{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T08:55:14Z","timestamp":1794387314727,"version":"4.9.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100022592","name":"Green Development and Demonstration Programme","doi-asserted-by":"crossref","award":["34009-19-1586"],"award-info":[{"award-number":["34009-19-1586"]}],"id":[{"id":"10.13039\/501100022592","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genet Sel Evol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Metabolomic profiling of blood samples can be done on selection candidates and could be a valuable information source for genetic evaluation of pigs. We hypothesized that integrating metabolomic data from pigs without individual phenotypes into the metabolomic-genomic best linear unbiased prediction (MGBLUP) model would generate estimated breeding values (EBVs) with a higher accuracy compared to what would be obtained without metabolomic data. We tested this hypothesis by predicting breeding values for average daily gain (ADG) using phenotypic, genomic, and metabolomic data. MGBLUP models were fitted to average daily gain of 8174 Duroc pigs that were genotyped and profiled for metabolomic features. Approximately half the pigs were males from a test station and the other half were females from breeding herds. Variance components were estimated, and we employed two validation schemes: test station to breeding herd validation and fivefold cross-validation. Accuracies of EBVs in the validation population were computed by combining results on predictive abilities with results on increases in accuracies from the linear regression method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Parameter estimates from MGBLUP showed a direct heritability of ADG of 0.15, a proportion of variance explained by metabolomic features of 0.18, and a heritability of metabolomic intensities of 0.14, together resulting in a total heritability of 0.17. Thus, the majority of the heritability was not mediated by the metabolome. For the test station to breeding herd validation, the accuracies of EBVs were 0.60 for genomic best linear unbiased prediction (GBLUP) with genotypes in validation population, 0.61 for MGBLUP with genotypes in validation population, 0.62 for MGBLUP with genotypes and metabolomic features in validation population, 0.72 for GBLUP with genotypes and phenotypes in validation population, and 0.74 for MGBLUP with genotypes, phenotypes and metabolomic features in validation population, whereas the corresponding numbers were 0.87, 0.87, 0.87, 0.91 and 0.92 for the fivefold cross-validation. Therefore, small increases in accuracies were observed when including metabolomic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The inclusion of metabolomics data provided small improvements in the accuracy of genetic evaluations for average daily gain in pigs. Further work will be needed to investigate, e.g., alternative time points for blood sampling, metabolomics on samples of other tissues, and other traits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12711-025-00972-4","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T14:31:53Z","timestamp":1747837913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Metabolomic-genomic prediction realizes small increases in accuracy of estimated breeding values for daily gain in pigs"],"prefix":"10.1186","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7354-1519","authenticated-orcid":false,"given":"Xiangyu","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pernille","family":"Sarup","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anders","family":"Bay Nord","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Henryon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tage","family":"Ostersen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ole F.","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"972_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.774113","volume":"13","author":"D Chakraborty","year":"2022","unstructured":"Chakraborty D, Sharma N, Kour S, Sodhi SS, Gupta MK, Lee SJ, et al. Applications of omics technology for livestock selection and improvement. Front Genet. 2022;13: 774113.","journal-title":"Front Genet"},{"key":"972_CR2","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1023\/A:1013713905833","volume":"48","author":"O Fiehn","year":"2002","unstructured":"Fiehn O. Metabolomics\u2014the link between genotypes and phenotypes. Plant Mol Biol. 2002;48:155\u201371.","journal-title":"Plant Mol Biol"},{"key":"972_CR3","doi-asserted-by":"publisher","first-page":"4729","DOI":"10.2527\/jas.2012-5338","volume":"90","author":"F Rohart","year":"2012","unstructured":"Rohart F, Paris A, Laurent B, Canlet C, Molina J, Mercat MJ, et al. Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds. J Anim Sci. 2012;90:4729\u201340.","journal-title":"J Anim Sci"},{"key":"972_CR4","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.cmet.2019.06.002","volume":"30","author":"C Jang","year":"2019","unstructured":"Jang C, Hui S, Zeng X, Cowan AJ, Wang L, Chen L, et al. Metabolite exchange between mammalian organs quantified in pigs. Cell Metab. 2019;30:594-606.e3.","journal-title":"Cell Metab"},{"key":"972_CR5","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1038\/s41598-019-57182-4","volume":"10","author":"VAO Carmelo","year":"2020","unstructured":"Carmelo VAO, Banerjee P, da Silva Diniz WJ, Kadarmideen HN. Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs. Sci Rep. 2020;10:255.","journal-title":"Sci Rep"},{"key":"972_CR6","unstructured":"Sarup PM, Jensen J, Ostersen T, Christensen OF, S\u00f8rensen P. H1 NMR metabolomic phenotypic prediction of growth in pigs. In: Proceedings of the 69th Annual Meeting of the European Federation of Animal Science; 27\u201331 August 2018; Dubrovnik. 2018."},{"key":"972_CR7","doi-asserted-by":"publisher","first-page":"iyab130","DOI":"10.1093\/genetics\/iyab130","volume":"219","author":"OF Christensen","year":"2021","unstructured":"Christensen OF, B\u00f6rner V, Varona L, Legarra A. Genetic evaluation including intermediate omics features. Genetics. 2021;219:iyab130.","journal-title":"Genetics"},{"key":"972_CR8","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3168\/jds.2008-1646","volume":"92","author":"BJ Hayes","year":"2009","unstructured":"Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009;92:433\u201343.","journal-title":"J Dairy Sci"},{"key":"972_CR9","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1017\/S1751731112002248","volume":"7","author":"A Bouquet","year":"2013","unstructured":"Bouquet A, Juga J. Integrating genomic selection into dairy cattle breeding programmes: a review. Animal. 2013;7:705\u201313.","journal-title":"Animal"},{"key":"972_CR10","doi-asserted-by":"publisher","first-page":"4414","DOI":"10.3168\/jds.2007-0980","volume":"91","author":"PM VanRaden","year":"2008","unstructured":"VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414\u201323.","journal-title":"J Dairy Sci"},{"key":"972_CR11","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s12711-023-00835-w","volume":"55","author":"X Guo","year":"2023","unstructured":"Guo X, Sarup P, Jahoor A, Jensen J, Christensen OF. Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley. Genet Sel Evol. 2023;55:61.","journal-title":"Genet Sel Evol"},{"key":"972_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s12711-018-0426-6","volume":"50","author":"A Legarra","year":"2018","unstructured":"Legarra A, Reverter A. Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method. Genet Sel Evol. 2018;50:53.","journal-title":"Genet Sel Evol"},{"key":"972_CR13","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/1471-2164-15-478","volume":"15","author":"M Sargolzaei","year":"2014","unstructured":"Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics. 2014;15:478.","journal-title":"BMC Genomics"},{"key":"972_CR14","doi-asserted-by":"publisher","first-page":"9887","DOI":"10.1021\/ac5025039","volume":"86","author":"AC Dona","year":"2014","unstructured":"Dona AC, Jim\u00e9nez B, Sch\u00e4fer H, Humpfer E, Spraul M, Lewis MR, et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem. 2014;86:9887\u201394.","journal-title":"Anal Chem"},{"key":"972_CR15","unstructured":"Haggart GA, Pearce J, Sands C. csmsoftware\/IMPaCTS: version 1.1.1. Zenodo; 2019."},{"key":"972_CR16","doi-asserted-by":"publisher","first-page":"4281","DOI":"10.1021\/ac051632c","volume":"78","author":"F Dieterle","year":"2006","unstructured":"Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006;78:4281\u201390.","journal-title":"Anal Chem"},{"key":"972_CR17","doi-asserted-by":"publisher","first-page":"259","DOI":"10.3390\/metabo3020259","volume":"3","author":"TN Vu","year":"2013","unstructured":"Vu TN, Laukens K. Getting your peaks in line: a review of alignment methods for NMR spectral data. Metabolites. 2013;3:259\u201376.","journal-title":"Metabolites"},{"key":"972_CR18","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jmr.2009.11.012","volume":"202","author":"F Savorani","year":"2010","unstructured":"Savorani F, Tomasi G, Engelsen SB. icoshift: A versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010;202:190\u2013202.","journal-title":"J Magn Reson"},{"key":"972_CR19","doi-asserted-by":"publisher","first-page":"3832","DOI":"10.1093\/jas\/skz228","volume":"97","author":"A Aliakbari","year":"2019","unstructured":"Aliakbari A, Ehsani A, Vaez Torshizi R, L\u00f8vendahl P, Esfandyari H, Jensen J, et al. Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle. J Anim Sci. 2019;97:3832\u201344.","journal-title":"J Anim Sci"},{"key":"972_CR20","unstructured":"Madsen P, Jensen J, Labouriau R, Christensen OF, Sahana G. DMU\u2014a package for analyzing multivariate mixed models in quantitative genetics and genomics. In: Proceedings of the 10th World Congress on Genetics Applied to Livestock Production; 17\u201322 August 2014. Vancouver. 2014."},{"key":"972_CR21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.tpb.2015.08.005","volume":"107","author":"A Legarra","year":"2016","unstructured":"Legarra A. Comparing estimates of genetic variance across different relationship models. Theor Popul Biol. 2016;107:26\u201330.","journal-title":"Theor Popul Biol"},{"key":"972_CR22","unstructured":"Revelle W. Psych: procedures for psychological, psychometric, and personality research. R package version 1.0\u201395. 2013. https:\/\/cran.r-project.org\/web\/packages\/psych\/psych.pdf. Accessed 07 May 2025."},{"key":"972_CR23","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1080\/01621459.1971.10482369","volume":"66","author":"OJ Dunn","year":"1971","unstructured":"Dunn OJ, Clark V. Comparison of tests of the equality of dependent correlation coefficients. J Am Stat Assoc. 1971;66:904\u20138.","journal-title":"J Am Stat Assoc"},{"key":"972_CR24","doi-asserted-by":"publisher","first-page":"830","DOI":"10.3390\/life11080830","volume":"11","author":"KG Santiago","year":"2021","unstructured":"Santiago KG, Lopez BI, Kim SH, Lee DH, Cho YG, Song YN, et al. Genetic parameters for different measures of feed efficiency and their relationship to production traits in three purebred pigs. Life. 2021;11:830.","journal-title":"Life"},{"key":"972_CR25","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.3390\/ani12151902","volume":"12","author":"W Li","year":"2022","unstructured":"Li W, Wang Z, Luo S, Wu J, Zhou L, Liu J. Genome-wide association analysis and genetic parameters for feed efficiency and related traits in Yorkshire and Duroc pigs. Animals. 2022;12:1902.","journal-title":"Animals"},{"key":"972_CR26","doi-asserted-by":"publisher","first-page":"4069","DOI":"10.2527\/jas.2012-6197","volume":"91","author":"DN Do","year":"2013","unstructured":"Do DN, Strathe AB, Jensen J, Mark T, Kadarmideen HN. Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds. J Anim Sci. 2013;91:4069\u201379.","journal-title":"J Anim Sci"},{"key":"972_CR27","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1186\/1471-2105-12-186","volume":"12","author":"D Habier","year":"2011","unstructured":"Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186.","journal-title":"BMC Bioinformatics"}],"container-title":["Genetics Selection Evolution"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12711-025-00972-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12711-025-00972-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12711-025-00972-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:30:01Z","timestamp":1792611001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12711-025-00972-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,21]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["972"],"URL":"https:\/\/doi.org\/10.1186\/s12711-025-00972-4","relation":{},"ISSN":["1297-9686"],"issn-type":[{"value":"1297-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,21]]},"assertion":[{"value":"18 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Studies involving animals must include a statement on ethics approval and for experimental studies involving client-owned animals, authors must also include a statement on informed consent from the client or owner.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. PS is employed by Nordic Seed A\/S. XG, MAH, TO and OFC are employed by Danish Agriculture & Food Council. The funders had no influence on the study design or choice of analysis methods.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}