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. 2024 Aug 27;25(1):278.
doi: 10.1186/s12859-024-05776-9.

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

Ravikiran Donthu #  1   2 , Jose A P Marcelino #  1   3 , Rosanna Giordano #  4   5 , Yudong Tao #  6 , Everett Weber  7 , Arian Avalos  8 , Mark Band  9 , Tatsiana Akraiko  9 , Shu-Ching Chen  10 , Maria P Reyes  11 , Haiping Hao  12 , Yarira Ortiz-Alvarado  13 , Charles A Cuff  13 , Eddie Pérez Claudio  14 , Felipe Soto-Adames  3 , Allan H Smith-Pardo  15 , William G Meikle  16 , Jay D Evans  17 , Tugrul Giray  18 , Faten B Abdelkader  19 , Mike Allsopp  20 , Daniel Ball  21 , Susana B Morgado  22 , Shalva Barjadze  23 , Adriana Correa-Benitez  24 , Amina Chakir  25 , David R Báez  26 , Nabor H M Chavez  27 , Anne Dalmon  28 , Adrian B Douglas  29 , Carmen Fraccica  3 , Hermógenes Fernández-Marín  30 , Alberto Galindo-Cardona  31 , Ernesto Guzman-Novoa  32 , Robert Horsburgh  3 , Meral Kence  33 , Joseph Kilonzo  34 , Mert Kükrer  33   35 , Yves Le Conte  28 , Gaetana Mazzeo  36 , Fernando Mota  37 , Elliud Muli  34   38 , Devrim Oskay  39 , José A Ruiz-Martínez  40 , Eugenia Oliveri  41 , Igor Pichkhaia  42 , Abderrahmane Romane  25 , Cesar Guillen Sanchez  43 , Evans Sikombwa  21 , Alberto Satta  44 , Alejandra A Scannapieco  45 , Brandi Stanford  3 , Victoria Soroker  46 , Rodrigo A Velarde  47 , Monica Vercelli  48 , Zachary Huang  49
Affiliations

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

Ravikiran Donthu et al. BMC Bioinformatics. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of honey bee samples. Geographic location of honey bee specimens assayed using the Fluidigm and Agena platforms are indicated by blue dots
Fig. 2
Fig. 2
HBeeID tool. Workflow diagram of procedure undertaken to generate the HBeeID tool
Fig. 3
Fig. 3
Process to identify diagnostic SNPs. DAPC plots showing clusters of samples of different HB populations used during the process to identify SNPs to differentiate populations. DAPC plots were generated using SNPs that differentiate a All samples into eight groups; b Puerto Rico HB; c A. m. syriaca HBs; d Africanized HBs; and e A. m. carnica, A. m. ligustica, EU and US domestic from all other HB populations. Meaning of acronyms used: SSA—Sub Saharan African; gAHB—Puerto Rico Bees (gentle Africanized HB); BrAHB—Brazilian AHB; CMAHB—Central Mexico AHB; US and EU Domestic; SWNOR—Sweden, Norway; US domestic—US HB; EU domestic—EU HB; carnica—carnica HB; syriaca—syriaca HB; CALDOMH—carnica, anatoliaca, ligustica; iberiensis—iberiensis HB; ligustica—ligustica HB
Fig. 4
Fig. 4
Visualization of hierarchical clustering. Diagram illustrating the agglomerative hierarchical clustering performed, using SNP genotypes, for the development of the Knowledge Base Network used to form HBeeID. Labels indicate the position of the respective groups and the level at which they clustered. The proportional graphs below show the genotype profile of honey bee samples for the 272 SNPs for selected nodes. The length of the bars represents the proportion of 0 (Green), 1 (Gray), or 2 (Pink) genotype in the honey bee samples that comprise each node. The higher the bar the higher the proportion of the corresponding state
Fig. 5
Fig. 5
a, b, c. a Genotypic relationship of honey bee samples. PCA plot generated using 874 HB samples genotyped with 272 SNPs using the Fluidigm genotyping platform. The reader is encouraged to see the interactive 3D version of this figure available to download in the supplementary section as Additional file 5_3D interactive plot and on github or Additional file 5 (html interface). b PCA plot of a subset of all samples from Africa, Italy, Malta, Israel, the Iberian Peninsula, Turkey, and the Republic of Georgia demarcated as per the HB group assignation given when using reference set III. c PCA of HB samples identified as per their assignation to the A, C, M, or O groups obtained in the GeneClass2 analysis run with reference set III
Fig. 6
Fig. 6
Assignation of honey bees with different reference data. Geographic distribution of samples from Argentina and Florida (US) and their genetic assignations obtained with GeneClass2 using three different combinations of samples from Avalos et al. [84] and Wallberg et al. [53] as reference
Fig. 7
Fig. 7
Visualization of agglomerative clusters. Dendrogram that visualizes the agglomerative clusters generated using the Ward clustering method for 874 samples genotyped for the 272 selected SNPs in HBeeID. The countries and their respective abbreviations are listed below the dendrogram
See this image and copyright information in PMC

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