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dc.contributor.authorZiliang, Luo
dc.contributor.authorMeng, Wang
dc.contributor.authorYan, Long
dc.contributor.authorHuang, Yongju
dc.contributor.authorShi, Lei
dc.contributor.authorZhang, Chunyu
dc.contributor.authorLiu, Xiang
dc.contributor.authorFitt, Bruce
dc.contributor.authorXiang, Jinxia
dc.contributor.authorMason, Annaliese
dc.contributor.authorSnowdon, Rod
dc.contributor.authorLiu, Peifa
dc.contributor.authorMeng, Jinling
dc.contributor.authorZou, Jun
dc.date.accessioned2018-01-30T22:26:12Z
dc.date.available2018-01-30T22:26:12Z
dc.date.issued2017-08-30
dc.identifier.citationZiliang , L , Meng , W , Yan , L , Huang , Y , Shi , L , Zhang , C , Liu , X , Fitt , B , Xiang , J , Mason , A , Snowdon , R , Liu , P , Meng , J & Zou , J 2017 , ' Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example ' , TAG: Theoretical and Applied Genetics , vol. 130 , no. 8 , pp. 1569-1585 . https://doi.org/10.1007/s00122-017-2911-7
dc.identifier.issn0040-5752
dc.identifier.otherPURE: 12090097
dc.identifier.otherPURE UUID: a5a4fbdb-52a5-4654-af96-7d7c7ad6bf5d
dc.identifier.otherScopus: 85018310295
dc.identifier.urihttp://hdl.handle.net/2299/19660
dc.description© The Author(s) 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.description.abstractMost agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/ genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.en
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofTAG: Theoretical and Applied Genetics
dc.rightsOpen
dc.titleIncorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an exampleen
dc.contributor.institutionAgriculture, Veterinary and Food Sciences
dc.contributor.institutionGeography, Environment and Agriculture
dc.contributor.institutionCrop and Environmental Protection
dc.contributor.institutionDepartment of Biological and Environmental Sciences
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionWeight and Obesity Research Group
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Life and Medical Sciences
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2017-08-30
rioxxterms.versionAM
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/s00122-017-2911-7
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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