通过调解分析与结构方程建模,研究宿主基因组,肠微生物组和奶牛料效率之间的关系
Guillermo Martinez-Boggio1, Hugo F Monteiro2, Fabio S Lima2
1Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
Journal of dairy science
|June 22, 2024
概括
了解乳头微生物群是乳牛料效率的关键. 这项研究揭示了宿主遗传和肠微生物如何相互作用以影响料效率,为育种和管理提供了改善乳制品农场可持续性的目标.
科学领域:
- 动物遗传学和动物基因组学
- 微生物学 微生物学
- 动物营养和新陈代谢
背景情况:
- 乳头微生物组对于乳牛的营养吸收和牛奶生产至关重要.
- 牛皮的微生物成分会影响料效率特征,如干物质摄入量和牛奶能量.
- 了解宿主基因组-微生物组相互作用对于提高乳制品行业利能力和可持续性至关重要.
研究的目的:
- 为了研究宿主基因组,肠微生物组和奶牛料效率之间的关系.
- 推断 rumen microbiome 在宿主基因中的调解作用对料效率产生影响.
- 为了估计遗传性和计算料效率特征的育种值,考虑微生物的影响.
主要方法:
- 利用结构方程模型来分析宿主基因型,乳头微生物丰度 (16S rRNA) 和料效率记录 (干物摄入量,牛奶能量,剩余料摄入量).
- 该模型包含了对表型的直接遗传影响 (GP → P),对表型的直接微生物影响 (M → P) 和通过微生物群的间接遗传影响 (GM → P).
- 分析了来自两个研究农场的448只中期哺乳期荷尔斯坦牛的数据.
主要成果:
- 鉴定出特定的肠微生物 (7%-30%),对料效率特征有显著的因果影响.
- 根据遗传性和因果作用将微生物分为三组,表明有针对性的干预或选择性育种的潜力.
- 将微生物数据纳入基因组模型通常会降低特征遗传性 (-15%至+5%),突出显示复杂的相互作用.
结论:
- 毛皮微生物组部分介导宿主基因对料效率的影响.
- 通过选择性育种或管理实践来提高料效率,可以准特定的小肠微生物进行操纵.
- 这些发现为通过优化宿主微生物群相互作用来提高奶牛生产率和可持续性提供了一个框架.
相关概念视频
Multiple Regression
3.3K
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...
3.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis
359
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...
359
Methods of Medium Optimization
70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70
Microbiota of the Large Intestine
96
The large intestine hosts the most densely populated microbial ecosystem in the human body. This complex community primarily consists of anaerobic bacteria, with Bacillota (formerly Firmicutes) and Bacteroidota (formerly Bacteroidetes) as the predominant groups. The distribution of these microbes varies along different sections of the large intestine, influenced by local environmental factors such as oxygen availability and nutrient composition.The cecum, located at the beginning of the large...
96


