Departamento de Producción Animal, E.T.S.I. Agrónomos-Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Animal. 2012 Aug;6(8):1216-24. doi: 10.1017/S1751731112000341.
This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h 2 = 0.10) and medium heritability (h 2 = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire' strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.
本研究评估了不同的雌性选择基因分型策略，以提高基因组育种值（GBV）在具有少量种公牛和大量后代的群体中的预测准确性。利用模拟的奶牛群体来解决研究的目标。使用了以下选择策略：随机选择、基于产量偏差的两尾选择、基于育种值的两尾选择、最高产量偏差选择和最高育种值选择。为了比较，分析中还包括另外两种策略，即种公牛的基因分型和传统遗传评估的系谱指数。模拟了两种情况，低遗传力（h 2 = 0.10）和中遗传力（h 2 = 0.30）。使用贝叶斯套索估计 GBV。两尾策略预测 GBV 的准确性优于其他策略（在低遗传力和中遗传力情况下，产量偏差两尾选择策略的准确性分别为 0.50 和 0.63，育种值两尾选择策略的准确性分别为 0.48 和 0.63，使用 1000 头基因分型奶牛）。当使用 996 头基因分型公牛作为训练群体时，种公牛策略导致低遗传力和中遗传力性状的准确性分别为 0.48 和 0.55。随机策略需要更大的训练群体才能优于系谱指数的准确性；然而，从群体的产量偏差或育种值的顶端选择雌性并不能提高相对于系谱指数的准确性。考虑到所有基因分型策略都存在偏差，尽管最高策略产生了最有偏差的预测。在种公牛数量有限且有可靠后代测试的育种计划中，可以实施涉及基因分型奶牛的策略。本研究的结果表明，在产量偏差分布中表现出上下极值的雌性可以被纳入训练群体，以提高小参考群体的可靠性。仅选择具有高估计育种值或产量偏差的雌性的策略产生了次优的结果。
