Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/24226 |
Resumo: | 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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Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPsApis mellifera, European subspeciesBiodiversityConservationMachine learningPredictionWith 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.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.Biblioteca Digital do IPBMomeni, JamalParejo, MelanieNielsen, Rasmus O.Langa, JorgeMontes, IratxePapoutsis, LaetitiaFarajzadeh, LeilaBendixen, ChristianCăuia, ElizaCharrière, Jean DanielCoffey, Mary F.Costa, CeciliaDall'Olio, RaffaeleDe la Rúa, PilarDražić, Marica MajaFilipi, JanjaGalea, ThomasGolubovski, MiroljubGregorc, AlešGrigoryan, KarinaHatjina, FaniIlyasov, RustemIvanova, Evgeniya NeshovaJanashia, IrakliKandemir, IrfanKaratasou, AikateriniKekecoglu, MeralKezic, NikolaMatray, Enikö SzMifsud, DavidMoosbeckhofer, RudolfNikolenko, Alexei G.Papachristoforou, AlexandrosPetrov, PlamenPinto, M. AlicePoskryakov, Aleksandr V.Sharipov, Aglyam Y.Siceanu, AdrianSoysal, M. IhsanUzunov, AleksandarZammit Mangion, MarionVingborg, RikkeBouga, MariaKryger, PerMeixner, Marina D.Estonba, Andone2018-01-19T10:00:00Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/24226engMomeni, 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; Drazic, M. Maja; Filipi, Janja; Galea, Thomas; Golubovski, Miroljub; Gregorc, Ales; Grigoryan, Karina; Hatjina, Fani; Ilyasov, Rustem; Ivanova, Evgeniya; 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 (2021). Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs. BMC Genomics. ISSN . 22:1, p.10.1186/s12864-021-07379-7info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:54:08Zoai:bibliotecadigital.ipb.pt:10198/24226Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:15:04.848960Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
title |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
spellingShingle |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs Momeni, Jamal Apis mellifera, European subspecies Biodiversity Conservation Machine learning Prediction |
title_short |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
title_full |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
title_fullStr |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
title_full_unstemmed |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
title_sort |
Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs |
author |
Momeni, Jamal |
author_facet |
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 |
author_role |
author |
author2 |
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 |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
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 |
dc.subject.por.fl_str_mv |
Apis mellifera, European subspecies Biodiversity Conservation Machine learning Prediction |
topic |
Apis mellifera, European subspecies Biodiversity Conservation Machine learning Prediction |
description |
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. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-19T10:00:00Z 2021 2021-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/24226 |
url |
http://hdl.handle.net/10198/24226 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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; Drazic, M. Maja; Filipi, Janja; Galea, Thomas; Golubovski, Miroljub; Gregorc, Ales; Grigoryan, Karina; Hatjina, Fani; Ilyasov, Rustem; Ivanova, Evgeniya; 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 (2021). Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs. BMC Genomics. ISSN . 22:1, p. 10.1186/s12864-021-07379-7 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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