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

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