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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.
ISSN: 1471-2164
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 
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 
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