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首页> 外文学位 >Genomic prediction in practice: Refining a new selection tool for commercial beef cattle producers.
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Genomic prediction in practice: Refining a new selection tool for commercial beef cattle producers.

机译：实践中的基因组预测：为商业肉牛生产者完善一种新的选择工具。

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摘要

The inclusion of genomic information into animal breeding predictions can provide more accurate breeding values for selection candidates at a younger age and facilitate selection for expensive or difficult to measure traits. Genomic prediction involves using large training populations with phenotypes and high density genotypes to estimate genetic effects throughout the genome. Implementing genomic selection in the beef industry presents challenges, both in development and application. To investigate the potential for accurate across breed genomic prediction, Bayesian algorithms were used to derive prediction equations for 52,156 SNP loci using approximately 2,000 mixed purebred bulls or 3,400 crossbred cattle with deregressed EPD or phenotypes for growth and carcass traits. MBV accounted for up to 18% of genetic variation in a pooled, multi-breed analysis and up to 42% in single breed subpopulations, depending on trait, and were more accurate in Angus and Hereford breeds as these were highly represented in both multi-breed training populations. These multi-breed derived genomic predictions were compared with Angus-derived genomic predictions in a commercial cattle population consisting of Angus bulls bred on four commercial cow-calf ranches in Northern California and producing nearly 6,000 phenotyped progeny. Compared with Angus-derived genomic predictions, multi-breed genomic predictions had lower accuracy (<0.3). In contrast, Angus-derived genomic predictions using 384 or 50,000 SNP had higher average accuracy (0.38-0.61). Further, Angus-derived 50,000 SNP genomic predictions were more predictive of future progeny performance than breed association EPD and low accuracy ranch progeny derived EPD, suggesting that single breed genomic predictions derived from training populations of several thousand animals are a better selection tool for commercial producers than breed association EPD lacking any genomic input.
机译：将基因组信息纳入动物育种预测可以为更年轻的选择候选者提供更准确的育种值，并有助于选择昂贵或难以测量的性状。基因组预测涉及使用具有表型和高密度基因型的大型训练种群来估计整个基因组的遗传效应。在牛肉行业中实施基因组选择在开发和应用方面都面临挑战。为了研究跨品种基因组预测的准确潜力，使用贝叶斯算法为52,156个SNP位点推导了预测方程，使用约2,000只混合纯种公牛或3,400匹具有降低的EPD或表型生长和car体性状的杂种牛。在混合，多品种分析中，MBV占遗传变异的18％，在单个品种亚群中占42％，具体取决于性状，并且在安格斯和赫里福德品种中更准确，因为它们在多品种繁殖训练种群。将这些多品种衍生的基因组预测与安格斯衍生的基因组预测进行了比较，该商业预测由北加州四个商业牛犊牧场上繁育的安格斯公牛组成，并产生近6,000个表型后代。与来自安格斯的基因组预测相比，多品种基因组预测的准确性较低（<0.3）。相比之下，使用384或50,000 SNP的安格斯衍生的基因组预测具有更高的平均准确度（0.38-0.61）。此外，来自安格斯的50,000个SNP基因组预测比品种关联EPD和低精度牧场后代衍生的EPD更能预测未来子代表现，这表明从数千只动物的训练种群中获得的单品种基因组预测对于商业生产者而言是更好的选择工具比没有任何基因组输入的品种协会EPD。

著录项

  • 作者

    Weber, Kristina Louise Estelle.;

  • 作者单位

    University of California, Davis.;

  • 授予单位 University of California, Davis.;
  • 学科 Agriculture Animal Culture and Nutrition.;Biology Genetics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 189 p.
  • 总页数 189
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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