Jamal Momeni1, Melanie Parejo2,3, Rikke Vingborg4, Maria Bouga5, Per Kryger6, Marina D Meixner7, Andone Estonba8, Rasmus O Nielsen4, Jorge Langa2, Iratxe Montes2, Laetitia Papoutsis5, Leila Farajzadeh9, Christian Bendixen9, Eliza Căuia10, Jean-Daniel Charrière3, Mary F Coffey11, Cecilia Costa12, Raffaele Dall'Olio13, Pilar De la Rúa14, M Maja Drazic15, Janja Filipi16, Thomas Galea17, Miroljub Golubovski18, Ales Gregorc19, Karina Grigoryan20, Fani Hatjina21, Rustem Ilyasov22,23, Evgeniya Ivanova24, Irakli Janashia25, Irfan Kandemir26, Aikaterini Karatasou27, Meral Kekecoglu28, Nikola Kezic29, Enikö Sz Matray30, David Mifsud31, Rudolf Moosbeckhofer32, Alexei G Nikolenko23, Alexandros Papachristoforou33, Plamen Petrov34, M Alice Pinto35, Aleksandr V Poskryakov23, Aglyam Y Sharipov36, Adrian Siceanu10, M Ihsan Soysal37, Aleksandar Uzunov7,38, Marion Zammit-Mangion39. 1. Eurofins Genomics Europe Genotyping A/S (EFEG), (Former GenoSkan A/S), Aarhus, Denmark. JamalMomeni@eurofins.dk. 2. Laboratory Genetics, University of the Basque Country (UPV/EHU), Leioa, Bilbao, Spain. 3. Swiss Bee Research Center, Agroscope, Bern, Switzerland. 4. Eurofins Genomics Europe Genotyping A/S (EFEG), (Former GenoSkan A/S), Aarhus, Denmark. 5. Laboratory of Agricultural Zoology and Entomology, Agricultural University of Athens, Athens, Greece. 6. Department of Agroecology, Aarhus University, Slagelse, Denmark. 7. Landesbetrieb Landwirtschaft Hessen, Bee Institute Kirchhain, Kirchhain, Germany. 8. Laboratory Genetics, University of the Basque Country (UPV/EHU), Leioa, Bilbao, Spain. andone.estonba@ehu.eus. 9. Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. 10. Institutul de Cercetare Dezvoltare pentru Apicultura SA, Bucharest, Romania. 11. University of Limerick, Limerick, Ireland. 12. CREA Research Centre for Agriculture and Environment, Bologna, Italy. 13. BeeSources, Bologna, Italy. 14. Veterinary Faculty, University of Murcia, Murcia, Spain. 15. Croatian Ministry of Agriculture, Zagreb, Croatia. 16. Department of Ecology, Agronomy and Aquaculture, University of Zadar, Zadar, Croatia. 17. Breeds of Origin, Haz-Zebbug, Malta. 18. MacBee Association, Skopje, North Macedonia. 19. Faculty of Agriculture and Life Sciences, University of Maribor, Maribor, Slovenia. 20. Yerevan State University, Yerevan, Armenia. 21. Department of Apiculture, Agricultural Organization 'DEMETER', Thessaloniki, Greece. 22. Division of Life Sciences, Major of Biological Sciences, and Convergence Research Center for Insect Vectors, Incheon National University, Incheon, Korea. 23. Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia. 24. University of Plovdiv "Paisii Hilendarski", Plovdiv, Bulgaria. 25. Agricultural University of Georgia, Tbilisi, Georgia. 26. Ankara University, Ankara, Turkey. 27. Federation of Greek Beekeepers' Associations, Larissa, Greece. 28. Düzce University, Düzce, Turkey. 29. University of Zagreb, Zagreb, Croatia. 30. Hungarian Bee Breeders Association, Budapest, Hungary. 31. Division of Rural Sciences and Food Systems, Institute of Earth Systems, University of Malta, Msida, Malta. 32. Österreichische Agentur für Gesundheit und Ernährungssicherheit GmbH, Wien, Austria. 33. Cyprus University of Technology, Limassol, Cyprus. 34. Agricultural University of Plovdiv, Plovdiv, Bulgaria. 35. Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Bragança, Portugal. 36. Shulgan-Tash Nature Reserve, Burzyansky District, Russia. 37. Tekirdag University, Tekirdag, Turkey. 38. Faculty of Agricultural Sciences and Food, University Ss. Cyril and Methodius, Skopje, Republic of Macedonia. 39. Department of Physiology and Biochemistry, University of Malta, Msida, Malta.
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.
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.
Entities:
Keywords:
Apis mellifera, European subspecies; Biodiversity; Conservation; Machine learning; Prediction
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