non Technical Summary
The honey bee, Apis mellifera, is the most important agricultural pollinator. Through pollination,honey bees contribute at least billions annually to the US economy. Unfortunately, it's an industry fraught with challenges.Beekeepers lose over 40% of their colonies annually, mainly as a result of parasites and pathogens. Bee breeders have responded to these threats by incorporating resistance traits into their selection schemes. Current bee breeding methods were developed almost a century ago and have yet to incorporate genome-enabled breeding techniques. Bee breeders, researchers, and beekeepers have clearly demonstrated interest in incorporating such information into on-going breeding efforts. Unfortunately, the lack of a theoretical underpinning for this work in honey bees inhibits our ability to properly and regularly incorporate genomic information into breeding practice to breed healthy pollinators. We propose to develop an inexpensive genotyping service and the theoretical and practical background required for bee breeders to unravel genomic regions associated with economically important traits in honey bee. Ultimately, our proposal will enable honey bee breeders to incorporate genomic-enabled predictions into their breeding schemes. In so doing, bee breeders can more rapidly select stocks expressing traits of interest and beekeepers will know whether a colony purchased from a given stock will express a trait ofinterest, especially those that are difficult to measure such as disease resistance.
Animal Health Component
20%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
30431101080100%
Knowledge Area
304 - Animal Genome;

Subject Of Investigation
3110 - Insects;

Field Of Science
1080 - Genetics;
Goals / Objectives
We propose to develop and evaluate affordable genome-enabled breeding techniques so that bee breeders can rapidly identify and select more resistant and productive honey bee stocks. This will be achieved with three major objectives:Objective 1: BeeWAS: Optimize genotyping schemes and Genome-Wide Association Studies forhaplodiploid social insects and develop a set of best practices for honey beeObjective 2: BeeGS: Evaluate Genomic Selection and breeding schemes for honey bees, with afocus on difficult-to-select traits (e.g., disease resistance); and,Objective 3: BeeOCS: Develop Optimal Cross Selection for managing genetic diversity withinselected honey bee populations.
Project Methods
Methods: Simulations: We will begin by creating real and simulated data sets to allow us to test therobustness of various study and sampling designs to detect associations and provide a useful jumpingoff point for further exploration into the use of genomic data in honey bee breeding (Objectives 2and 3). By using simulated datasets, in which the true locations of the causal mutations are known,alternative scenarios can be compared and recommended to honey bee researchers. In addition, thesefindings will be used to validate real datasets currently available (Table 1). Simulations using an updatedAlphaSimR (Gaynor et al. 2017;Gaynor et al. 2020) and SIMplyBee (Strachan et al., 2022), will serve asthe background from which we will be able to determine the infrastructure required for unravelingsignificant associations between genomic markers and phenotypic traits of interest to honey beebreeds as well as the optimal methods for the implementation of genomic selection to increaseproductivity and resilience of honey bee colonies while maintaining high levels of genetic diversity inUS pollinators. The data simulation structure will be performed to mimic the honey bee genome structure and specificities of honey bee biology (Section D).We will compare also different methods to perform genomic prediction in honeybee traits using both simulated and real datasets. The use of simulated datasets is of great importancebecause the True Breeding Values and True Dominance Deviations are known and therefore, theaccuracy of genomic prediction can be properly assessed. Various statistical models for geneticevaluation of honey bee traits will be evaluated. This work will involve comparing alternative modelsfor fitting the queen, drone, worker, or colony genetic effects, the use of genetic groups to accountfor unknown drones (genetic merit of drone-producing queens) in the context of pedigree-basedmodels (PBLUP; Brascamp & Bijma, 2014), genome-based models (GBLUP; Wang & Xu, 2019), andcombined pedigree and genome "single-step" models (ssGBLUP; Legarra et al. 2014).

Progress 01/15/24 to 01/14/25

Outputs
Target Audience:Our target audience is the ~250,000 beekeepers of the United States. Beekeepers today expect to lose an average of 30% of their colonies each year. For both native and managed bees, pathogens play an outsized role in causing or exacerbating declines. An average of 50% of honey bee colonies died in the US in 2018-2019 as a direct result of pathogens (Sadler, 2018). Dozens of pathogens can infect honey bees and they can collectively or individually cause colony death or reduce productivity (Chen et al. 2006; Schwarz et al. 2015). Beekeepers have traditionally responded to established and emergent pests and pathogens by screening their colonies for resistance and incorporating resistant stocks into their breeding programs. There are dozens of beekeeper-driven breeding organizations around North America and even more globally that aim to create stocks that express traits beekeepers value. Changes/Problems:It took significantly longer to obtain a visa for the post-doctoral recruit, but we have overcome this challange and look forward ot a fruitful year. What opportunities for training and professional development has the project provided?Graduate student workers on this proposal have presented at three national and international conferences. Post-doctoral trainees will have the opportunity next year. We have, thusfar, presented at the Entomological Society of America, "BeeCon", and the Plant Animal Genomes Conference. Both grad student and post-doc trainees travelled to Gregor Gorjanc (Roslind) to develop theory for Obj 1-3. How have the results been disseminated to communities of interest?Our preliminary results have been shared with our stakeholders and communities through. Specifically, we have shared preliminary results with beekeepers at our in-person lectures and through digital lectures. In addition, we have provided early insights in the Indiana Beekeeper's Journal and at the Purdue Field Day to beekeepers. What do you plan to do during the next reporting period to accomplish the goals?We will continue to push to complete peer-reviewed papers from this work for objectives 2-3 and we will continue outreach and extension to beekeepers and bee breeders.

Impacts
What was accomplished under these goals? Objective 1: BeeWAS - Optimizing Genotyping Schemes and Genome-Wide Association Studies for Honey Bees We have made significant strides in optimizing genotyping schemes and performing Genome-Wide Association Studies (GWAS) for honey bees, particularly focusing on the haplodiploid nature of social insects. To date, we have sequenced 2000 honey bee samples using two distinct methodologies, which have provided us with a rich dataset for comparative analysis. This work has laid the foundation for the development of Genome-Wide Association (GWA) models, which we are currently fine-tuning for optimal suitability and accuracy. We have already submitted the results for publication, and we are now transitioning to focus on Objective 2, with the expectation of disseminating our findings within this year. Our collaboration with Gregor Gorjanc at Roslind has been instrumental in advancing the theoretical framework for this objective. Objective 2: BeeGS - Evaluating Genomic Selection and Breeding Schemes for Honey Bees Objective 2 has seen substantial progress, particularly in our collaboration with bee breeders to design and evaluate optimized genomic selection programs for honey bee populations. We have successfully completed field trials, where our breeding schemes have been implemented and tested in real-world conditions. These trials are providing valuable insights into the genetic selection of difficult-to-select traits, such as disease resistance. Alongside the physical trials, we've also developed computational models to simulate and refine these breeding programs (in silico). Moving forward, we are preparing for a comprehensive reevaluation of our strategies this summer to further optimize the breeding process. Our work in this area continues to build on the theory developed with Gregor Gorjanc, ensuring robust scientific foundations for our efforts. Objective 3: BeeOCS - Developing Optimal Cross Selection for Managing Genetic Diversity We are now transitioning into Objective 3, which focuses on developing Optimal Cross Selection strategies to manage genetic diversity within honey bee populations. We are drawing upon the theoretical work developed with Gregor Gorjanc to guide our approach in optimizing cross-selection strategies. The next steps will involve refining our methods and starting the practical implementation of these strategies within selected populations. The goal is to ensure that our breeding programs not only select for desirable traits but also maintain a healthy level of genetic diversity, which is crucial for long-term population resilience. Overall, we are making substantial progress across all three objectives, with ongoing collaborations and field trials driving our research forward.

Publications

  • Type: Peer Reviewed Journal Articles Status: Under Review Year Published: 2024 Citation: Ryals et al. 2025. Genomics Selection Evolution. Assessing ancestry and diversity in honey bees (Apis mellifera) through low-pass whole-genome sequencing.


Progress 01/15/23 to 01/14/24

Outputs
Target Audience: Our target audience is the ~250,000 beekeepers of the United States.Beekeepers today expect to lose an average of 30% of their colonies each year. For both native and managed bees, pathogens play an outsized role in causing or exacerbating declines. An average of 50% of honey bee colonies died in the US in 2018-2019 as a direct result of pathogens (Sadler, 2018). Dozens of pathogens can infect honey bees and they can collectively or individually cause colony death or reduce productivity (Chen et al. 2006; Schwarz et al. 2015). Beekeepers have traditionally responded to established and emergent pests and pathogens by screening their colonies for resistance and incorporating resistant stocks into their breeding programs.There are dozens of beekeeper-driven breeding organizations around North America and even more globally that aim to create stocks that express traits beekeepers value. Changes/Problems:It took significantly longer to obtain a visa for the post-doctoral recruit, but we have overcome this challagne and look forward ot a fruitful year. What opportunities for training and professional development has the project provided?Graduate student workers on this proposalhave presented at three national and international conferences. Post-doctoral trainees will have the opportunity next year. We have, thusfar, presented at the Entomological Society of America, "BeeCon", and the Plant Animal Genomes Conference. How have the results been disseminated to communities of interest?Our preliminary results have been shared with our stakeholders and communities through. Specifically, we have shared preliminary results with beekeepers at our in-person lectures and through digital lectures. In addition, we have provided early insights in the Indiana Beekeeper's Journal and at the 2023 Purdue Field Day to beekeepers. What do you plan to do during the next reporting period to accomplish the goals?We will continue to push to complete early peer-reviewed papers from this work.

Impacts
What was accomplished under these goals? For objective 1, our focus has been on the comprehensive sequencing of 2000 honey bee samples utilizing two distinct methodologies, culminating in a rich dataset that enables a comparative analysis of genotyping effectiveness. Concurrently, we're in the process of crafting Genome-Wide Association (GWA) models to evaluate their suitability and accuracy. With an eye towards dissemination, we are on track to publish our findings within this calendar year, thereby contributing significantly to the understanding of honey bee genetics. As for objective 2, our efforts have been geared towards collaborating closely with bee breeders to devise an optimized breeding program. We've meticulously designed this program, both in physical implementation and through computational simulations (in silico). While our initial results have been promising, we are gearing up for a comprehensive reevaluation this summer to refine our methodologies and enhance outcomes.

Publications