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

作　　者：Lucio F.M.Mota Diana Giannuzzi Sara Pegolo Hugo Toledo‑Alvarado Stefano Schiavon Luigi Gallo Erminio Trevisi Alon Arazi Gil Katz Guilherme J.M.Rosa Alessio Cecchinato 

机构地区：[1]Department of Agronomy,Food,Natural resources,Animals and Environment(DAFNAE),University of Padova,Legnaro,Padova 35020,Italy [2]Department of Genetics and Biostatistics,School of Veterinary Medicine and Zootechnics,National Autonomous University of Mexico,Ciudad Universitaria,Mexico City 04510,Mexico [3]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 Sciences,Universita Cattolica del Sacro Cuore,Piacenza 29122,Italy [4]Afimilk LTD,Afikim 15148,Israel [5]Department of Animal and Dairy Sciences,University of Wisconsin,Madison,WI 53706,USA

出　　处：《Journal of Animal Science and Biotechnology》2024年第6期2229-2241,共13页畜牧与生物技术杂志(英文版)

基　　金：funding provided by Universitàdegli Studi di Padova;part of the project PROH-DAIRY project(Development of precision livestock breeding tools toward One Health in Italian and Israeli dairy chains)funded by the Ministry of Foreign Affairs and International Cooperation(MAECI)within the Italy-Israel R&D Cooperation Program(Roma,Italy);the Agritech National Research Center and received funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)-MISSIONE 4 COM-PONENTE 2,INVESTIMENTO 1.4-D.D.103217/06/2022,CN00000022)。

摘　　要：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 mod els:(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,B H B,urea,and c reatinin e),20%for liver functio n/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 mineralse compared to M1.We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of>2.0by 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 redu ctions were observed fo r phosphorus(2%),ferricreducing antioxidant power(1%),and glucose(3%).Furthermore,it was found that prediction accuracies are influenced by using more restrictive thresholds(-log_(10)^(P-value)>2.5 and 3.0),with a lower

关 键 词：Blood metabolites Dairy cattle Data integration Feature selection Metabolic disorders NIR Precision livestock farming 

分 类 号：S823[农业科学—畜牧学]