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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u00a0&gt; 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 (\u2212log\n                      <jats:sub>10<\/jats:sub>\n                      (\n                      <jats:italic>P<\/jats:italic>\n                      -value) &gt; 2.5 and 3.0), with a lower increase in the predictive ability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40104-024-01042-3","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T20:02:03Z","timestamp":1717876923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle"],"prefix":"10.1186","volume":"15","author":[{"given":"Lucio F. 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