使用乳牛随机回归模型估计料效率特征的遗传参数
K Houlahan1, F S Schenkel1, F Miglior2
1Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1.
Journal of dairy science
|September 10, 2023
概括
由于成本上升和环境问题,提高奶牛料效率至关重要. 这项研究分析了料效率的遗传参数,发现遗传性估计在哺乳期各个阶段有所不同,为繁殖计划策略提供了信息.
科学领域:
- 动物科学动物科学
- 遗传学 遗传学 是一个
- 乳制品生产 乳制品生产
背景情况:
- 料成本上升和环境问题需要提高奶牛的料效率.
- 对大型种群来说,准确测量料摄入量是具有挑战性的,阻碍其纳入育种计划.
- 了解整个哺乳期料效率的遗传参数是改善乳牛养殖的关键.
研究的目的:
- 在荷尔斯坦牛的第一个哺乳期内调查料效率的遗传参数和相关特征.
- 开发一种基因纠正的料效率测量方法 (基因残留料摄入量 - - gRFI).
- 提供对基因变异和料效率的潜在选择策略的洞察力.
主要方法:
- 利用了来自6个国家的7,440只初哺乳荷尔斯坦奶牛的大量数据集 (121,226只干物摄入量,120,500只能量校正的牛奶,98,975只代谢体重记录).
- 使用多特征随机回归模型与第四阶莱根德多项式来估计遗传参数.
- 通过重新参数化干物摄入量 (DMI) 来获得基因组剩余料摄入量 (gRFI),以对能量校正牛奶 (ECM) 和代谢体重 (MBW) 进行基因校正.
主要成果:
- 对DMI的遗传性估计范围从0.15到0.29,ECM从0.24到0.29,MBW从0.55到0.83,和gRFI从0.12到0.22.
- 在早期哺乳期,与晚期相比,遗传概率估计一般较低.
- 每周哺乳期特征之间的附加遗传相关性各不相同,显示相邻周之间更强的关联.
结论:
- 料效率特征的遗传参数在整个第一个哺乳期都会发生变化.
- 开发的gRFI为乳牛料效率的遗传选择提供了有价值的工具.
- 研究结果支持将料效率纳入乳制品育种计划,以提高可持续性和生产力.
相关概念视频
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis
64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
79
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
79
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K


