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Developing reduced SNP assays from whole-genome sequence data to estimate introgression in an organism with complex genetic patterns, the Iberian honeybee (Apis mellifera iberiensis)

2018Journal article Open access
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dc.contributor.authorHenriques, Dora
dc.contributor.authorParejo, Melanie
dc.contributor.authorVignal, Alain
dc.contributor.authorWragg, David
dc.contributor.authorWallberg, Andreas
dc.contributor.authorWebster, Matthew T.
dc.contributor.authorPinto, M. Alice
dc.date.accessioned2018-02-19T10:00:00Z
dc.date.accessioned2019-02-04T17:09:17Z
dc.date.available2018-01-19T10:00:00Z
dc.date.available2019-02-04T17:09:17Z
dc.date.issued2018
dc.description.abstractThe most important managed pollinator, the honeybee (Apis mellifera L.), has been subject to a growing number of threats. In western Europe, one such threat is large- scale introductions of commercial strains (C- lineage ancestry), which is leading to introgressive hybridization and even the local extinction of native honeybee populations (M- lineage ancestry). Here, we developed reduced assays of highly informative SNPs from 176 whole genomes to estimate C- lineage introgression in the most diverse and evolutionarily complex subspecies in Europe, the Iberian honeybee (Apis mellifera iberiensis). We started by evaluating the effects of sample size and sampling a geographically restricted area on the number of highly informative SNPs. We demonstrated that a bias in the number of fixed SNPs (FST = 1) is introduced when the sample size is small (N ≤ 10) and when sampling only captures a small fraction of a population’s genetic diversity. These results underscore the importance of having a representative sample when developing reliable reduced SNP assays for organisms with complex genetic patterns. We used a training data set to design four independent SNP assays selected from pairwise FST between the Iberian and C- lineage honeybees. The designed assays, which were validated in holdout and simulated hybrid data sets, proved to be highly accurate and can be readily used for monitoring populations not only in the native range of A. m. iberiensis in Iberia but also in the introduced range in the Balearic islands, Macaronesia and South America, in a time- and cost- effective manner. While our approach used the Iberian honeybee as model system, it has a high value in a wide range of scenarios for the monitoring and conservation of potentially hybridized domestic and wildlife populations.en_EN
dc.description.sponsorshipWe thank numerous researchers, beekeepers and beekeeping associations who provided samples and assisted with sampling in the several countries. João Costa, Instituto Gulbenkian Ciência, designed the multiplexes with the Assay Design tool. José Rufino provided computational resources at IPB. A. m. ligustica drone samples were kindly provided by Cecilia Costa, from the Council for Agricultura Research and Economics Honey Bee and Silkworm Unit, Bologna, Italy. John C. Patton, Phillip San Miguel, Paul Parker, Rick Westerman, from the University of Purdue, USA, sequenced most honeybees and many reference samples. Reference samples were also sequenced at the GeT PlaGe platform in Toulouse, France. An earlier version of the manuscript was improved by the constructive comments made by two anonymous reviewers. Dora Henriques was supported by a PhD scholarship from the Fundação para a Ciência e Tecnologia (FCT) (SFRH/BD/84195/2012) and Melanie Parejo by the Swiss Federal Office for Agriculture FOAG and the Sur-la- Croix foundation, Basel. MAP is a member of and receives support from the COST Action FA1307 (SUPER-B). Analyses were performed at UPPMAX, Uppsala University, and UBELIX, University of Bern. The Roslin Institute was supported by the Biotechnology and Biological Services Research Council (BBS/E/D/20211551). This research was funded through the projects PTDC/BIA-BEC/ 099640/2008 (FCT and COMPETE/ QREN/EU) and the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the national funders FCT (Portugal), “Agence Nationale de la Recherche” (France) and “Ministério de Economia y Competividade” (Spain) to MAP.
dc.description.versioninfo:eu-repo/semantics/publishedVersionen_EN
dc.identifier.citationHenriques, Dora; Parejo, Melanie; Vignal, Alain; Wragg, David; Wallberg, Andreas; Webster, Matthew T.; Pinto, M. Alice (2018). Developing reduced SNP assays from whole-genome sequence data to estimate introgression in an organism with complex genetic patterns, the Iberian honeybee (Apis mellifera iberiensis). Evolutionary Applications. ISSN 1752-4571. 11, p. 1270-1282en_EN
dc.identifier.doi10.1111/eva.12623en_EN
dc.identifier.issn1752-4571
dc.identifier.urihttp://hdl.handle.net/10198/18729
dc.language.isoeng
dc.peerreviewedyesen_EN
dc.relationBBS/E/D/20211551
dc.subjectApis mellifera iberiensisen_EN
dc.subjectfixation indexen_EN
dc.subjectinformative SNPsen_EN
dc.subjectreduced SNP assaysen_EN
dc.titleDeveloping reduced SNP assays from whole-genome sequence data to estimate introgression in an organism with complex genetic patterns, the Iberian honeybee (Apis mellifera iberiensis)en_EN
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F84195%2F2012/PT
oaire.fundingStreamSFRH
person.familyNameHenriques
person.familyNamePinto
person.givenNameDora
person.givenNameMaria Alice
person.identifier.ciencia-id291F-986F-07DA
person.identifier.ciencia-idF814-A1D0-8318
person.identifier.orcid0000-0001-7530-682X
person.identifier.orcid0000-0001-9663-8399
person.identifier.scopus-author-id55761737300
person.identifier.scopus-author-id8085507800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccessen_EN
rcaap.typearticleen_EN
relation.isAuthorOfPublicationd2abd09f-a90c-4cfb-9a60-7fc32f56184d
relation.isAuthorOfPublication0667fe04-7078-483d-9198-56d167b19bc5
relation.isAuthorOfPublication.latestForDiscovery0667fe04-7078-483d-9198-56d167b19bc5
relation.isProjectOfPublication0f7cd1b1-ad88-4908-8771-659d5cbca5f6
relation.isProjectOfPublication.latestForDiscovery0f7cd1b1-ad88-4908-8771-659d5cbca5f6

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