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Improving Accuracy of Genomic Prediction for Economically Important Traits in Canadian Holstein Dairy Cattle

  • Author / Creator
    Nayeri,Shadi
  • Milk is a valuable source of energy, high quality protein, and several key minerals and vitamins for humans. Selection for milk production in dairy cattle was first based on phenotype and pedigree information and the knowledge of genetic parameters for the trait of interest. However, substantial advances in molecular genetics technology including bovine SNP discovery and sequencing projects have enabled researchers to apply new selection tools (such as genomic selection or GS) to identify genetically superior animals. GS is based on linkage disequilibrium (LD) between unknown functional variants and SNP genotypes that are spread out across the whole genome. It is hypothesized in this work that incorporation of candidate causal mutations into genotyping panels can increase the accuracy of genomic predictions. These variations are expected to more likely affect the trait and to be more effective across populations and generations due to persistent LD. The objectives of this study were to identify candidate causal genes and variants for production and fertility traits in Holstein dairy cattle, and then to include the candidate variants in genomic predictions by constructing and using a custom genotyping panel. In order to develop a more balanced selection tool, fertility traits were also included in this study. In the first study, genome-wide association analysis (GWAS) was performed to identify or refine the positions of genomic regions associated with milk production, milk components and fertility traits, and these positions were used to identify genes and pathways that may influence these traits. The identified QTL regions for production traits support previous findings, overlapping with genes with known relevant biological functions identified in earlier studies such as DGAT1 and CPSF1. A significant region on chromosome 21 encompassing the gene FAM181A and not previous linked to fertility in dairy cattle was identified for the calving to first service interval and days open traits. A functional enrichment analysis of the association results yielded GO terms consistent with the specific phenotypes tested; for example, GO term GO:0007595 (lactation) for milk production (MILK) and GO:0040019 (positive regulation of embryonic development) for calving to first service interval (CTFS). In the second study, GWAS was performed to determine the locations of genome regions affecting lifetime profit index (LPI), female fertility (age at first service, cow first service to conception, heifer and cow non-return rate) and longevity (direct and indirect herd life and daughter herd life) in the Canadian Holstein dairy cattle population. As with study 1, the results overlap in part with previous findings and some novel regions were discovered, specifically loci on BTA13 and BTA27 associated with lactation persistency. Previously proposed causative and candidate genes supported by this work include GRINA while new candidates are SLC2A4RG and THRB. In the third study, a custom genotyping panel was designed using the GWAS results from the first two studies, sequence and SNP information from a variety of sources, with the goal of including candidate causal mutations. The new Affymetrix panel, termed 80K, was evaluated as a tool for improving genomic predictions. The effects of combining the panel with the existing 50K panel (creating a 124K panel) and of using only those SNPs that overlap with transcribed sequences (transcriptome panel) were also investigated. The results showed that a small increase in the accuracy of genomic prediction (0.57% averaged across all traits) was achieved by incorporating the genotypes of candidate variants identified through GWAS. The accuracy of prediction using the transcriptome panel was better (0.72% averaged across all traits). In summary, GWAS results have detected several regions associated with milk production, LPI, longevity and fertility traits in Canadian Holstein cattle. Most of these regions were identified in other studies; however, novel regions of association were detected for days open, calving to first service interval and lactation persistency. These novel regions can be used to guide future mapping and functional analysis to identify genes and sequence differences that explain variations in these traits. The genomic prediction results obtained through the use of custom genotyping panel show a small increase in the accuracy; however, the accuracy was better for a subset of variants selected within the transcribed regions. Coupling variant annotation information with more recent approaches, including imputation to the sequence data, may lead to better prediction accuracies.

  • Subjects / Keywords
  • Graduation date
    2017-06:Spring 2017
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3MP4W08G
  • 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
    Doctoral
  • Department
    • Department of Agricultural, Food, and Nutritional Science
  • Specialization
    • Animal Science
  • Supervisor / co-supervisor and their department(s)
    • Paul Stothard, Department of Agricultural, Food and Nutritional Science
  • Examining committee members and their departments
    • Michael Steele, Department of Agricultural, Food and Nutritional Science, University of Alberta
    • Stephen Miller, Centre for Genetic Improvement of Livestock, University of Guelph
    • Michael Dyck, Department of Agricultural, Food and Nutritional Science, University of Alberta
    • Christian Maltecca, Genetics And Genomics Animal Science, North Carolina State University
    • Stephen Moore, The University Of QueenslandQueensland Alliance for Agriculture and Food Innovation