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Whole genome scan of QTL for ultrasound and carcass merit traits in beef cattle

  • Author / Creator
    Nalaila, Sungael
  • A whole genome scan was conducted to identify and fine map QTL regions for ultrasound and carcass merit traits in beef cattle. A total of 465 steers and bulls, genotyped for 4592 SNPs, were analysed for 16 ultrasound and carcass merit traits using interval mapping, single marker regression and Bayesian shrinkage approaches. Thirty QTL and 22 SNPs associated with traits were identified by interval mapping and single marker regression respectively. In Bayesian shrinkage estimation, 218 QTL were identified, wherein 11 of the 30 QTL identified by interval mapping were confirmed. The proportions of QTL variance on the trait variations estimated by Bayesian shrinkage analysis were relatively small. They ranged from 0.1 to 4.8% compared to 6.1 to 11.7% in interval mapping because the QTL in Bayesian approach were adjusted to remove effects of other QTL in the genome. These results are useful for detection of underlying causative QTN variants.

  • Subjects / Keywords
  • Graduation date
    2010-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3HP72
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Agricultural, Food and Nutritional Science
  • Supervisor / co-supervisor and their department(s)
    • Wang, Zhiquan (Agricultural, Food and Nutritional Science)
    • Li, Changxi (Agricultural, Food and Nutritional Science)
  • Examining committee members and their departments
    • Dixon, Walter (Agricultural, Food and Nutritional Science)
    • Li, Changxi (Agricultural, Food and Nutritional Science)
    • Moore, Steve (Agricultural, Food and Nutritional Science)
    • Wang, Zhiquan (Agricultural, Food and Nutritional Science)
    • Lin, Guohui (Computing Science)