Spelman R J, Coppieters W, Karim L, van Arendonk J A, Bovenhuis H
Department of Animal Breeding, Wageningen Institute of Animal Sciences, Wageningen Agricultural University, The Netherlands.
Genetics. 1996 Dec;144(4):1799-808. doi: 10.1093/genetics/144.4.1799.
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.
在一项孙女实验设计中,对20个荷兰荷斯坦-弗里生家系(共715头种公牛)进行了标记-QTL关联评估。分析了五个性状——产奶量、乳脂产量、乳蛋白产量、乳脂率和乳蛋白率。采用多标记回归原理进行家系间分析。拟合了单QTL模型和双QTL模型。通过对数据进行置换,凭经验计算检验统计量的临界值。置换后的检验统计量的个体性状分布不同,因此必须为每个性状计算分布。计算了实验范围内的临界值,该临界值考虑了在所有29条常染色体牛染色体上评估标记-QTL关联以及五个性状。在单QTL模型和双QTL模型中均鉴定出一个乳蛋白率QTL,分别在1%和2%水平上显著。将多标记回归方法扩展到包括两个QTL的分析受到家系在所有标记上缺乏信息性的限制,这导致了奇异性。第一个和最后一个标记的杂合度低于平均水平,降低了第一个和最后一个标记区间的信息含量。在已定位染色体末端的高信息性标记将克服第一个和最后一个标记区间信息含量的降低以及双QTL模型的奇异性。