HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Bmc
Access Rights
info:eu-repo/semantics/openAccess
Abstract
BackgroundHoney 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.
Description
Keywords
Honey bee, SNP, Invasive, Diagnostic, Hierarchical agglomerative clustering, Network
Journal or Series
Bmc Bioinformatics
WoS Q Value
N/A
Scopus Q Value
Q1
Volume
25
Issue
1
