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

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dc.authoridScannapieco, Alejandra Carla/0000-0002-4228-2996
dc.authoridAvalos, Arian/0000-0002-4011-3099
dc.authoridKukrer, Mert/0000-0003-1755-1119
dc.authoridCORREA-BENITEZ, ADRIANA/0000-0001-7806-1995
dc.contributor.authorDonthu, Ravikiran
dc.contributor.authorMarcelino, Jose A. P.
dc.contributor.authorGiordano, Rosanna
dc.contributor.authorTao, Yudong
dc.contributor.authorWeber, Everett
dc.contributor.authorAvalos, Arian
dc.contributor.authorBand, Mark
dc.date.accessioned2024-10-29T17:58:49Z
dc.date.available2024-10-29T17:58:49Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractBackgroundHoney 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.ResultsWe 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.ConclusionHBeeID 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.
dc.description.sponsorshipPuerto Rico Science, Technology and Research Trust, United States of America [FL 32311]; Middle East Technical University, Dept. of Biology Sciences [1545803, 1736019]; NSF-OISE [1826729, 2022-00001, 2020-00081]; NSF-DEB [AP20PPQS, T00C009, 1649]; USDA-APHIS; Institute of Environment at Florida International University
dc.description.sponsorshipWe would like to thank the following people for assistance in bringing this work to conclusion: Jim Nardi (Dept. of Entomology, University of Illinois, Urbana, IL 61801, USA), Andreas Wallberg (Dept. of Biology, Uppsala University. Sweden), Arnaud Faille (Dept. of Entomology, Stuttgart State Museum of Natural History, Stuttgart, Germany), Aykut Kence (posthumously) Middle East Technical University, Dept. of Biology Sciences. Becky Hogg and Tony Hogg (Florida State Beekeepers Association, Tallahassee, FL 32311, USA), Jennifer Holmes (Hani Honey Company Stuart, Stuart, FL 34994, USA), Patrick Cooley, (California Beekeeper San Diego), Veronique Petrucci and three anonymous reviewers. This work was supported with funding from NSF-OISE #1545803; NSF_HRD #1736019; NSF-DEB #1826729; PRSTRT #2022-00001 to T. Giray and PRSTRT # 2020-00081 and USDA-APHIS #AP20PPQS & T00C009 to T. Giray and R. Giordano. This is contribution #1649 from the Institute of Environment at Florida International University.
dc.identifier.doi10.1186/s12859-024-05776-9
dc.identifier.issn1471-2105
dc.identifier.issue1en_US
dc.identifier.pmid39192185
dc.identifier.scopus2-s2.0-85202347552
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1186/s12859-024-05776-9
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14516
dc.identifier.volume25
dc.identifier.wosWOS:001298963800004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofBmc Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectHoney bee
dc.subjectSNP
dc.subjectInvasive
dc.subjectDiagnostic
dc.subjectHierarchical agglomerative clustering
dc.subjectNetwork
dc.titleHBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations
dc.typeArticle

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