Open Access
BMC Genomics, volume 22, issue 1, publication number 101
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs
Jamal Momeni
1
,
Melanie Parejo
2, 3
,
Rasmus O. Nielsen
1
,
Jorge Langa
2
,
Iratxe Montes
2
,
Laetitia Papoutsis
4
,
Leila Farajzadeh
5
,
Christian BENDIXEN
5
,
Eliza Căuia
6
,
Jean Daniel Charrière
3
,
Mary F Coffey
7
,
Cecilia Costa
8
,
Raffaele Dallolio
9
,
Pilar De La Rúa
10
,
M Maja Drazic
11
,
Janja Filipi
12
,
Thomas Galea
13
,
Miroljub Golubovski
14
,
Aleš Gregorc
15
,
Karina Grigoryan
16
,
Fani Hatjina
17
,
Rustem Ilyasov
18, 19
,
Evgeniya Ivanova
20
,
Irakli Janashia
21
,
İrfan Kandemir
22
,
Aikaterini Karatasou
23
,
Meral Kekeçoğlu
24
,
Nikola Kezić
25
,
Enikö Sz Matray
26
,
David Mifsud
27
,
Rudolf Moosbeckhofer
28
,
Alexei G Nikolenko
19
,
Alexandros Papachristoforou
29
,
Plamen Petrov
30
,
M Alice Pinto
31
,
Aleksandr V Poskryakov
19
,
Aglyam Y Sharipov
32
,
Adrian Siceanu
6
,
M. İhsan Soysal
33
,
Aleksandar Uzunov
34, 35
,
Marion Zammit Mangion
36
,
Rikke Vingborg
1
,
Maria Bouga
4
,
Per Kryger
37
,
Marina D Meixner
34
,
A Estonba
2
1
Eurofins Genomics Europe Genotyping A/S (EFEG), (Former GenoSkan A/S), Aarhus, Denmark
|
3
Swiss Bee Research Center, Agroscope, Bern, Switzerland
|
4
6
Institutul de Cercetare Dezvoltare pentru Apicultura SA, Bucharest, Romania
|
9
BeeSources, Bologna, Italy
|
11
Croatian Ministry of Agriculture, Zagreb, Croatia
|
13
Breeds of Origin, Haz-Zebbug, Malta
|
14
MacBee Association, Skopje, North Macedonia
|
17
Department of Apiculture, Agricultural Organization ‘DEMETER’, Thessaloniki, Greece
|
23
Federation of Greek Beekeepers’ Associations, Larissa, Greece
|
26
Hungarian Bee Breeders Association, Budapest, Hungary
|
28
Österreichische Agentur für Gesundheit und Ernährungssicherheit GmbH, Wien, Austria
|
31
32
Shulgan-Tash Nature Reserve, Burzyansky District, Russia
|
33
Tekirdag University, Tekirdag, Turkey
|
34
Landesbetrieb Landwirtschaft Hessen, Bee Institute Kirchhain, Kirchhain, Germany
|
35
Faculty of Agricultural Sciences and Food, University Ss. Cyril and Methodius, Skopje, Republic of Macedonia
|
Publication type: Journal Article
Publication date: 2021-02-03
Genetics
Biotechnology
Abstract
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. 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. 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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