PDF(Pubmed)
Abstract：
BACKGROUND: Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results.
RESULTS: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.
CONCLUSIONS: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.
RESULTS: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.
CONCLUSIONS: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.
摘要：
背景：已知各种血液代谢物是奶牛健康状况的有用指标，但是他们的常规评估很耗时，贵,对牛群水平的奶牛来说压力很大。因此，我们评估了将在线近红外（NIR）牛奶光谱与农场（牛奶中的天数[DIM]和胎次）和遗传标记相结合来预测荷斯坦牛血液代谢产物的有效性。数据来自具有AfiLab系统的农场的388头荷斯坦奶牛。近红外光谱，农场信息，和单核苷酸多态性（SNP）标记进行混合，以使用弹性网（ENet）方法开发血液代谢物的校准方程，考虑3个模型：(1)模型1(M1)，仅包括NIR信息，（2）同时具有NIR和农场信息的模型2（M2），和(3)结合NIR的模型3(M3)，农场和基因组信息。通过从全基因组关联研究(GWAS)结果中预选SNP标记来考虑M3的维度减少。
结果：结果表明，M2对能量相关代谢物的预测能力平均提高了19％（葡萄糖，胆固醇,NEFA,BHB,尿素,和肌酐），20%用于肝功能/肝损伤，7%用于炎症/先天免疫，24%为氧化应激代谢物，与M1相比，矿物质为23%。同时,M3进一步将能量相关代谢物的预测能力提高了34%，32%为肝功能/肝损害，22％的炎症/先天免疫，氧化应激代谢物的42.1%，矿物占41%，与M1相比。我们发现，使用来自GWAS结果的选择的SNP标记，对能量相关代谢物的阈值>2.0乘以5%，改善了M3的预测能力。9%为肝功能/肝损害，8%用于炎症/先天免疫，22%为氧化应激代谢物，9%为矿物质。观察到磷(2%)略有减少，三价铁还原抗氧化能力(1%)，和葡萄糖（3％）。此外，发现使用更严格的阈值(-log10(P值)>2.5和3.0)会影响预测精度，预测能力的增加较低。
结论：我们的结果强调了将几种信息来源结合在一起的潜力，比如遗传标记，农场信息，在线近红外红外数据提高了奶牛血液代谢物的预测能力，代表了在商业牛群中进行大规模在线健康监测的有效策略。
结果：结果表明，M2对能量相关代谢物的预测能力平均提高了19％（葡萄糖，胆固醇,NEFA,BHB,尿素,和肌酐），20%用于肝功能/肝损伤，7%用于炎症/先天免疫，24%为氧化应激代谢物，与M1相比，矿物质为23%。同时,M3进一步将能量相关代谢物的预测能力提高了34%，32%为肝功能/肝损害，22％的炎症/先天免疫，氧化应激代谢物的42.1%，矿物占41%，与M1相比。我们发现，使用来自GWAS结果的选择的SNP标记，对能量相关代谢物的阈值>2.0乘以5%，改善了M3的预测能力。9%为肝功能/肝损害，8%用于炎症/先天免疫，22%为氧化应激代谢物，9%为矿物质。观察到磷(2%)略有减少，三价铁还原抗氧化能力(1%)，和葡萄糖（3％）。此外，发现使用更严格的阈值(-log10(P值)>2.5和3.0)会影响预测精度，预测能力的增加较低。
结论：我们的结果强调了将几种信息来源结合在一起的潜力，比如遗传标记，农场信息，在线近红外红外数据提高了奶牛血液代谢物的预测能力，代表了在商业牛群中进行大规模在线健康监测的有效策略。
