Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/68879
Title: | Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
Authors: | Momeni, Jamal Parejo, Melanie Nielsen, Rasmus O. Langa, Jorge Montes, Iratxe Papoutsis, Laetitia Farajzadeh, Leila Bendixen, Christian Căuia, Eliza Charrière, Jean-Daniel Coffey, Mary F. Costa, Cecilia Dall’Olio, Raffaele De la Rúa, Pilar Drazic, M. Maja Filipi, Janja Galea, Thomas Golubovski, Miroljub Gregorc, Ales Grigoryan, Karina Hatjina, Fani Ilyasov, Rustem Ivanova, Evgeniya Janashia, Irakli Kandemir, Irfan Karatasou, Aikaterini Kekecoglu, Meral Kezic, Nikola Matray, Enikö Sz. Mifsud, David Moosbeckhofer, Rudolf Nikolenko, Alexei G. Papachristoforou, Alexandros Petrov, Plamen Pinto, M. Alice Poskryakov, Aleksandr V. Sharipov, Aglyam Y. Siceanu, Adrian Soysal, M. Ihsan Uzunov, Aleksandar Zammit-Mangion, Marion Vingborg, Rikke Bouga, Maria Kryger, Per Meixner, Marina D. Estonba, Andone |
Keywords: | Insects -- Malta Hymenoptera -- Malta Apidae -- Malta Apis (Insects) -- Malta Honeybee -- Malta Honeybee -- Conservation Bees -- Conservation Biodiversity -- Malta |
Issue Date: | 2021 |
Publisher: | BMC |
Citation: | Momeni, J., Parejo, M., Nielsen, R. O., Langa, J., Montes, I., Papoutsis, L., ... & Estonba, A. (2021). Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs. BMC Genomics, 22(1), 1-12. |
Abstract: | Background: With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds
a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered
by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for
subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled
across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative
SNPs for ancestry inference. Results: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/68879 |
Appears in Collections: | Scholarly Works - InsESRSF |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
momeni_etal_2021.pdf | 1.08 MB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.