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ARS Home » Southeast Area » Baton Rouge, Louisiana » Honey Bee Lab » Research » Publications at this Location » Publication #401350
Research Project: Using Genetics to Improve the Breeding and Health of Honey Bees

Location: Honey Bee Breeding, Genetics, and Physiology Research

Title: BeeID: A molecular tool that identifies honey bee subspecies from different geographic populations

Author
item DONTHU, RAVIKIRAN - Puerto Rico Science, Technology And Research Trust
item MARCELINO, JOSE - University Of Florida
item GIORDANO, ROSANNA - Puerto Rico Science, Technology And Research Trust
item TAO, YUDONG - University Of Miami
item WEBER, EVERETT - Dartmouth College
item Avalos, Arian
item BAND, MARK - University Of Illinois
item AKRAIKO, TATSIANA - University Of Illinois
item SHU-CHING, CHEN - University Of Missouri
item REYES, MARIA - Florida International University
item HAO, HAIPING - Johns Hopkins University School Of Medicine
item ORTIZ-ALVARADO, YARIRA - University Of Puerto Rico
item CUFF, CHARLES - University Of Puerto Rico
item PÉREZ CLAUDIO, EDDIE - University Of Pittsburgh
item SMITH-PARDO, ALLAN - Animal And Plant Health Inspection Service (APHIS)
item Meikle, William
item Evans, Jay
item GIRAY, TUGRUL - University Of Puerto Rico
item ABDELKADER, FATEN - University Of Carthage, Tunisia
item ALLSOPP, MIKE - Agricultural Research Council Of South Africa
item BALL, DANIEL - Forest Fruits Ltd
item MORGADO, SUSANA - Meltagus, Tagus International Natural Park Beekeeping Association
item BARJADZE, BARJADZE - Ilia State University
item CORREA-BENITEZ, ADRIANA - The National Autonomous University Of Mexico
item CHAKIR, AMINA - Universite Cadi Ayyad
item BÁEZ, DAVID - Farmer
item CHAVEZ, NABOR H - Cochabamba Beekeepers Federation
item DALMON, ANNE - Inrae
item DOUGLAS, ADRIAN - University Of Malta
item FRACCICA, CARMEN - Florida Department Of Agriculture And Consumer Services
item FERNÁNDEZ-MARÍN, HERMÓGENES - Institute Of Scientific Research And High Technology Services Of Panama
item GALINDO-CARDONA, ALBERTO - National Scientific And Technical Research Council (CONICET)
item GUZMAN-NOVOA, ERNESTO - University Of Guelph
item HORSBURGH, ROBERT - Florida Department Of Agriculture And Consumer Services
item KENCE, MERAL - Middle East Technical University
item KILONZO, JOSEPH - International Centre Of Insect Physiology And Ecology
item KÜKRER, MERT - Middle East Technical University
item LE CONTE, YVES - Inrae
item MAZZEO, GAETANA - University Of Catania
item MOTA, FERNANDO - Farmer
item MULI, ELLIUD - International Centre Of Insect Physiology And Ecology
item OSKAY, DEVRIM - Namik Kemal University
item RUIZ-MARTÍNEZ, JOSÉ - University Of Cordoba
item OLIVERI, EUGENIA - Consultant
item PICHKHAIA, IGOR - Chkhorotsku Local Historical Museum
item ROMANE, ABDERRAHMANE - Universite Cadi Ayyad
item SANCHEZ, CESAR - National Banana Corporation (CORBANA)
item SIKOMBWA, EVANS - Forest Fruits Ltd
item SATTA, ALBERTO - University Of Sassari
item SCANNAPIECO, ALEJANDRA - National Scientific And Technical Research Council (CONICET)
item STANFORD, BRANDI - Florida Department Of Agriculture And Consumer Services
item SOROKER, VICTORIA - Agricultural Research Organization Of Israel
item VELARDE, RODRIGO - Bolivian Apiculture Institute (IAB)
item VERCELLI, MONICA - Consultant
item HUANG, ZACHARY - Michigan State University

Submitted to: Science Advances
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2024
Publication Date: 8/27/2024
Citation: Donthu, R., Marcelino, J., Giordano, R., Tao, Y., Weber, E., Avalos, A., Band, M., Akraiko, T., Shu-Ching, C., Reyes, M.P., Hao, H., Ortiz-Alvarado, Y., Cuff, C.A., Pérez Claudio, E., Smith-Pardo, A.H., Meikle, W.G., Evans, J.D., Giray, T., Abdelkader, F.B., Allsopp, M., Ball, D., Morgado, S.B., Barjadze, B., Correa-Benitez, A., Chakir, A., Báez, D.R., Chavez, N.M., Dalmon, A., Douglas, A.B., Fraccica, C., Fernández-Marín, H., Galindo-Cardona, A., Guzman-Novoa, E., Horsburgh, R., Kence, M., Kilonzo, J., Kükrer, M., Le Conte, Y., Mazzeo, G., Mota, F., Muli, E., Oskay, D., Ruiz-Martínez, J.A., Oliveri, E., Pichkhaia, I., Romane, A., Sanchez, C.G., Sikombwa, E., Satta, A., Scannapieco, A.A., Stanford, B., Soroker, V., Velarde, R.A., Vercelli, M., Huang, Z. 2024. BeeID: A molecular tool that identifies honey bee subspecies from different geographic populations. Science Advances. https://doi.org/10.1186/s12859-024-05776-9.
DOI: https://doi.org/10.1186/s12859-024-05776-9

Interpretive Summary: Identification tools are greatly needed for identifying honey bee population membership. Such tools are used in a variety of applications, from tracking pests, to monitoring existing populations. This manuscript introduces BeeID, a population identification platform that uses microfluidic chemistry and machine learning approaches in accurately discriminate among honey bee populations. This approach has a high degree of certainty in established populations even with a partial genetic profile. Prediction capacity is reduced though still reliable in admixed populations but retains a high degree of flexibility such that future data can be readily incorporated to improve results. This tool could be helpful in conservation, monitoring and regulatory capacity, towards the aim of maintaining healthy bee populations.

Technical Abstract: Pollinators and other arthropods are experiencing a decline due to anthropogenic induced factors. Global trade is a potential source for the introduction of undesirable bee strains, their pathogens, and parasites. Tools are needed to determine the origin of invasive bees. Genomic data for economically and ecologically important organisms, including pollinators, is increasing, but its application for solving problems is limited. We introduce BeeID 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. Test results of BeeID 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 BeeID. Its prediction capacity decreases with highly admixed samples. BeeID is a valuable tool to screen invasive honey bees. Its flexible design allows for future improvement via sample data additions from other localities.