Lillehammer M, Hayes B J, Meuwissen T H E, Goddard M E
Norwegian University of Life Sciences, Department of Animal and Aquacultural Sciences, 1432 As, Norway.
J Dairy Sci. 2009 Aug;92(8):4008-17. doi: 10.3168/jds.2008-1484.
Dairy farming is carried out under a wide range of production environments, including large variations in the level of feeding. Although reranking of dairy sires based on the level of feeding of their daughters has been reported, detecting the genetic mutations that cause this genotype by environment interaction has not been previously attempted. In our experiment to find genetic markers for such mutations, we selected 388 Holstein bulls from the Australian dairy bull population and genotyped them for 9,919 single nucleotide polymorphism (SNP) markers. Production data, consisting of first-lactation test-day records for milk yield, fat yield, protein yield, protein percentage, and fat percentage, from the daughters of the genotyped bulls were used to estimate the effect of each SNP, which was modeled as a regression on herd mean test-day yield, where herd mean test-day yield is a descriptor of the environment. Data were analyzed with 4 models; in 2 models, daughter records were analyzed directly, with and without taking sire relationships into account. With the other 2 models, sire reaction norms for each trait were calculated and marker effects on the sire reaction norms were estimated with and without taking sire relationships into account. The results showed that using daughter records directly and accounting for sire relationships was necessary to obtain high power and to limit the number of false positives. With this approach, SNP with significant effects were found for all traits. Log transformation of the data did not affect the power of gene detection. The significant markers were categorized according to their joint effects on production and environmental sensitivity. Potential gene candidates and application of the markers is discussed. About one-third of the significant markers affect intercept and slope in opposite directions, and some of these facilitate marker-assisted selection for robustness.
奶牛养殖在广泛的生产环境中进行，包括饲养水平的巨大差异。尽管已有报道根据其女儿的饲养水平对奶牛种公牛进行重新排名，但此前尚未尝试检测导致这种基因型与环境相互作用的基因突变。在我们寻找此类突变的遗传标记的实验中，我们从澳大利亚奶牛公牛群体中挑选了388头荷斯坦公牛，并对它们进行了9919个单核苷酸多态性（SNP）标记的基因分型。来自基因分型公牛女儿的生产数据，包括首胎泌乳期测试日的产奶量、产脂量、产蛋白量、蛋白百分比和脂肪百分比记录，用于估计每个SNP的效应，该效应被建模为对牛群平均测试日产奶量的回归，其中牛群平均测试日产奶量是环境的一个描述指标。数据用4种模型进行分析；在2种模型中，直接分析女儿的记录，分别考虑和不考虑父系关系。在另外2种模型中，计算每个性状的父系反应规范，并分别考虑和不考虑父系关系来估计标记对父系反应规范的效应。结果表明，直接使用女儿的记录并考虑父系关系对于获得高功效和限制假阳性数量是必要的。采用这种方法，发现所有性状都有显著效应的SNP。数据的对数转换不影响基因检测的功效。根据其对生产和环境敏感性的联合效应，对显著标记进行了分类。讨论了潜在的基因候选物和标记的应用。约三分之一的显著标记对截距和斜率有相反方向的影响，其中一些有助于进行稳健性的标记辅助选择。
