University of Hertfordshire Research Archive

        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UHRABy Issue DateAuthorsTitlesThis CollectionBy Issue DateAuthorsTitles

        Arkivum Files

        My Downloads
        View Item 
        • UHRA Home
        • University of Hertfordshire
        • Research publications
        • View Item
        • UHRA Home
        • University of Hertfordshire
        • Research publications
        • View Item

        Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

        View/Open
        Final Published version (PDF, 1Mb)
        Author
        Ziliang, Luo
        Meng, Wang
        Yan, Long
        Huang, Yongju
        Shi, Lei
        Zhang, Chunyu
        Liu, Xiang
        Fitt, Bruce
        Xiang, Jinxia
        Mason, Annaliese
        Snowdon, Rod
        Liu, Peifa
        Meng, Jinling
        Zou, Jun
        Attention
        2299/19660
        Abstract
        Most 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.
        Publication date
        2017-08-30
        Published in
        TAG: Theoretical and Applied Genetics
        Published version
        https://doi.org/10.1007/s00122-017-2911-7
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/19660
        Relations
        School of Life and Medical Sciences
        Metadata
        Show full item record
        Keep in touch

        © 2019 University of Hertfordshire

        I want to...

        • Apply for a course
        • Download a Prospectus
        • Find a job at the University
        • Make a complaint
        • Contact the Press Office

        Go to...

        • Accommodation booking
        • Your student record
        • Bayfordbury
        • KASPAR
        • UH Arts

        The small print

        • Terms of use
        • Privacy and cookies
        • Criminal Finances Act 2017
        • Modern Slavery Act 2015
        • Sitemap

        Find/Contact us

        • T: +44 (0)1707 284000
        • E: ask@herts.ac.uk
        • Where to find us
        • Parking
        • hr
        • qaa
        • stonewall
        • AMBA
        • ECU Race Charter
        • disability confident
        • AthenaSwan