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 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.
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