| Title: | Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
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| Authors: | ID Momeni, Jamal (Author)
ID Parejo, Melanie (Author)
ID Nielsen, Rasmus O. (Author)
ID Langa, Jorge (Author)
ID Montes, Iratxe (Author)
ID Papoutsis, Laetitia (Author)
ID Farajzadeh, Leila (Author)
ID Brendixen, Christian (Author)
ID Cǎuia, Eliza (Author)
ID Gregorc, Aleš (Author), et al. |
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| Files: |  Momeni-2021-Authoritative_subspecies_diagnosis.pdf (1,06 MB)
MD5: CC3D9CBFDFA6BB7905316DF548BEF095
 https://doi.org/10.1186/s12864-021-07379-7
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FKBV - Faculty of Agriculture and Life Sciences
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| 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. |
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| Keywords: | Apis mellifera, European suspecies, conservation, machine learning, prediction, biodiversity |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 29.05.2020 |
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| Article acceptance date: | 08.01.2021 |
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| Publication date: | 03.02.2021 |
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| Publisher: | BioMed Central |
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| Year of publishing: | 2021 |
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| Number of pages: | Str. 1-12 |
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| Numbering: | Letn. 22, Št. članka 101 |
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| PID: | 20.500.12556/DKUM-90880  |
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| UDC: | 638.1 |
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| ISSN on article: | 1471-2164 |
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| COBISS.SI-ID: | 51100163 |
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| DOI: | 10.1186/s12864-021-07379-7 |
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| Publication date in DKUM: | 01.10.2024 |
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| Views: | 229 |
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| Downloads: | 9 |
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| Metadata: |    |
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| Categories: | Misc.
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