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Percorrer por autor "Ilyasov, Rustem"

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  • Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs
    Publication . 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; Dražić, Marica Maja; Filipi, Janja; Galea, Thomas; Golubovski, Miroljub; Gregorc, Aleš; Grigoryan, Karina; Hatjina, Fani; Ilyasov, Rustem; Ivanova, Evgeniya Neshova; Janashia, Irakli; Kandemir, Irfan; 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
    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.
    2021Artigo científico Acesso aberto Ver mais
  • Using the software DeepWings© to classify honey bees across europe through wing geometric morphometrics
    Publication . Yadró Garcia, Carlos A.; Rodrigues, Pedro João; Tofilski, Adam; Elen, Dylan; McCormack, Grace P.; Oleksa, Andrzej; Henriques, Dora; Ilyasov, Rustem; Kartashev, Anatoly; Bargain, Christian; Fried, Balser; Pinto, M. Alice
    DeepWings© is a software that uses machine learning to automatically classify honey bee subspecies by wing geometric morphometrics. Here, we tested the five subspecies classifier (A. m. carnica, Apis mellifera caucasia, A. m. iberiensis, Apis mellifera ligustica, and A. m. mellifera) of DeepWings© on 14,816 wing images with variable quality and acquired by different beekeepers and researchers. These images represented 2601 colonies from the native ranges of the M-lineage A. m. iberiensis and A. m. mellifera, and the C-lineage A. m. carnica. In the A. m. iberiensis range, 92.6% of the colonies matched this subspecies, with a high median probability (0.919). In the Azores, where the Iberian subspecies was historically introduced, a lower proportion (85.7%) and probability (0.842) were observed. In the A. m mellifera range, only 41.1 % of the colonies matched this subspecies, which is compatible with a history of C-derived introgression. Yet, these colonies were classified with the highest probability (0.994) of the three subspecies. In the A. m. carnica range, 88.3% of the colonies matched this subspecies, with a probability of 0.984. The association between wing and molecular markers, assessed for 1214 colonies from the M-lineage range, was highly significant but not strong (r = 0.31, p < 0.0001). The agreement between the markers was influenced by C-derived introgression, with the best results obtained for colonies with high genetic integrity. This study indicates the good performance of DeepWings© on a realistic wing image dataset.
    2022Artigo científico Acesso aberto Ver mais