Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens.
Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens.
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DOI：
10.1186/s12863-014-0110-y
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发表时间：
2014-10-15
期刊：
影响因子：
2.9
通讯作者：
Su G
中科院分区：
文献类型：
--
作者：
Liu T;Qu H;Luo C;Shu D;Wang J;Lund MS;Su G
生长和胴体性状是肉鸡的重要性状。然而，胴体性状只能在死后测量。基因组选择可以在没有自身表型信息的情况下准确预测动物的育种值，因此可能成为这类性状的有力工具。本研究以中国三黄鸡为研究对象，探讨了基因组预测的有效性。中国三黄鸡是通过杂交选育而成的新品系，有效群体小。分析了两个生长性状和三个胴体性状：6周龄体重、12周龄体重、净膛率、胸肌率和腿肌率。基因组预测进行了评估，使用4倍交叉验证程序的两个验证方案。在第一种情况下，每个测试数据集包括两个半同胞家庭（家庭样本），其余的代表参考数据。在第二种情况下，整个数据被随机分为四个子集（随机样本）。在每次验证中，一个子集用作测试数据，其他子集用作每次验证中的参考数据。使用基因组最佳线性无偏预测模型、贝叶斯最小绝对收缩和选择算子模型和具有四个分布的贝叶斯混合模型预测基因组育种值。基因组估计育种值（GEBV）的准确性是衡量GEBV与校正表型值之间的相关性。在家系抽样方案中，生长性状的相关系数在0.448 ~ 0.468之间，胴体性状的相关系数在0.176 ~ 0.255之间;在随机抽样方案中，生长性状的相关系数在0.487 ~ 0.536之间，胴体性状的相关系数在0.312 ~ 0.430之间。三种模型之间的预测精度差异非常小;贝叶斯混合模型稍微更准确。根据随机抽样方案的结果，GEBV的准确性比传统的系谱指数高0.197，平均在5个性状。结果表明，与常规选择相比，基因组选择可大大提高鸡的选择准确率。肉鸡生长和胴体性状的基因组选择具有广阔的应用前景。本文的在线版本（doi：10.1186/s12863-014-0110-y）包含补充材料，可供授权用户使用。
Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage. Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits. The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising. The online version of this article (doi:10.1186/s12863-014-0110-y) contains supplementary material, which is available to authorized users.
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