Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

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  • Jamal Momeni, Eurofins Genomics Europe Genotyping A/S (EFEG), Denmark
  • Melanie Parejo, University of the Basque Country UPV/EHU, Swiss Bee Research Center, Agroscope Liebefeld-Posieux Research Station ALP, CH-3003 Bern, Switzerland., Spain
  • Rasmus O. Nielsen, Eurofins Genomics Europe Genotyping A/S (EFEG), Denmark
  • Jorge Langa, University of the Basque Country UPV/EHU, Spain
  • Iratxe Montes, University of the Basque Country UPV/EHU, Spain
  • Laetitia Papoutsis, Agricultural University of Athens, Greece
  • Leila Farajzadeh
  • Christian Bendixen
  • ,
  • Eliza Cauia, Institutul de Cercetare Dezvoltare pentru Apicultura SA, Bucharest, Romania
  • Jean-Daniel Charrière, Swiss Bee Research Center, Agroscope Liebefeld-Posieux Research Station ALP, CH-3003 Bern, Switzerland., Switzerland
  • Mary F. Coffey, University of Limerick, Ireland
  • Cecilia Costa, CREA Research Centre for Agriculture and Environment, Italy
  • Raffaele Dall'Olio, BeeSources, Bologna, Italy
  • Pilar De la Rúa, University of Murcia, Spain
  • M. Maja Drazic, Croatian Ministry of Agriculture, Croatia
  • Janja Filipi, University of Zadar, Croatia
  • Thomas Galea, Breeds of Origin, Haz-Zebbug, Malta
  • Miroljub Golubovski, MacBee Association, Skopje, Macedonia, The Former Yugoslav Republic of
  • Ales Gregorc, University of Maribor, Slovenia
  • Karina Grigoryan, Yerevan State University, Armenia
  • Fani Hatjina, Department of Apiculture, Institute of Animal Science - Hellenic Agricultural Organization 'DEMETER', Greece
  • Rustem Ilyasov, University of Incheon, Ufa Federal Research Centre of the Russian Academy of Sciences, Korea, Republic of
  • Evgeniya Ivanova, University of Plovdiv "Paisii Hilendarski", Bulgaria
  • Irakli Janashia, Agricultural University of Georgia, Tbilisi, Georgia
  • Irfan Kandemir, Ankara University, Turkey
  • Aikaterini Karatasou, Federation of Greek Beekeepers’ Associations, Greece
  • Meral Kekecoglu, Duzce University, Turkey
  • Nikola Kezic, University of Zagreb, Croatia
  • Enikö Sz. Matray, Hungarian Bee Breeders Association, Hungary
  • David Mifsud, University of Malta, Malta
  • Rudolf Moosbeckhofer, Österreichische Agentur für Gesundheit und Ernährungssicherheit GmbH, Austria
  • Alexei G. Nikolenko, Ufa Federal Research Centre of the Russian Academy of Sciences, Russian Federation
  • Alexandros Papchristoforou, Cyprus University of Technology, Cyprus
  • Plamen Petrov, Agricultural University of Plovdiv, Bulgaria
  • M. Alice Pinto, Centro de Investigacao de Montanha, Portugal
  • Aleksandr V. Poskryakov, Ufa Federal Research Centre of the Russian Academy of Sciences, Russian Federation
  • Aglyam Y. Sharipov, Shulgan-Tash Nature Reserve, Burzyansky District, Russian Federation
  • Adrian Siceanu, Institutul de Cercetare Dezvoltare pentru Apicultura SA, Bucharest, Romania
  • M. Ihsan Soysal, Tekirdağ Namık Kemal University (NKÜ), Turkey
  • Aleksandar Uzunov, LLH Bee Institute, Kirchhain, University Ss. Cyril and Methodius, Germany
  • Marion Zammit-Mangion, University of Malta, Malta
  • Rikke Vingborg, Eurofins Genomics Europe Genotyping A/S (EFEG), Denmark
  • Maria Bouga, Agricultural University of Athens, Greece
  • Per Kryger
  • Marina D. Meixner, LLH Bee Institute, Kirchhain, Germany
  • Andone Estonba, University of the Basque Country, Spain

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 F ST) 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.

Original languageEnglish
Article number101
JournalBMC Genomics
Volume22
Number of pages12
ISSN1471-2164
DOIs
Publication statusPublished - Feb 2021

    Research areas

  • Apis mellifera, European subspecies, Biodiversity, Conservation, Machine learning, Prediction

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