Department of Animal and Dairy Science, University of Georgia, Athens 30602-2771, USA.
J Anim Sci. 2011 Jan;89(1):23-8. doi: 10.2527/jas.2010-3071. Epub 2010 Oct 1.
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.
使用两个纯系三代的数据进行基因组评估。完整的群体（完整数据集；FDS）由两条线的 183784 只和 164246 只肉鸡组成。基于不同指数应用于每个系的 20 多个性状，对基因分型子集（SUB）进行了预选择。3 个性状进行了分析：6 周龄体重（BW6）、胸部肌肉超声测量（BM）和腿部评分（LS），编码为 1 = 无缺陷，2 = 有缺陷。一些表型缺失 BM。训练群体包括前两代，包括 FDS 中的所有动物或 SUB 中的所有基因分型动物。验证数据集仅包含第三代中的基因分型动物。遗传评估使用 3 种方法进行：1）表型 BLUP，2）扩展 BLUP 方法，以便在单个步骤中利用系谱和基因组信息（ssGBLUP），3）贝叶斯 A。虽然 BLUP 和 ssGBLUP 利用了所有表型数据，但贝叶斯 A 只能使用基因分型子集中的数据。BW6 的遗传力为 0.17 至 0.20，BM 的遗传力为 0.30 至 0.35，LS 的遗传力为 0.09 至 0.11。BLUP 用于 BW6、BM 和 LS 的验证群体的平均准确性为 SUB 时为 0.46、0.30 和 <0，FDS 时为 0.51、0.34 和 0.28。对于 ssGBLUP，SUB 的准确性分别为 0.60、0.34 和 0.06，FDS 的准确性分别为 0.61、0.40 和 0.37。对于贝叶斯 A，SUB 的准确性为 0.60、0.36 和 0.09。对于遗传力较高的性状，贝叶斯 A/SUB 和 ssGBLUP/FDS 的准确性相似，比 BLUP/FDS 高多达 50%。然而，对于遗传力较低的性状，ssGBLUP/FDS 的准确性比贝叶斯 A/SUB 高 4 到 6 倍，比 BLUP/FDS 高 50%。最佳的基因组评估将是多性状的，涉及所有基于选择的性状和记录。
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