Genetica i Millora Animal, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon s/n, Caldes de Montbui, Barcelona, 08140, Spain.
Genet Sel Evol. 2020 Sep 29;52(1):53. doi: 10.1186/s12711-020-00572-4.
Traits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG and BF reported in a Duroc pig line could be partially avoided. We estimated genetic and non-genetic correlations between BF, ADG and FCR on 1144 pigs using Bayesian methods considering the SAM; and responses to selection indexes that combine estimates of indirect (IGE) and direct (DGE) genetic effects for ADG and BF by stochastic simulation.
Estimates of the ratio of the variance of DGE to the phenotypic variance were 0.31, 0.39 and 0.25 and those of the total genetic variance to the phenotypic variance were 0.63, 0.74 and 0.93 for ADG, BF and FCR, respectively. In spite of this, when the SAM was used to generate data and for the genetic evaluations, the average economic response was worse than that obtained when BV predictions from the AM were considered. The achieved economic response was due to a direct reduction in BF and not to an improvement in FCR.
Our results show that although social genetic effects play an important role in the traits studied, their proper consideration in pig breeding programs to improve FCR indirectly is still difficult. The correlations between IGE and DGE that could help to overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes; also, the genetic parameters estimates from the SAM have large errors. These two factors penalize the average response under the SAM compared to the AM.
在群体中饲养的动物所记录的特征可以通过社会效应动物模型 (SAM) 进行分析。对于多个特征，该模型比动物模型 (AM) 更完整地指定遗传相关结构。我们的假设是，通过使用 SAM 对平均日增重 (ADG) 和背膘厚 (BF) 进行遗传评估，由于杜洛克猪系中报告的 ADG 和 BF 之间不利的遗传相关性可以部分避免，因此可能会实现饲料转化率 (FCR) 的高改进率。我们使用贝叶斯方法考虑 SAM 对 1144 头猪的 BF、ADG 和 FCR 之间的遗传和非遗传相关性进行了估计；并通过随机模拟对结合了 ADG 和 BF 的间接 (IGE) 和直接 (DGE) 遗传效应估计的选择指数的响应进行了估计。
DGE 方差与表型方差之比的估计值分别为 0.31、0.39 和 0.25，而 ADG、BF 和 FCR 的总遗传方差与表型方差之比的估计值分别为 0.63、0.74 和 0.93。尽管如此，当使用 SAM 生成数据和进行遗传评估时，平均经济响应不如使用 AM 的 BV 预测时获得的经济响应好。所实现的经济响应是由于 BF 的直接减少，而不是由于 FCR 的改善。
我们的结果表明，尽管社会遗传效应在研究的特征中起着重要作用，但在猪育种计划中适当考虑这些效应以间接提高 FCR 仍然具有挑战性。有助于克服 DGE 之间不利遗传相关性的 IGE 和 DGE 之间的相关性没有达到足够高的幅度；此外，SAM 中的遗传参数估计存在较大误差。这两个因素使得 SAM 下的平均响应相对于 AM 下的平均响应受到惩罚。
