Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle

• Published in: Journal of animal science and biotechnology Vol. 15; no. 1; pp. 83 - 13
• Main Authors: Mota, Lucio F. M, Giannuzzi, Diana, Pegolo, Sara, Toledo-Alvarado, Hugo, Schiavon, Stefano, Gallo, Luigi, Trevisi, Erminio, Arazi, Alon, Katz, Gil, Rosa, Guilherme J. M, Cecchinato, Alessio
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.06.2024; BMC
• Subjects: Animal lactation; Anticoagulants; Biomarkers; Blood; Blood metabolites; Cattle; Cholesterol; Creatinine; Dairy cattle; Damage; Data integration; Dietary minerals; Farms; Feature selection; Genetic markers; Genome-wide association studies; Glucose; Inflammation; Infrared spectra; Innate immunity; Liver; Machine learning; Metabolic disorders; Metabolites; Milk; Milk production; Minerals; Near infrared radiation; NIR; Nucleotides; Oxidation; Oxidative stress; Potassium; Principal components analysis; Proteins; Quality control; Sensors; Single-nucleotide polymorphism; Spectrum analysis; Urea; Italy
• ISSN: 1674-9782; 2049-1891
• DOI: 10.1186/s40104-024-01042-3

BackgroundVarious 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.ResultsResults 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.ConclusionOur 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.