Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score
Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score
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DOI：
10.3168/jds.2009-2730
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发表时间：
2010-02-01
影响因子：
3.5
通讯作者：
Lawlor, T. J.
中科院分区：
文献类型：
--
作者：
Aguilar, I.;Misztal, I.;Lawlor, T. J.
第一个全国性的单步，全信息（表型，系谱，和标记基因型）遗传评估开发的最终得分美国荷斯坦牛。数据包括1955年至2009年记录的6，232人的最终得分。，548头荷斯坦奶牛。来自Cooperative Dairy DNA Repository（贝尔茨维尔，MD）的牛SNP 50（Illumina，San Diego，CA）基因型可用于6，508头公牛。三项分析使用了目前用于美国国家评价的可重复性动物模型。前2项分析使用了截至2004年记录的最终评分。第一个分析只使用了基于谱系的关系矩阵。第二种分析使用基于系谱和基因组信息的关系矩阵（单步方法）。第三次分析使用了完整的数据集，仅使用了基于谱系的关系矩阵。第四次分析使用来自第一次分析的预测（最终得分直到2004年并且仅基于谱系的关系矩阵）和使用基于基因组的矩阵的预测以获得遗传评估（多步法）。不同的等位基因频率进行了测试，在构建的基因组关系矩阵。从父母的平均值，一步，多步的方法和他们的2009年的女儿偏差的年轻公牛的预测之间的决定系数分别为0.24，0.37至0.41，和0.40，分别。当使用假设等位基因频率为0.5的基因组关系矩阵时，观察到单步方法的决定系数最高。2009年女儿偏离父母平均值、单步和多步预测的回归系数分别为0.76、0.68至0.79和0.86，这表明预测有一定的膨胀。一步回归系数可以增加到0.92，通过缩放基因组和基于家系的关系矩阵之间的差异，几乎没有损失的预测精度。一个完整的评估需要大约2小时的计算时间和2.7千兆字节的内存。单步分析的计算时间比基于谱系的分析稍长（2%）。一个国家的单步遗传评估与系谱关系矩阵与基因组信息增强提供了基因组预测的准确性和偏差相媲美的多步程序，并可以解释任何人口或数据结构。当动物根据基因型进行预选时，单步评估的优势将在未来增加。
The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232.,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a, repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2 h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.