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

Date

2024-12-01

Author

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
Cuff, Charles A.
Claudio, Eddie Pérez
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
Sanchez, Cesar Guillen
Sikombwa, Evans
Satta, Alberto
Scannapieco, Alejandra A.
Stanford, Brandi
Soroker, Victoria
Velarde, Rodrigo A.
Vercelli, Monica
Huang, Zachary

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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.

Subject Keywords

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

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202347552&origin=inward
https://hdl.handle.net/11511/111238

Collections

Department of Biology, Article