ICAR-NDRI, Karnal, Haryana, India.
ICAR-NBAGR, Karnal, Haryana, India.
J Anim Breed Genet. 2023 Jul;140(4):400-412. doi: 10.1111/jbg.12767. Epub 2023 Mar 7.
In the present study, random regression models (RRM) were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Legendre polynomial function (LP), with the objective to find the best combination of "minimum test-day model," which would be essential and sufficient to evaluate the trait successfully. Data included for analysis were 10,615 first lactation monthly test-day milk yield records (5th, 35th, 65th, …, 305th) from 965 Murrah buffaloes for the period 1975-2018. Cubic to octic-order orthogonal polynomials with homogeneous residual variances were used for the estimation of genetic parameters. Random regression models with sixth-order were selected based on goodness of fit criteria like lower AIC, BIC and residual variance. Heritability estimates ranged from 0.079 (TD6) to 0.21(TD10). For both ends of lactation, the additive genetic and environmental variances were higher and ranged from 0.21 ± 0.12 (TD6) to 0.85 ± 0.35 kg (TD1) and 3.74 ± 0.36 (TD11) to 1.36 ± 0.14 kg (TD9), respectively. Between adjacent test-day records, genetic correlation estimates ranged from 0.09 ± 0.31 (TD1 and TD2) to 0.97 ± 0.03 (TD3 and TD4; TD4 and TD5), but values gradually declined as the distance between test days increased. Negative genetic correlations were also obtained between TD1 with TD3 to TD9, TD2 with TD9 and TD10, and TD3 with TD10. On the basis of genetic correlations, models with 5 and/or 6 test-days combination were able to account for 86.1%-98.7% of variation along the lactation. Models with fourth and fifth-order LP functions were considered to account for variance with combinations of 5 and/or 6 test-day milk yields. The model with 6 test-day combinations had a higher rank correlation (0.93) with model using 11 monthly test-day milk yield records. On the basis of relative efficiency, the model with 6 monthly test day combinations with fifth-order was more efficient (maximum 99%) than the model using 11 monthly test-day milk yield records. Looking into the similar accuracy with the 11TD model, and the low resources requirement, we recommend the use of the "6 test-day combination model" for sire evaluation. These models may help in reducing the cost and time for data recording of milk yield.
在本研究中，使用勒让德多项式函数（LP），利用随机回归模型（RRM）估计了摩拉水牛的产奶量的遗传参数，目的是找到“最小测试日模型”的最佳组合，该模型对于成功评估性状是必要且充分的。分析中包括 1975-2018 年间 965 头摩拉水牛的 10615 个首次泌乳期每月测试日产奶量记录（第 5、35、65、……、第 305 个）。采用同质性残差方差的三次到八次立方正交多项式来估计遗传参数。基于 AIC、BIC 和残差方差等拟合优度标准，选择了六阶随机回归模型。遗传力估计值范围从 0.079（TD6）到 0.21（TD10）。在泌乳期的两端，加性遗传方差和环境方差均较高，范围分别为 0.21±0.12（TD6）至 0.85±0.35kg（TD1）和 3.74±0.36（TD11）至 1.36±0.14kg（TD9）。相邻测试日记录之间的遗传相关性估计值范围从 0.09±0.31（TD1 和 TD2）到 0.97±0.03（TD3 和 TD4；TD4 和 TD5），但随着测试日之间距离的增加，相关性值逐渐下降。TD1 与 TD3 至 TD9、TD2 与 TD9 和 TD10 以及 TD3 与 TD10 之间也存在负遗传相关性。基于遗传相关性，5 天和/或 6 天的测试日组合模型能够解释 86.1%-98.7%的泌乳期变异。四阶和五阶 LP 函数模型被认为可以解释 5 天和/或 6 天产奶量测试日的组合方差。第六阶组合模型与使用 11 个每月产奶量测试记录的模型具有更高的等级相关系数（0.93）。基于相对效率，第六阶组合模型（最高 99%）比使用 11 个每月产奶量测试记录的模型更有效。考虑到与 11TD 模型的相似精度和较低的资源要求，我们建议使用“6 天测试日组合模型”进行种公牛评估。这些模型可能有助于降低产奶量数据记录的成本和时间。
