首页> 外文期刊>Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation >Quantitative Trait Loci Analysis for Five Milk Production Traits on Chromosome Six in the Dutch Holstein-Friesian Population
【24h】

Quantitative Trait Loci Analysis for Five Milk Production Traits on Chromosome Six in the Dutch Holstein-Friesian Population

机译:荷兰荷斯坦-弗里斯兰人口中六号染色体上五个牛奶生产性状的数量性状位点分析

获取原文
           

摘要

Twenty Dutch Holstein-Friesian families, with a total of 715 sires, were evaluated in a granddaughter experiment design for marker-QTL associations. Five traits—milk, fat and protein yield and fat and protein percent—were analyzed. Across-family analysis was undertaken using multimarker regression principles. One and two QTL models were fitted. Critical values for the test statistic were calculated empirically by permuting the data. Individual trait distributions of permuted test statistics differed and, thus distributions, had to be calculated for each trait. Experimentwise critical values, which account for evaluating marker-QTL associations on all 29 autosomal bovine chromosomes and for five traits, were calculated. A QTL for protein percent was identified in one and two QTL models and was significant at the 1 and 2% level, respectively. Extending the multimarker regression approach to an analysis including two QTL was limited by families not being informative at all markers, which resulted in singularity. Below average heterozygosity for the first and last marker lowered information content for the first and last marker bracket. Highly informative markers at the ends of the mapped chromosome would overcome the decrease in information content in the first and last marker bracket and singularity for the two QTL model.
机译:在孙女实验设计中对标记-QTL关联进行了评估,共有20个荷兰荷斯坦-弗里斯兰(Hersstein-Friesian)家庭,总共715个父本。分析了五个特征-牛奶,脂肪和蛋白质的产量以及脂肪和蛋白质的百分比。使用多标记回归原理进行了跨家庭分析。安装了一个和两个QTL模型。通过对数据进行置换,可以凭经验计算出测试统计的关键值。置换检验统计数据的各个特征分布不同,因此必须为每个特征计算分布。计算了实验上的临界值,这些临界值用于评估所有29个常染色体牛染色体上的标记-QTL关联以及五个性状。在一个和两个QTL模型中确定了一个蛋白质百分比的QTL,分别在1%和2%水平上显着。将多标记回归方法扩展到包括两个QTL的分析中,是由于家庭对所有标记的了解不足,从而导致了奇异性。低于第一个和最后一个标记的平均杂合度会降低第一个和最后一个标记括号的信息含量。在映射的染色体末端的高信息量标记将克服两个QTL模型的第一个和最后一个标记括号中的信息含量以及奇异性的减少。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号