HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations

Publication Type

Journal Article

Journal Name

BMC Bioinformatics

Name of Author

• Ravikiran Donthu, Technology and Research Trust
• Jose A.P. Marcelino, Technology and Research Trust
• Rosanna Giordano, Technology and Research Trust
• Yudong Tao, University of Miami College of Engineering
• Everett Weber, Dartmouth College
• Arian Avalos, USDA Agricultural Research Service
• Mark Band, University of Illinois Urbana-Champaign
• Tatsiana Akraiko, University of Illinois Urbana-Champaign
• Shu Ching Chen, University of Missouri-Kansas City
• Maria P. Reyes, FIU College of Engineering and Computing
• Haiping Hao, Johns Hopkins University School of Medicine
• Yarira Ortiz-Alvarado, Universidad de Puerto Rico
• Charles A. Cuff, Universidad de Puerto Rico
• Eddie Pérez Claudio, University of Pittsburgh School of Medicine
• Felipe Soto-Adames, Florida Division of Plant Industry
• Allan H. Smith-Pardo, USDA Animal and Plant Health Inspection Service (APHIS)
• William G. Meikle, USDA ARS Carl Hayden Bee Research Center
• Jay D. Evans, USDA Agricultural Research Service
• Tugrul Giray, Universidad de Puerto Rico
• Faten B. Abdelkader, University of Carthage, Institut National Agronomique de Tunisie
• Mike Allsopp, Agricultural Research Council, Pretoria
• Daniel Ball, Forest Fruits Ltd
• Susana B. Morgado, Associação de Apicultores do Parque Natural do Tejo Internacional
• Shalva Barjadze, Ilia State University (ISU)
• Adriana Correa-Benitez, Universidad Nacional Autónoma de México
• Amina Chakir, Faculté des Sciences Semlalia
• David R. Báez, Amateur Beekeeper
• Nabor H.M. Chavez, Cochabamba Beekeepers Federation (FEDAC)

Publication Date

12-1-2024

Abstract

Background: Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited. Results: We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples. Conclusion: HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.

Keywords

Diagnostic, Hierarchical agglomerative clustering, Honey bee, Invasive, Network, SNP

PubMed ID

39192185

Recommended Citation

Donthu, R., Marcelino, J., Giordano, R., Tao, Y., Weber, E., Avalos, A., Band, M., Akraiko, T., Chen, S., Reyes, M., Hao, H., Ortiz-Alvarado, Y., Cuff, C., Claudio, E., Soto-Adames, F., Smith-Pardo, A., Meikle, W., Evans, J., Giray, T., Abdelkader, F., Allsopp, M., Ball, D., Morgado, S., Barjadze, S., Correa-Benitez, A., Chakir, A., Báez, D., & Chavez, N. (2024). HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations. BMC Bioinformatics, 25 (1) https://doi.org/10.1186/s12859-024-05776-9