Abstract
Increasing agricultural production while reducing the reliance on synthetic inputs such as antibiotics is an important challenge. For cattle breeding, this implies better understanding the genetics underlying meat production and the immune response. Here, we use systems immunology to investigate the genetic and environmental drivers of immune variation in Belgian White Blue male cattle, a breed historically bred for meat production. While seasonality and other non-genetic factors account for much of the immune variation observed, genome-wide association studies identify loci with major effects on specific immunophenotypes. Genetics also emerges as the primary driver of cytokine production. Finally, we develop a predictive model linking genetic data to cytokine responses. Our findings support the selection of cattle with improved immunity and advance our understanding of mammalian immune variation.
Similar content being viewed by others
Data availability
The RNA-seq data generated in this study have been deposited in the ENA under accession code PRJEB94322. The genotype and GWAS summary statics are deposited in zenodo (https://doi.org/10.5281/zenodo.15967035) under restricted access for consent as data are partially owned by the BWB herdbook association. The access to genotype data, full GWAS summary statistics, and raw phenotype data can be requested to Prof. Laurent Gillet (l.gillet@uliege.be). The time-frame for response to requests is within one month. Data on clinical phenotypes from Holstein cattle are publicly available (https://doi.org/10.1186/s12864-020-6461-z). Source data are provided with this paper.
Code availability
Codes used for the analyses in the study are available at GitHub (https://github.com/frucelee/System-immunology_BWB) and Zenodo (https://doi.org/10.5281/zenodo.17514186)98.
References
De Jager, P. L. et al. ImmVar project: Insights and design considerations for future studies of ‘healthy’ immune variation. Semin. Immunol. 27, 51–57 (2015).
Kumar, V., Wijmenga, C. & Xavier, R. J. Genetics of immune-mediated disorders: From genome-wide association to molecular mechanism. Curr. Opin. Immunol. 31, 51–57 (2014).
Liston, A., Carr, E. J. & Linterman, M. A. Shaping Variation in the Human Immune System. Trends Immunol 37, 637–646 (2016).
Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345 (2014).
Duffy, D. Understanding immune variation for improved translational medicine. Curr. Opin. Immunol. 65, 83–88 (2020).
Sanz, J., Randolph, H. E. & Barreiro, L. B. Genetic and evolutionary determinants of human population variation in immune responses. Curr. Opin. Genet. Dev. 53, 28–35 (2018).
Patin, E. et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat. Immunol. 19, 302–314 (2018).
Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).
Notarangelo, L. D., Bacchetta, R., Casanova, J. L. & Su, H. C. Human inborn errors of immunity: an expanding universe. Sci. Immunol. 5 (2020).
Aguirre-Gamboa, R. et al. Differential effects of environmental and genetic factors on T and B cell immune traits. Cell Rep 17, 2474–2487 (2016).
Davis, M. M. A prescription for human immunology. Immunity 29, 835–838 (2008).
Davis, M. M., Tato, C. M. & Furman, D. Systems immunology: Just getting started. Nat. Immunol. 18, 725–732 (2017).
Davis, M. M. & Brodin, P. Rebooting human immunology. Annu. Rev. Immunol. 36, 843–864 (2018).
Hagan, T., Nakaya, H. I., Subramaniam, S. & Pulendran, B. Systems vaccinology: enabling rational vaccine design with systems biological approaches. Vaccine 33, 5294–5301 (2015).
Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110.e14 (2016).
Ter Horst, R. et al. Host and environmental factors influencing individual human cytokine responses. Cell 167, 1111–1124.e13 (2016).
Brodin, P. New approaches to the study of immune responses in humans. Hum. Genet. 139, 795–799 (2020).
Eckhardt, M., Hultquist, J. F., Kaake, R. M., Hüttenhain, R. & Krogan, N. J. A systems approach to infectious disease. Nat. Rev. Genet. 21, 339–354 (2020).
Pulendran, B. & Davis, M. M. The science and medicine of human immunology. Science 369 (2020).
Wang, L. et al. An Atlas of genetic variation linking pathogen-induced cellular traits to human disease. Cell Host Microbe 24, 308–323.e6 (2018).
Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).
Lagou, V. et al. Genetic architecture of adaptive immune system identifies key immune regulators. Cell Rep 25, 798–810.e6 (2018).
Li, Y. et al. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat. Med. 22, 952–960 (2016).
Marderstein, A. R. et al. Demographic and genetic factors influence the abundance of infiltrating immune cells in human tissues. Nat. Commun. 11, 1–14 (2020).
Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).
Oosting, M. et al. Functional and genomic architecture of Borrelia burgdorferi-induced cytokine responses in humans. Cell Host Microbe 20, 822–833 (2016).
Roederer, M. et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).
Flori, L. et al. Immunity traits in pigs: Substantial genetic variation and limited covariation. PLoS ONE 6 (2011).
Ballester, M. et al. Genetic parameters and associated genomic regions for global immunocompetence and other health-related traits in pigs. Sci. Rep. 10, 1–15 (2020).
Braun, R. O. et al. System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep. npj Vaccines 3 (2018).
Maroilley, T. et al. Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis. BMC Genomics 18, 1–19 (2017).
Emam, M. et al. The effect of host genetics on in vitro performance of bovine monocyte-derived macrophages. J. Dairy Sci. 102, 9107–9116 (2019).
Thompson-Crispi, K. A., Sewalem, A., Miglior, F. & Mallard, B. A. Genetic parameters of adaptive immune response traits in Canadian Holsteins. J. Dairy Sci. 95, 401–409 (2012).
Mach, N. et al. The peripheral blood transcriptome reflects variations in immunity traits in swine: Towards the identification of biomarkers. BMC Genomics 14 (2013).
Mallard, B. A., Wilkie, B. N., Kennedy, B. W. & Quinton, M. Use of estimated breeding values in a selection index to breed Yorkshire Pigs for high and low immune and innate resistance factors. Anim. Biotechnol. 3, 257–280 (1992).
Martin, P. et al. Identification of genome regions and promising candidate genes linked to innate immune capacity on young Holstein calves. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) (ed. Veerkamp, R. F. and de Haas, Y.) 426–429 (Wageningen Academic, 2022).
Espinosa, R., Tago, D. & Treich, N. Infectious diseases and meat production. Environ. Resour. Econ 76, 1019–1044 (2020).
Ikhimiukor, O. O., Odih, E. E., Donado-Godoy, P. & Okeke, I. N. A bottom-up view of antimicrobial resistance transmission in developing countries. Nat. Microbiol. 7, 757–765 (2022).
Georges, M., Charlier, C. & Hayes, B. Harnessing genomic information for livestock improvement. Nat. Rev. Genet. 20, 135–156 (2019).
Wells, S. B. et al. Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age. bioRxiv https://doi.org/10.1101/2024.01.03.573877 (2024).
ter Horst, R. et al. Seasonal and nonseasonal longitudinal variation of immune function. J. Immunol. 207, 696–708 (2021).
Kapellos, T. S. et al. Human monocyte subsets and phenotypes in major chronic inflammatory diseases. Front. Immunol. 10, 1–13 (2019).
Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat Immunol 17, 461–468 (2016).
Hill, D. L. et al. Immune system development varies according to age, location, and anemia in African children. Sci. Transl. Med. 12 (2020).
Cotugno, N. et al. Virological and immunological features of SARS-CoV-2-infected children who develop neutralizing antibodies. Cell Rep. 34 (2021).
Argelaguet, R. et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491 (2019).
Dopico, X. C. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 6 (2015).
Freebern, E. et al. GWAS and fine-mapping of livability and six disease traits in holstein cattle. BMC Genomics 21, 41 (2020).
Baldwin, C. L. et al. Special features of γδ T cells in ruminants. Mol. Immunol. 134, 161–169 (2021).
Dotiwala, F. & Lieberman, J. Granulysin: killer lymphocyte safeguard against microbes. Curr. Opin. Immunol. 60, 19–29 (2019).
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef Stat. Ref. Online 1–15 https://doi.org/10.1002/9781118445112.stat07841 (2017).
Chen, L. et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat. Med. https://doi.org/10.1038/s41591-022-02014-8 (2022).
Bakker, O. B. et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses. Nat. Immunol. 19, 776–786 (2018).
Chu, X. et al. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol. 22, 1–22 (2021).
Kaczorowski, K. J. et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc. Natl. Acad. Sci. USA. 114, E6097–E6106 (2017).
Lin Da, J. et al. Rewilding Nod2 and Atg16l1 mutant mice uncovers genetic and environmental contributions to microbial responses and immune cell composition. Cell Host Microbe 27, 830–840.e4 (2020).
Netea, M. G., Wijmenga, C. & O’Neill, L. A. J. Genetic variation in Toll-like receptors and disease susceptibility. Nat. Immunol. 13, 535–542 (2012).
Chu, X. et al. A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes. Elife 11, 1–17 (2022).
Boahen, C. K. et al. A functional genomics approach in Tanzanian population identifies distinct genetic regulators of cytokine production compared to European population. Am. J. Hum. Genet. 109, 471–485 (2022).
Kullberg, B. et al. A comprehensive genetic map of cytokine responses in Lyme borreliosis. 1–15 https://doi.org/10.1038/s41467-024-47505-z (2024).
Saint-André, V. et al. Smoking changes adaptive immunity with persistent effects. Nature 626, 827–835 (2024).
Dong Kim, K. et al. Adaptive immune cells temper initial innate responses. Nat. Med. 13, 1248–1252 (2007).
Lakshmikanth, T. et al. Human immune system variation during 1 year. Cell Rep. 32 (2020).
Stojkovic, B., McLoughlin, R. M. & Meade, K. G. In vivo relevance of polymorphic Interleukin 8 promoter haplotype for the systemic immune response to LPS in Holstein-Friesian calves. Vet. Immunol. Immunopathol. 182, 1–10 (2016).
Meade, K. G., O’Gorman, G. M., Narciandi, F., MacHugh, D. E. & O’Farrelly, C. Functional characterisation of bovine interleukin 8 promoter haplotypes in vitro. Mol. Immunol. 50, 108–116 (2012).
Stojkovic, B., Mullen, M. P., Donofrio, G., McLoughlin, R. M. & Meade, K. G. Interleukin 8 haplotypes drive divergent responses in uterine endometrial cells and are associated with somatic cell score in Holstein-Friesian cattle. Vet. Immunol. Immunopathol. 184, 18–28 (2017).
Berg, D. J. et al. Enterocolitis and colon cancer in interleukin-10-deficient mice are associated with aberrant cytokine production and CD4+ Th1-like responses. J. Clin. Invest. 98, 1010–1020 (1996).
Cesta, M. C. et al. The Role of Interleukin-8 in Lung Inflammation and Injury: Implications for the Management of COVID-19 and Hyperinflammatory Acute Respiratory Distress Syndrome. Front. Pharmacol. 12, 1–7 (2022).
Carlini, V. et al. The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Front. Immunol. 14, 1–19 (2023).
Pardon, B. et al. Longitudinal study on morbidity and mortality in white veal calves in Belgium. BMC Vet. Res. 8 (2012).
Meyermans, R. et al. Genetic and genomic analysis of Belgian Blue’s susceptibility for psoroptic mange. Genet. Sel. Evol. 56, 1–12 (2024).
Duffy, D. et al. Functional analysis via standardized whole-blood stimulation systems defines the boundaries of a healthy immune response to complex stimuli. Immunity 40, 436–450 (2014).
Reid, C., Beynon, C., Kennedy, E., O’Farrelly, C. & Meade, K. G. Bovine innate immune phenotyping via a standardized whole blood stimulation assay. Sci. Rep. 11, 1–13 (2021).
Lesueur, J. et al. Standardized whole blood assay and bead-based cytokine profiling reveal commonalities and diversity of the response to bacteria and TLR ligands in cattle. Front. Immunol. 13, 1–15 (2022).
Sailani, M. R. et al. Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California. Nat. Commun. 11, 1–12 (2020).
de Goede, O. M. et al. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease. Cell 184, 2633–2648.e19 (2021).
Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022).
Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376 (2022).
Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376 (2022).
Reid, C. et al. Long-term in vivo vitamin D3 supplementation modulates bovine IL-1 and chemokine responses. Sci. Rep. 13, 1–13 (2023).
Flores-Villalva, S. et al. Low serum vitamin D concentrations in Spring-born dairy calves are associated with elevated peripheral leukocytes. Sci. Rep. 11, 1–11 (2021).
Barnett, I. J., Lee, S. & Lin, X. Detecting rare variant effects using extreme phenotype sampling in sequencing association studies. Genet. Epidemiol. 37, 142–151 (2013).
Bjørnland, T., Bye, A., Ryeng, E., Wisløff, U. & Langaas, M. Powerful extreme phenotype sampling designs and score tests for genetic association studies. Stat. Med. 37, 4234–4251 (2018).
van Deuren, R. C. et al. Impact of rare and common genetic variation in the interleukin-1 pathway on human cytokine responses. Genome Med. 13, 1–17 (2021).
Gualdrón Duarte, J. L. et al. Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals. Genet. Sel. Evol. 55, 1–17 (2023).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Depristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–501 (2011).
Nath, A. P. et al. Multivariate genome-wide association analysis of a cytokine network reveals variants with widespread immune, haematological, and cardiometabolic pleiotropy. Am. J. Hum. Genet. 105, 1076–1090 (2019).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing author (s): Yoav Benjamini and Yosef Hochberg Source. J. R. Stat. Soc. 57, 289–300 (1995).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Druet, T. et al. Selection in action: Dissecting the molecular underpinnings of the increasing muscle mass of Belgian Blue Cattle. BMC Genomics 15, 1–12 (2014).
Shim, H. et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS ONE 10, 1–20 (2015).
McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10 (2014).
Dixon, P. Computer program review VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Argelaguet, R. et al. MOFA+: A statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 1–17 (2020).
Li, S. & Gillet, L. Genetic and non-genetic factors distinctly shape the variation of the immune response in cattle. GitHub Repos https://doi.org/10.5281/zenodo.17514186 (2025).
Acknowledgements
C.D. is a research fellow of the FRIA. We thank the bacteriology lab of FARAH for providing the bacteria used in our study. We are grateful to the Walloon Region for providing funding through the Resistomics Project (L.G., C.C., P.M.). This research was supported by the European Regional Development Fund (FEDER-SYSTIMM; L.G.). We thank the Consortium des Equipements de Calcul Intensitf en Fédération Wallonie Bruxelles (CECI), funded by the F.R.S.-FNRS for providing the supercomputing facilities for the genome-wide association studies. We also thank all the people in CBS (Ciney, Belgium) for their assistance in sample collection.
Author information
Authors and Affiliations
Contributions
S.L., F.M. and C.D. measured all immunophenotypes; J.J. and R.S. provided support to perform analyses in the lab; P.C., M.D. and P.M. provided support for the collection of data on animals; L.T., G.C.M., J.L.G.D., T.D., and C.C. performed genomic imputations and transcriptomic analyses and provided support for GWAS and colocalization analyses; S.L., F.M., C.D., and L.G. analyzed the data; T.D., M.N., M.G., and C.C. critically reviewed the analyses; S.L., F.M., C.C., and L.G. conceived the approach; S.L., F.M., and L.G. drafted the paper; all authors contributed to the final paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Cliona O’Farrelly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Li, S., Myster, F., Darimont, C. et al. Genetic and non-genetic factors distinctly shape the variation of the immune response in cattle. Nat Commun (2026). https://doi.org/10.1038/s41467-025-68234-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-025-68234-x
