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Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions

机译:将基因组学与营养模型相集成以改善饲养场条件下牛的生长性能和Car体组成的预测

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摘要

Cattle body composition is difficult to model because several factors affect the composition of the average daily gain (>ADG) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism (>SNP) panels that could improve the predictability of days on feed (>DOF) to reach a target United States Department of Agriculture (>USDA) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value (>MBV) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat (>EBF) and the shrunk body weight (>SBW) at 28% EBF (>AFSBW) were computed by the Cattle Value Discovery System (>CVDS) model using hip height (EBFHH and AFSBWHH) or carcass traits (EBFCT and AFSBWCT) of the animals. The DOFHH was calculated when AFSBWHH and ADGHH were used and DOFCT was calculated when AFSBWCT and ADGCT were used. The CVDS estimates dry matter required (>DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBWCT was assumed more accurate than the AFSBWHH because it was computed using carcass traits. The difference between AFSBWCT and AFSBWHH, DOFCT and DOFHH, and DMR and dry matter intake (>DMI) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.
机译:牛的身体组成很难建模,因为有几个因素会影响生长中动物的平均日增重(> ADG )组成。这项研究的目的是确定商业单核苷酸多态性(> SNP )面板,这些面板可以提高饲料天数(> DOF )达到目标美国日粮的可预测性。农业(> USDA )等级,根据饲养场条件提供动物,饮食和环境信息。该研究的数据包括来自商业饲养场的杂交小母牛(n = 681)和and牛(n = 836)。评估了候选基因多态性和两个瘦素基因SNP(UASMS2和E2FB)的SNP专家组得出的11个分子育种值(> MBV )得分。空腹脂肪(> EBF )和收缩体重(> SBW )在EBF为28%(> AFSBW )时由牛值发现计算使用动物的臀部高度(EBFHH和AFSBWHH)或or体特征(EBFCT和AFSBWCT)的系统(> CVDS )模型。使用AFSBWHH和ADGHH时计算DOFHH,使用AFSBWCT和ADGCT时计算DOFCT。当提供观察到的ADG和AFSBW时,CVDS估计成组喂养的个体所需的干物质(> DMR )。假定AFSBWCT比AFSBWHH更准确,因为它是使用car体性状计算的。根据MBV评分和瘦素基因SNP对AFSBWCT和AFSBWHH,DOFCT和DOFHH之间的差异以及DMR和干物质摄入量(> DMI )进行了回归,以解释这种差异。我们的结果表明MBV得分与模型输入和输出变量之间存在很大的相关性,但MBV肋眼区域与DOF,AFSBW和DMI的差异之间的相关性最强,分别解释了8、13.2和6.5%。变化。这表明特定的MBV分数可能解释了营养模型在预测动物个体行为中使用的输入和输出变量的其他变化。

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    Luis O. Tedeschi;

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  • 年(卷),期 -1(10),11
  • 年度 -1
  • 页码 e0143483
  • 总页数 15
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