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.
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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.
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.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Gating strategy for the T cell flow cytometry panel.
Gating strategy for the Treg cell flow cytometry panel.
Gating strategy for the NKT/MAIT cell flow cytometry panel.
Gating strategy for the NK cell flow cytometry panel.
Gating strategy for the lineage cell flow cytometry panel.
Gating strategy for the B cell flow cytometry panel.
Gating strategy for the PMN cell flow cytometry panel.
Gating strategy for the DC flow cytometry panel.
Gating strategy for the TH cell flow cytometry panel.
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.
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.
Comparison of cell counts measured in different flow cytometry panels across the 1,000 Milieu Intérieur donors. Spearman’s ρ correlation coefficients are shown.
(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).
(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 Figures 1-23
Demographic variables included in the study
Staining antibodies used for the ten 8-color flow cytometry panels
Immunophenotypes measured by standardized flow cytometry in this study
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
Association signals (-log10(P)) between genome-wide significant variants of loci 1 to 14 and all measured immunophenotypes
Other genome-wide significant (P<10-10) or suggestive (P<5x10-8) signals of association with immunophenotypes in the Milieu Intérieur cohort
Significant associations of HLA amino-acid positions with candidate immunophenotypes in the Milieu Intérieur cohort
Associations of HLA classical alleles with candidate immunophenotypes in the Milieu Intérieur cohort
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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
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