Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors

A Publisher Correction to this article was published on 03 May 2018

This article has been updated

Abstract

The quantification and characterization of circulating immune cells provide key indicators of human health and disease. To identify the relative effects of environmental and genetic factors on variation in the parameters of innate and adaptive immune cells in homeostatic conditions, we combined standardized flow cytometry of blood leukocytes and genome-wide DNA genotyping of 1,000 healthy, unrelated people of Western European ancestry. We found that smoking, together with age, sex and latent infection with cytomegalovirus, were the main non-genetic factors that affected variation in parameters of human immune cells. Genome-wide association studies of 166 immunophenotypes identified 15 loci that showed enrichment for disease-associated variants. Finally, we demonstrated that the parameters of innate cells were more strongly controlled by genetic variation than were those of adaptive cells, which were driven by mainly environmental exposure. Our data establish a resource that will generate new hypotheses in immunology and highlight the role of innate immunity in susceptibility to common autoimmune diseases.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Quantification of immune cells and cell-surface markers measured in the Milieu Intérieur cohort.
Fig. 2: Effects of age, sex and CMV infection on the number of innate and adaptive cells in healthy people.
Fig. 3: Effects of smoking on the number of innate and adaptive immune cells in healthy people.
Fig. 4: Genome-wide significant associations with 166 immunophenotypes measured in healthy people.
Fig. 5: Proportion of variance of the parameters of innate and adaptive cells explained by non-genetic and genetic factors.

Change history

  • 03 May 2018

    In the version of this article initially published, the name of one author was incorrect (James P. Santo). The correct name is James P. Di Santo. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    Bernard, C. Introduction à l’étude de la médecine expérimentale. (Libraires de l’Académie Impériale de Médecine, 1865).

  2. 2.

    Altfeld, M. & Gale, M. Jr. Innate immunity against HIV-1 infection. Nat. Immunol. 16, 554–562 (2015).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Orme, I. M., Robinson, R. T. & Cooper, A. M. The balance between protective and pathogenic immune responses in the TB-infected lung. Nat. Immunol. 16, 57–63 (2015).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Tollerud, D. J. et al. The influence of age, race, and gender on peripheral blood mononuclear-cell subsets in healthy nonsmokers. J. Clin. Immunol. 9, 214–222 (1989).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Reichert, T. et al. Lymphocyte subset reference ranges in adult Caucasians. Clin. Immunol. Immunopathol. 60, 190–208 (1991).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Liston, A., Carr, E. J. & Linterman, M. A. Shaping Variation in the Human Immune System. Trends Immunol. 37, 637–646 (2016).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Goronzy, J. J. & Weyand, C. M. Successful and maladaptive T cell aging. Immunity 46, 364–378 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Sauce, D. & Appay, V. Altered thymic activity in early life: how does it affect the immune system in young adults? Curr. Opin. Immunol. 23, 543–548 (2011).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Furman, D. et al. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9, 659 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Boeckh, M. & Geballe, A. P. Cytomegalovirus: pathogen, paradigm, and puzzle. J. Clin. Invest. 121, 1673–1680 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Wertheimer, A. M. et al. Aging and cytomegalovirus infection differentially and jointly affect distinct circulating T cell subsets in humans. J. Immunol. 192, 2143–2155 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Furman, D. et al. Cytomegalovirus infection enhances the immune response to influenza. Sci. Transl. Med. 7, 281ra43 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Roederer, M. et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Thomas, S. et al. The Milieu Intérieur study - an integrative approach for study of human immunological variance. Clin. Immunol. 157, 277–293 (2015).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Vivier, E. et al. Innate or adaptive immunity? the example of natural killer cells. Science 331, 44–49 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hasan, M. et al. Semi-automated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping. Clin. Immunol. 157, 261–276 (2015).

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Patterson, S. et al. Cortisol patterns are associated with T cell activation in HIV. PLoS ONE 8, e63429 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Serafini, N., Vosshenrich, C. A. J. & Di Santo, J. P. Transcriptional regulation of innate lymphoid cell fate. Nat. Rev. Immunol. 15, 415–428 (2015).

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Dusseaux, M. et al. Human MAIT cells are xenobiotic-resistant, tissue-targeted, CD161hi IL-17-secreting T cells. Blood 117, 1250–1259 (2011).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Amado, I. F. et al. IL-2 coordinates IL-2-producing and regulatory T cell interplay. J. Exp. Med. 210, 2707–2720 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Pennell, L. M., Galligan, C. L. & Fish, E. N. Sex affects immunity. J. Autoimmun. 38, J282–J291 (2012).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Furman, D., Hejblum, B. P., Simon, N., Jojic, V., Dekker, C. L., Thiébaut, R., Tibshirani, R. J. & Davis, M. M. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc Natl Acad Sci USA (2), 869–874 (2014).

  27. 27.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Della Bella, S. et al. Peripheral blood dendritic cells and monocytes are differently regulated in the elderly. Clin. Immunol. 122, 220–228 (2007).

    Article  PubMed  Google Scholar 

  29. 29.

    Puchta, A. et al. TNF drives monocyte dysfunction with age and results in impaired anti-pneumococcal immunity. PLoS. Pathog. 12, e1005368 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Vrisekoop, N. et al. Sparse production but preferential incorporation of recently produced naive T cells in the human peripheral pool. Proc. Natl Acad. Sci. USA 105, 6115–6120 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Tsuchiya, M. et al. Smoking a single cigarette rapidly reduces combined concentrations of nitrate and nitrite and concentrations of antioxidants in plasma. Circulation 105, 1155–1157 (2002).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Kearley, J. et al. Cigarette smoke silences innate lymphoid cell function and facilitates an exacerbated type I interleukin-33-dependent response to infection. Immunity 42, 566–579 (2015).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Monticelli, L. A. et al. Innate lymphoid cells promote lung-tissue homeostasis after infection with influenza virus. Nat. Immunol. 12, 1045–1054 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Cassard, L., Jönsson, F., Arnaud, S. & Daëron, M. Fcγ receptors inhibit mouse and human basophil activation. J. Immunol. 189, 2995–3006 (2012).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Hu, X. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. Nat. Genet. 47, 898–905 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Piasecka, B. et al. Distinctive roles of age, sex and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl. Acad. Sci. USA 115, E488–E497 (2017).

  37. 37.

    GTEx Consortium. The Genotype–Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  Google Scholar 

  38. 38.

    Garris, C. S., Blaho, V. A., Hla, T. & Han, M. H. Sphingosine-1-phosphate receptor 1 signalling in T cells: trafficking and beyond. Immunology 142, 347–353 (2014).

  39. 39.

    Pellegrini, M. et al. Loss of Bim increases T cell production and function in interleukin 7 receptor-deficient mice. J. Exp. Med. 200, 1189–1195 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    van der Harst, P. et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369–375 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Motohashi, T. et al. Molecular cloning and chromosomal mapping of a novel protein gene, M83. Biochem. Biophys. Res. Commun. 250, 244–250 (2000).

    Article  Google Scholar 

  42. 42.

    Feltenmark, S. et al. Eoxins are proinflammatory arachidonic acid metabolites produced via the 15-lipoxygenase-1 pathway in human eosinophils and mast cells. Proc. Natl Acad. Sci. 105, 680–685 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Stämpfli, M. R. & Anderson, G. P. How cigarette smoke skews immune responses to promote infection, lung disease and cancer. Nat. Rev. Immunol. 9, 377–384 (2009).

    Article  PubMed  Google Scholar 

  44. 44.

    Venet, F., Lukaszewicz, A.-C., Payen, D., Hotchkiss, R. & Monneret, G. Monitoring the immune response in sepsis: a rational approach to administration of immunoadjuvant therapies. Curr. Opin. Immunol. 25, 477–483 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Kolaczkowska, E. & Kubes, P. Neutrophil recruitment and function in health and inflammation. Nat. Rev. Immunol. 13, 159–175 (2013).

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Farber, D. L., Yudanin, N. A. & Restifo, N. P. Human memory T cells: generation, compartmentalization and homeostasis. Nat. Rev. Immunol. 14, 24–35 (2014).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Mangino, M., Roederer, M., Beddall, M. H., Nestle, F. O. & Spector, T. D. Innate and adaptive immune traits are differentially affected by genetic and environmental factors. Nat. Commun. 8, 13850 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Casanova, J. L. & Abel, L. Disentangling inborn and acquired immunity in human twins. Cell 160, 13–15 (2015).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Paternoster, L. et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47, 1449–1456 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    von Bubnoff, D. et al. Natural killer cells in atopic and autoimmune diseases of the skin. J. Allergy Clin. Immunol. 125, 60–68 (2010).

    Article  Google Scholar 

  51. 51.

    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using. J. Stat. Softw. 67, 41–48 (2015).

    Article  Google Scholar 

  52. 52.

    Kenward, M. G. & Roger, J. H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997 (1997).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Halekoh, U. & Højsgaard, S. A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models - The R package pbkrtest. J. Stat. Softw. 59, 1–30 (2014).

    Article  Google Scholar 

  54. 54.

    Benjamini, Y. & Yekutieli, D. False discovery rate–adjusted multiple confidence intervals for selected parameters. J. Am. Stat. Assoc. 1000, 71–93 (2005).

    Article  Google Scholar 

  55. 55.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

  56. 56.

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS. Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Behar, D. M. et al. The genome-wide structure of the Jewish people. Nature 466, 238–242 (2010).

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  60. 60.

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

  61. 61.

    Mefford, J. & Witte, J. S. The Covariate’s Dilemma. PLoS Genet. 8, e1003096 (2012).

  62. 62.

    Meinshausen, N. & Bühlmann, P. Stability selection. J. R. Stat. Soc. B 72, 417–473 (2010).

    Article  Google Scholar 

  63. 63.

    Shah, R. D. & Samworth, R. J. Variable selection with error control: another look at stability selection. J. R. Stat. Soc. B 75, 55–80 (2013).

    Article  Google Scholar 

  64. 64.

    Hastie, T., Tibshirani, R. & Friedman, J. Elements of Statistical Learning (Springer, 2009).

  65. 65.

    Wakefield, J. Bayesian and Frequentist Regression Methods (Springer, 2013).

  66. 66.

    Zhou, X. & Stephens, M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407–409 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS ONE 8, e64683 (2013).

  71. 71.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

  73. 73.

    Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Center for Translation Research, Institut Pasteur, and the OMNI Biomarker Development-Flow Cytometry Biomarker group, Genentech, for support. This work benefited from support of the French government’s program ‘Investissement d’Avenir’, managed by the Agence Nationale de la Recherche (reference 10-LABX-69-01). J.B. is a member of the LCCC Linnaeus Center and the ELLIIT Excellence Center at Lund University and is supported by the ELLIIT Excellence Center.

Author information

Affiliations

Authors

Consortia

Contributions

Conceptualization, E.P., L.Q.-M. and M.L.A.; methodology, M.H., V.L., A.U., F.J., B.B., C.L., F.H., L.R., I.P., O.L. and J.P.D.; software, E.P., J.B., V.R., P.S., C.H., B.P. and J.F.; validation, M.H., V.R., F.J., Y.W.L. and M.L.A.; formal analysis, E.P., J.B., V.R., P.S., C.H., C.G., B.P. and J.F.; investigation, E.P., M.H., J.B., V.L., A.U., C.A., F.J., H.Q., M.Z., B.P., C.L., L.R., F.H., O.L., J.P.D. and M.L.A.; data curation, E.P., M.H., J.B., V.R., V.L., A.U., B.P., C.L., L.R., I.P., O.L. and J.P.D.; writing (original draft), E.P., J.B., C.A., D.D. and M.L.A.; writing (review and editing), E.P., M.H., J.B., C.A., L.R., O.L., M.F., J.P.D., J.F., L.Q.-M. and M.L.A.; supervision, E.P., M.H., M.F., J.F., D.D. and M.L.A.; project administration, J.H., S.T. and D.D.; and funding acquisition, M.F., J.F., L.Q.-M. and M.L.A. L.Q.M. and M.L.A. are co-coordinators of the Milieu Intérieur Consortium (more information available at http://www.milieuinterieur.fr/en).

Corresponding authors

Correspondence to Etienne Patin or Matthew L. Albert.

Ethics declarations

Competing interests

A.U., C.H., Y.W.L., J.H., M.Z., C.G. and M.L.A. are employees of Genentech, a member of The Roche Group.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1

Gating strategy for the T cell flow cytometry panel.

Supplementary Figure 2

Gating strategy for the Treg cell flow cytometry panel.

Supplementary Figure 3

Gating strategy for the NKT/MAIT cell flow cytometry panel.

Supplementary Figure 4

Gating strategy for the NK cell flow cytometry panel.

Supplementary Figure 5

Gating strategy for the lineage cell flow cytometry panel.

Supplementary Figure 6

Gating strategy for the B cell flow cytometry panel.

Supplementary Figure 7

Gating strategy for the PMN cell flow cytometry panel.

Supplementary Figure 8

Gating strategy for the DC flow cytometry panel.

Supplementary Figure 9

Gating strategy for the TH cell flow cytometry panel.

Supplementary Figure 10

Gating strategy for the ILC flow cytometry panel.

Supplementary Figure 11 Raw distributions of the 166 immunophenotypes studied in the Milieu Intérieur cohort.

Distributions are shown before outlier removal, imputation, batch correction and transformation to normality. Transformed distributions are shown in Supplementary Fig. 15.

Supplementary Figure 12 Repeatability of semi-automated flow cytometry measurements.

Repeatability of (a) absolute cell counts and (b) MFI estimated based on measurements performed at five different time points over five months on the same five individuals. Points represent the average of measurements across replicates of the same individual, and bars 3 times their standard deviation.

Supplementary Figure 13 Reproducibility of semi-automated flow cytometry measurements.

Comparison of cell counts measured in different flow cytometry panels across the 1,000 Milieu Intérieur donors. Spearman’s ρ correlation coefficients are shown.

Supplementary Figure 14 Batch effects on measured immunophenotypes.

(a) Effect of the sampling hour on the absolute number of CD16hi NK cells. Whole blood samples were collected from the 1,000 Milieu Intérieur healthy subjects every working day from 8 to 11AM. The sampling hour has a significant effect on different immune cell counts, such as the absolute number of CD16hi NK cells, which were adjusted for this batch effect. (b) Effect of the sampling day on immunophenotypes measured in the Milieu Intérieur cohort. Whole blood samples were collected from the 1,000 Milieu Intérieur healthy subjects every working day from September 2012 to august 2013. Variation in the mean fluorescence intensity (MFI) of several protein markers was observed along the sampling period. All MFIs were corrected for this batch effect using the ComBat nonparametric Bayesian framework. Three examples of uncorrected (left column) and corrected (right columns) values are shown.

Supplementary Figure 15 Transformed distributions of the 166 immunophenotypes studied in the Milieu Intérieur cohort.

Distributions are shown after outlier removal, imputation, batch correction and transformation to normality. Raw distributions are shown in Supplementary Fig. 11.

Supplementary Figure 16 Correlations among circulating levels of immune cell populations in the Milieu Intérieur cohort.

Sample correlations were estimated between the residuals of the log-transformed immunophenotypes from a regression model that included non-genetic covariates selected with the stability selection algorithm (described below) on the 39 non-genetic covariates, together with the batch variables. Correlations between -0.3 and 0.3 were set to 0, to improve readability.

Supplementary Figure 17 Age, sex and CMV infection impact protein levels of immune cell markers in 1,000 healthy individuals.

Significant multiplicative effects of (a) increasing age, (b) female sex and (c) CMV seropositivity on the mean fluorescence intensity (MFI) of protein markers of immune cells of 1,000 healthy individuals, controlling for the two other factors, batch effects and genome-wide significant SNPs The multiplicative effect sizes were estimated in a linear mixed model with a log-transformed response variable, controlling for batch effects and genome-wide significant SNPs, and then transformed to the original data scale. The 99% confidence intervals (99%CIs) were false coverage-adjusted, considering that only 99%CIs of effect sizes with a significant test (adjusted P<0.01) are shown. Adaptive and innate immune cells are represented in grey and black, respectively. These analyses were performed with the mmi R package (Online Methods).

Supplementary Figure 18 Estimated dependency matrix among 39 non-genetic variables analyzed in this study.

Values are generalized R2 measures from pairwise fitted generalized linear models (Online Methods). Variables listed in rows are response variables and those listed in columns are predictor variables in the models. Categorical variables (i.e., exposure to dust, level of education, income and smoking) were used only as predictors. Details on the non-genetic variables can be found in Supplementary Table 1.

Supplementary Figure 19 Smoking strongly impacts protein levels of immune cell markers in 1,000 healthy individuals.

(a) Significant multiplicative effects of 39 non-genetic factors on protein levels of immune cell markers (i.e., MFI) in healthy individuals. Colors represent levels of association (i.e., –log10(q-values)) between the 39 non-genetic factors and protein levels of immune cell markers, at a false discovery rate (FDR) < 1%. Except when their effects were specifically measured, immunophenotypes were regressed for age, sex, CMV status, batch effects and genome-wide significant SNPs (Online Methods). (b) Significant effect sizes of active smoking on protein levels of immune cell markers in 1,000 healthy individuals. The multiplicative effect sizes were estimated in a linear mixed model with a log-transformed response variable, controlling for age, sex, CMV status, batch effects and genome-wide significant SNPs, and then transformed to the original data scale. The 99% confidence intervals (99%CIs) were false coverage-adjusted, considering that only 99%CIs of effect sizes with a significant test (adjusted P<0.01) are shown. Adaptive and innate immune cells are represented in grey and black, respectively. Effect sizes in past smokers are shown, for comparison purposes. These analyses were performed with the mmi R package (Online Methods).

Supplementary Figure 20 Genetic relatedness and structure in the Milieu Intérieur cohort.

(a) Genetic relatedness in the Milieu Intérieur cohort. Pairs of related subjects were detected using an estimate of the kinship coefficient and the proportion of SNPs that are not identical-by-state between all possible pairs of subjects, using KING. (b) Genetic structure of the Milieu Intérieur cohort. Genetic structure was estimated with the Principal Component Analysis (PCA) implemented in EIGENSTRAT. For comparison purposes, the analysis was performed on 261,827 independent SNPs and 1,723 individuals, which include the 1,000 Milieu Intérieur subjects together with 723 individuals from a selection of 36 populations of North Africa, the Near East, western and northern Europe (Online Methods).

Supplementary Figure 21 Local association signals for the 15 genome-wide significant hits associated with immunophenotypes measured in the Milieu Intérieur cohort.

Each point is a SNP, whose color represents its level of linkage disequilibrium (r2) with the best hit (in purple). Blue lines indicate local recombination rates.

Supplementary Figure 22 Local association signals for the six genome-wide significant hits identified by conditional GWAS of the 15 immunophenotypes showing strong genetic association in the Milieu Intérieur cohort.

These association signals were identified when conditioning on genotypes of main GWAS signals (Online Methods).

Supplementary Figure 23 Local association signals detected by multi-trait GWAS of immunophenotypes measured in the Milieu Intérieur cohort.

(a) Local association signals at loci influencing principal components of a PCA of all innate cell immunophenotypes. No suggestive signal (P<5x10-8) was observed for PCs of a PCA of adaptive cell immunophenotypes. (b) Local association signal with the absolute numbers of HLA-DR+ TCM, TEM and TEMRA cells, either CD4+ or CD8+. The six corresponding immunophenotypes were analyzed altogether using a multivariate GWAS (Online Methods).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-23

Life Sciences Reporting Summary

Supplementary Note

Supplementary Table 1

Demographic variables included in the study

Supplementary Table 2

Staining antibodies used for the ten 8-color flow cytometry panels

Supplementary Table 3

Immunophenotypes measured by standardized flow cytometry in this study

Supplementary Table 4

Genome-wide signals of association with immunophenotypes in the Milieu Intérieur cohort, with the likely effects of candidate variants on gene expression, protein sequence and human diseases and traits

Supplementary Table 5

Association signals (-log10(P)) between genome-wide significant variants of loci 1 to 14 and all measured immunophenotypes

Supplementary Table 6

Other genome-wide significant (P<10-10) or suggestive (P<5x10-8) signals of association with immunophenotypes in the Milieu Intérieur cohort

Supplementary Table 7

Significant associations of HLA amino-acid positions with candidate immunophenotypes in the Milieu Intérieur cohort

Supplementary Table 8

Associations of HLA classical alleles with candidate immunophenotypes in the Milieu Intérieur cohort

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Patin, E., Hasan, M., Bergstedt, J. et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat Immunol 19, 302–314 (2018). https://doi.org/10.1038/s41590-018-0049-7

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing