National Association of Animal Breeders, Columbia, MO 65205.
Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705.
J Dairy Sci. 2012 Sep;95(9):5378-5383. doi: 10.3168/jds.2011-5006.
Multibreed models are currently used in traditional US Department of Agriculture (USDA) dairy cattle genetic evaluations of yield and health traits, but within-breed models are used in genomic evaluations. Multibreed genomic models were developed and tested using the 19,686 genotyped bulls and cows included in the official August 2009 USDA genomic evaluation. The data were divided into training and validation sets. The training data set comprised bulls that were daughter proven and cows that had records as of November 2004, totaling 5,331 Holstein, 1,361 Jersey, and 506 Brown Swiss. The validation data set had 2,508 Holstein, 413 Jersey, and 185 Brown Swiss bulls that were unproven (no daughter information) in November 2004 and proven by August 2009. A common set of 43,385 single nucleotide polymorphisms (SNP) was used for all breeds. Three methods of multibreed evaluation were investigated. Method 1 estimated SNP effects separately within breed and then applied those breed-specific SNP estimates to the other breeds. Method 2 estimated a common set of SNP effects from combined genotypes and phenotypes of all breeds. Method 3 solved for correlated SNP effects within each breed estimated jointly using a multitrait model where breeds were treated as different traits. Across-breed genomic predicted transmitting ability (GPTA) and within-breed GPTA were compared using regressions to predict the deregressed validation data. Method 1 worked poorly, and coefficients of determination (R(2)) were much lower using training data from a different breed to estimate SNP effects. Correlations between direct genomic values computed using training data from different breeds were less than 30% and sometimes negative. Across-breed GPTA from method 2 had higher R(2) values than parent average alone but typically produced lower R(2) values than the within-breed GPTA. The across-breed R(2) exceeded the within-breed R(2) for a few traits in the Brown Swiss breed, probably because information from the other breeds compensated for the small numbers of Brown Swiss training animals. Correlations between within-breed GPTA and across-breed GPTA ranged from 0.91 to 0.93. The multibreed GPTA from method 3 were significantly better than the current within-breed GPTA, and adjusted R(2) for protein yield (the only trait tested for method 3) were highest of all methods for all breeds. However, method 3 increased the adjusted R(2) by only 0.01 for Holsteins, ≤0.01 for Jerseys, and 0.01 for Brown Swiss compared with within-breed predictions.
多品种模型目前被用于美国农业部（USDA）传统的奶牛产奶量和健康性状遗传评估中，但在基因组评估中使用的是品种内模型。多品种基因组模型是使用官方 2009 年 8 月 USDA 基因组评估中包含的 19686 头已基因分型的公牛和母牛开发和测试的。数据分为训练集和验证集。训练数据集包括已女儿证明和截至 2004 年 11 月有记录的母牛，总计 5331 头荷斯坦牛、1361 头泽西牛和 506 头瑞士褐牛。验证数据集有 2508 头荷斯坦牛、413 头泽西牛和 185 头瑞士褐牛公牛，这些公牛在 2004 年 11 月没有经过女儿信息验证（无女儿信息），但在 2009 年 8 月得到了验证。所有品种都使用了一套共同的 43385 个单核苷酸多态性（SNP）。研究了三种多品种评估方法。方法 1 在品种内分别估计 SNP 效应，然后将这些品种特异性 SNP 估计值应用于其他品种。方法 2 从所有品种的组合基因型和表型中估计一组共同的 SNP 效应。方法 3 通过使用多性状模型解决每个品种内的相关 SNP 效应，其中品种被视为不同的性状。使用回归来比较跨品种基因组预测传递能力（GPTA）和品种内 GPTA，以预测去回归验证数据。方法 1 效果不佳，使用来自不同品种的训练数据来估计 SNP 效应时，确定系数（R2）要低得多。使用不同品种的训练数据计算的直接基因组值之间的相关性小于 30%，有时甚至为负。与仅父母平均值相比，方法 2 的跨品种 GPTA 具有更高的 R2 值，但通常产生的 R2 值低于品种内 GPTA。在瑞士褐牛品种中，几个性状的跨品种 R2 超过了品种内 R2，这可能是因为来自其他品种的信息弥补了瑞士褐牛训练动物数量较少的不足。品种内 GPTA 和跨品种 GPTA 之间的相关性在 0.91 到 0.93 之间。方法 3 的多品种 GPTA 明显优于当前的品种内 GPTA，并且所有方法对所有品种的蛋白质产量（方法 3 测试的唯一性状）的调整 R2 最高。然而，与品种内预测相比，方法 3 仅使荷斯坦牛的调整 R2 增加了 0.01、泽西牛的调整 R2 增加了≤0.01、瑞士褐牛的调整 R2 增加了 0.01。
