首页|Combining genetic markers,on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle
Combining genetic markers,on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
Combining genetic markers,on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle
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 con-sidered 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.Conclusion Our results highlighted the potential of combining several sources of information,such as genetic mark-ers,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.
Department of Agronomy,Food,Natural resources,Animals and Environment(DAFNAE),University of Padova,Legnaro,Padova 35020,Italy
Department of Genetics and Biostatistics,School of Veterinary Medicine and Zootechnics,National Autonomous University of Mexico,Ciudad Universitaria,Mexico City 04510,Mexico
Department of Animal Science,Food and Nutrition(DIANA)and the Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production(CREI),Faculty of Agricultural,Food,and Environmental Sci-ences,Università Cattolica del Sacro Cuore,Piacenza 29122,Italy
Afimilk LTD,Afikim 15148,Israel
Department of Animal and Dairy Sciences,University of Wisconsin,Madison,WI 53706,USA
国家科技期刊平台
NSTL
万方数据
