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Ireland participates in the latest advances in developing Molecular tools for genotyping Honey Bees

posted Feb 10, 2021, 3:24 AM by Helen Mooney
One of the reasons for membership of FIBKA or IBA is to ensure the Irish beekeepers are represented at European level when research grants are allocated. Irish scientists regularly participate in collaborative research across Europe and have produced excellent stand alone data on the genotype of our locally adapted dark bees.  Although some of the papers are very technical, there is no doubt that the data is readily shared via our irish beekeeping magazines and press release, which, again, are only available to members of the Federation of irish Beekeepers Associations (FIBKA) or the Irish Beekeepers Association CLG (IBA CLG).

Here we include the citation and abstract of the latest collaboration, involving Dr Mary Coffey (UL and Dept Agriculture) which aims to improve the accuracy of establishing the genetic make-up of native sub- species, purely bred hybrids as well as  bees of mixed race.

CITATION: MC Genomics. 22. 10.1186/s12864-021-07379-7.

Momeni, Jamal & Parejo, Melanie & Nielsen, Rasmus & Langa, Jorge & Montes, Iratxe & Papoutsis, Laetitia & Farajzadeh, Leila & Bendixen, Christian & Cauia, Eliza & Charrière, Jean-Daniel & Coffey, Mary & Costa, Cecilia & Dall'Olio, Raffaele & De la Rua, Pilar & Dražić, Maja & Filipi, Janja & Galea, Thomas & Golubovski, Miroljub & Gregorc, Aleš & Estonba, Andone. (2021). Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs. BMC Genomics. 22. 10.1186/s12864-021-07379-7. 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.

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