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

Autor: Cecilia Costa, Enikö Sz Matray, Per Kryger, Nikola Kezić, Plamen Petrov, Maja Drazic, Leila Farajzadeh, Jean Daniel Charrière, Aglyam Y. Sharipov, Meral Kekecoglu, Aleksandar Uzunov, Aikaterini Karatasou, M. Alice Pinto, Melanie Parejo, Fani Hatjina, Irakli Janashia, David Mifsud, M. Ihsan Soysal, Aleš Gregorc, Christian Bendixen, Jorge Langa, Adrian Siceanu, Alexei G. Nikolenko, Andone Estonba, Pilar De la Rúa, Iratxe Montes, Karina Grigoryan, Thomas Galea, Jamal Momeni, R. A. Ilyasov, Janja Filipi, Evgeniya Ivanova, Rasmus Nielsen, Alexandros Papachristoforou, Marina D. Meixner, İrfan Kandemir, Mary F. Coffey, Raffaele Dall’Olio, Maria Bouga, A. V. Poskryakov, Miroljub Golubovski, Laetitia Papoutsis, Rudolf Moosbeckhofer, Eliza Cauia, Rikke Kirstine Kaae Vingborg, Marion Zammit-Mangion
Přispěvatelé: European Commission, [Belirlenecek]
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0106 biological sciences
lcsh:QH426-470
Genotype
Range (biology)
lcsh:Biotechnology
Biodiversity
Inference
Single-nucleotide polymorphism
European subspecies
Conservation
Subspecies
Biology
01 natural sciences
Polymorphism
Single Nucleotide
03 medical and health sciences
apis mellifera
Discriminative model
lcsh:TP248.13-248.65
Machine learning
Genetics
Animals
Apis mellifera
European subspecies
Conservation
Machine learning
Prediction
Biodiversity
030304 developmental biology
biodiversity
2. Zero hunger
0303 health sciences
Genetic diversity
Geography
Methodology Article
conservation
prediction
Biological Sciences
Bees
Biological Evolution
Worker bee
Apis mellifera
European subspecies
Europe
lcsh:Genetics
010602 entomology
machine learning
Evolutionary biology
Apis mellifera
Natural Sciences
Prediction
european subspecies
Biotechnology
Zdroj: BMC Genomics
Addi: Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco
Momeni, J, Parejo, M, Nielsen, R O, Langa, J, Montes, I, Papoutsis, L, Farajzadeh, L, Bendixen, C, Cauia, E, Charrière, J-D, Coffey, M F, Costa, C, Dall'Olio, R, De la Rúa, P, Drazic, M M, Filipi, J, Galea, T, Golubovski, M, Gregorc, A, Grigoryan, K, Hatjina, F, Ilyasov, R, Ivanova, E, Janashia, I, Kandemir, I, Karatasou, A, Kekecoglu, M, Kezic, N, Matray, E S, Mifsud, D, Moosbeckhofer, R, Nikolenko, A G, Papchristoforou, A, Petrov, P, Pinto, M A, Poskryakov, A V, Sharipov, A Y, Siceanu, A, Soysal, M I, Uzunov, A, Zammit-Mangion, M, Vingborg, R, Bouga, M, Kryger, P, Meixner, M D & Estonba, A 2021, ' Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs ', BMC Genomics, vol. 22, 101 . https://doi.org/10.1186/s12864-021-07379-7
BMC Genomics, Vol 22, Iss 1, Pp 1-12 (2021)
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Addi. Archivo Digital para la Docencia y la Investigación
instname
ISSN: 1471-2164
Popis: 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. European CommissionEuropean CommissionEuropean Commission Joint Research Centre [2013.1.3-02, 613960]; Basque GovernmentBasque Government [IT1233-19] The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript. WOS:000614429400001 2-s2.0-85100413967 PubMed: 33535965
Databáze: OpenAIRE
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