TY - JOUR
T1 - HBeeID
T2 - a molecular tool that identifies honey bee subspecies from different geographic populations
AU - Donthu, Ravikiran
AU - Marcelino, Jose A.P.
AU - Giordano, Rosanna
AU - Tao, Yudong
AU - Weber, Everett
AU - Avalos, Arian
AU - Band, Mark
AU - Akraiko, Tatsiana
AU - Chen, Shu Ching
AU - Reyes, Maria P.
AU - Hao, Haiping
AU - Ortiz-Alvarado, Yarira
AU - Cuff, Charles A.
AU - Claudio, Eddie Pérez
AU - Soto-Adames, Felipe
AU - Smith-Pardo, Allan H.
AU - Meikle, William G.
AU - Evans, Jay D.
AU - Giray, Tugrul
AU - Abdelkader, Faten B.
AU - Allsopp, Mike
AU - Ball, Daniel
AU - Morgado, Susana B.
AU - Barjadze, Shalva
AU - Correa-Benitez, Adriana
AU - Chakir, Amina
AU - Báez, David R.
AU - Chavez, Nabor H.M.
AU - Dalmon, Anne
AU - Douglas, Adrian B.
AU - Fraccica, Carmen
AU - Fernández-Marín, Hermógenes
AU - Galindo-Cardona, Alberto
AU - Guzman-Novoa, Ernesto
AU - Horsburgh, Robert
AU - Kence, Meral
AU - Kilonzo, Joseph
AU - Kükrer, Mert
AU - Le Conte, Yves
AU - Mazzeo, Gaetana
AU - Mota, Fernando
AU - Muli, Elliud
AU - Oskay, Devrim
AU - Ruiz-Martínez, José A.
AU - Oliveri, Eugenia
AU - Pichkhaia, Igor
AU - Romane, Abderrahmane
AU - Sanchez, Cesar Guillen
AU - Sikombwa, Evans
AU - Satta, Alberto
AU - Scannapieco, Alejandra A.
AU - Stanford, Brandi
AU - Soroker, Victoria
AU - Velarde, Rodrigo A.
AU - Vercelli, Monica
AU - Huang, Zachary
N1 - We 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\u00A0(Dept. of Biology,\u00A0Uppsala University. Sweden),\u00A0Arnaud 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\u00A0and 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.
Project partially funded by the National Science Foundation under Grant No. NSF-OISE #1545803; NSF_HRD #1736019; NSF-DEB #1826729; as well the Puerto Rico Science, Technology and Research Trust Grant No. PRSTRT #2022-00001 and PRSTRT # 2020-00081. This work was also supported by the United States Department of Agriculture Animal & Plant Health Inspection Service Grant No. USDA-APHIS #AP20PPQS & T00C009.
We 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.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Diagnostic
KW - Hierarchical agglomerative clustering
KW - Honey bee
KW - Invasive
KW - Network
KW - SNP
UR - http://www.scopus.com/inward/record.url?scp=85202347552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85202347552&partnerID=8YFLogxK
U2 - 10.1186/s12859-024-05776-9
DO - 10.1186/s12859-024-05776-9
M3 - Article
C2 - 39192185
AN - SCOPUS:85202347552
SN - 1471-2105
VL - 25
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - 1
M1 - 278
ER -