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HBeeID : a molecular tool that identifes honey bee subspecies from diferent geographic populations

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 dat...

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Main Authors: Donthu, Ravikiran, Marcelino, Jose A. P., Giordano, Rosanna, Tao, Yudong, Weber, Everett, Avalos, Arian, Band, Mark, Akraiko, Tatsiana, Chen, Shu‑Ching, Reyes, Maria P, Hao, Haiping, Ortiz‑Alvarado, Yarira, Cuf, Charles A., Pérez Claudio, Eddie, Soto‑Adames, Felipe, Smith‑Pardo, Allan H., Meikle, William G., Evans, Jay D., Giray, Tugrul, Abdelkader, Faten B., Allsopp, Mike, Ball, Daniel, Morgado, Susana B., Barjadze, Shalva, Correa‑Benitez, Adriana, Chakir, Amina, Báez, David R., Chavez, Nabor H. M., Dalmon, Anne, Douglas, Adrian B., Fraccica, Carmen, Fernández‑Marín, Hermógenes, Galindo Cardona, Alberto, Guzman‑Novoa, Ernesto, Horsburgh, Robert, Kence, Meral, Kilonzo, Joseph, Kükrer, Mert, Le Conte, Yves, Mazzeo, Gaetana, Mota, Fernando, Muli, Elliud, Oskay, Devrim, Ruiz‑Martínez, José A., Oliveri, Eugenia, Pichkhaia, Igor, Romane, Abderrahmane, Guillen Sanchez, Cesar, Sikombwa, Evans, Satta, Alberto, Scannapieco, Alejandra Carla, Stanford, Brandi, Soroker, Victoria, Velarde, Rodrigo A., Vercelli, Monica, Huang, Zachary
Format: info:ar-repo/semantics/artículo
Language:Inglés
Published: BioMed Central 2025
Subjects:
Honey Bees
Single Nucleotide Polymorphisms
Diagnostic Techniques
Networks
Invasive Species
Abeja Melífera
Polimorfismo de Nucleótido Unico
Técnica de Diagnóstico
Red
Especie Invasiva
Online Access:http://hdl.handle.net/20.500.12123/22854
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05776-9
https://doi.org/10.1186/s12859-024-05776-9
Summary: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.

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