University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Veterinary and Agricultural Sciences
  • Melbourne Veterinary School
  • Veterinary Clinical Sciences
  • Veterinary Clinical Sciences - Research Publications
  • View Item
  • Minerva Access
  • Veterinary and Agricultural Sciences
  • Melbourne Veterinary School
  • Veterinary Clinical Sciences
  • Veterinary Clinical Sciences - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep

    Thumbnail
    Download
    Published version (1.177Mb)

    Citations
    Scopus
    Web of Science
    Altmetric
    27
    21
    Author
    Raadsma, HW; Jonas, E; McGill, D; Hobbs, M; Lam, MK; Thomson, PC
    Date
    2009-10-22
    Source Title
    Genetics Selection Evolution
    Publisher
    BMC
    University of Melbourne Author/s
    McGill, David
    Affiliation
    Veterinary Clinical Sciences
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Raadsma, H. W., Jonas, E., McGill, D., Hobbs, M., Lam, M. K. & Thomson, P. C. (2009). Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep. GENETICS SELECTION EVOLUTION, 41 (1), https://doi.org/10.1186/1297-9686-41-45.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255484
    DOI
    10.1186/1297-9686-41-45
    Abstract
    An (Awassi x Merino) x Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [53039]
    • Veterinary Clinical Sciences - Research Publications [118]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors