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https://hdl.handle.net/20.500.14279/22645
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 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 |
Major Field of Science: | Natural Sciences | Field Category: | Biological Sciences | Keywords: | Apis mellifera;European subspecies;Biodiversity;Conservation;Machine learning;Prediction | Issue Date: | Dec-2021 | Source: | BMC Genomics, 2021. vol.l 22, no. 1, articl. no. 101 | Volume: | 22 | Issue: | 1 | Journal: | BMC Genomics | 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://hdl.handle.net/20.500.14279/22645 | ISSN: | 14712164 | DOI: | 10.1186/s12864-021-07379-7 | Rights: | © The Author(s). 2021 Open Access | Type: | Article | Affiliation : | Eurofins Genomics Europe Genotyping University of the Basque Country (UPV/EHU) Swiss Bee Research Center Agricultural University of Athens Aarhus University Institutul de Cercetare Dezvoltare pentru Apicultura SA University of Limerick CREA Research Centre for Agriculture and Environment BeeSources University of Murcia Croatian Ministry of Agriculture University of Zadar Breeds of Origin MacBee Association University of Maribor Yerevan State University Hellenic Agricultural Organization “Demeter” Incheon National University Ufa Federal Research Centre of the Russian Academy of Sciences University of Plovdiv “Paisii Hilendarski” Agricultural University of Georgia Ankara University Federation of Greek Beekeepers’ Associations Düzce University University of Zagreb Hungarian Bee Breeders Association University of Malta Österreichische Agentur für Gesundheit und Ernährungssicherheit GmbH Cyprus University of Technology Agricultural University of Plovdiv Instituto Politécnico de Bragança Shulgan-Tash Nature Reserve Tekirdag University Bee Institute Kirchhain University Ss. Cyril and Methodius University of Malta Aarhus University |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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s12864-021-07379-7.pdf | Fulltext | 1.08 MB | Adobe PDF | View/Open |
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