We report on the influence of ~22 million variants on 731 immune cell traits in a cohort of 3,757 Sardinians. We detected 122 significant (P < 1.28 × 10−11) independent association signals for 459 cell traits at 70 loci (53 of them novel) identifying several molecules and mechanisms involved in cell regulation. Furthermore, 53 signals at 36 loci overlapped with previously reported disease-associated signals, predominantly for autoimmune disorders, highlighting intermediate phenotypes in pathogenesis. Collectively, our findings illustrate complex genetic regulation of immune cells with highly selective effects on autoimmune disease risk at the cell-subtype level. These results identify drug-targetable pathways informing the design of more specific treatments for autoimmune diseases.
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Full GWAS summary statistics have been deposited in the GWAS Catalog with accession numbers from GCST0001391 (https://www.ebi.ac.uk/gwas/studies/GCST0001391) to GCST0002121 (https://www.ebi.ac.uk/gwas/studies/GCST0002121). The accession number for each trait is reported in Supplementary Table 1B.
Disease summary statistics used to identify coincident associations were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/home, version v1.0, Ensembl release version e93, date 2018-08-28) and from ImmunoBase (https://genetics.opentargets.org/immunobase, version 12-May-2016). Summary statistics for colocalization analyses were downloaded from the respective web pages: RA (http://plaza.umin.ac.jp/~yokada/datasource/files/GWASMetaResults/RA_GWASmeta_European_v2.txt.gz); T1D (https://datadryad.org/stash/dataset/doi:10.5061/dryad.ns8q3); MS (http://imsgc.net/publications/); SLE (http://insidegen.com/insidegen-LUPUS-data.html); allergy (https://genepi.qimr.edu.au/staff/manuelf/gwas_results/SHARE-without23andMe.LDSCORE-GC.SE-META.v0.gz); IBD, Crohn’s disease and ulcerative colitis (ftp://ftp.sanger.ac.uk/pub/project/humgen/summary_statistics/human/2016-11-07/). Molecular QTLs are from the LinDA QTL Catalog (http://linda.irgb.cnr.it/, version 20190109). Source data are provided with this paper.
Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).
Roederer, M. et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).
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).
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).
Lagou, V. et al. Genetic architecture of adaptive immune system identifies key immune regulators. Cell Rep. 25, 798–810 (2018).
Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).
Chang, Y. J., Zhao, X. Y. & Huang, X. J. Immune reconstitution after haploidentical hematopoietic stem cell transplantation. Biol. Blood Marrow Transplant. 20, 440–449 (2014).
Ogonek, J. et al. Immune reconstitution after allogeneic hematopoietic stem cell transplantation. Front. Immunol. 17, 507 (2016).
Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Steri, M. et al. Overexpression of the cytokine BAFF and autoimmunity risk. N. Engl. J. Med. 376, 1615–1626 (2017).
Han, J. W. et al. Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus. Nat. Genet. 41, 1234–1237 (2009).
Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).
Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).
International Multiple Sclerosis Genetics Consortium (IMSGC) et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).
Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).
Jiang, D. K. et al. Genetic variants in five novel loci including CFB and CD40 predispose to chronic hepatitis B. Hepatology 62, 118–128 (2015).
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
Lee, Y. C. et al. Two new susceptibility loci for Kawasaki disease identified through genome-wide association analysis. Nat. Genet. 44, 522–525 (2012).
Ferreira, M. A. et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 49, 1752–1757 (2017).
Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).
Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).
Hinks, A. et al. Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis. Nat. Genet. 45, 664–669 (2013).
Liu, J. Z. et al. Dense genotyping of immune-related disease regions identifies nine new risk loci for primary sclerosing cholangitis. Nat. Genet. 45, 670–675 (2013).
Petukhova, L. et al. Genome-wide association study in alopecia areata implicates both innate and adaptive immunity. Nature 466, 113–117 (2010).
Betz, R. C. et al. Genome-wide meta-analysis in alopecia areata resolves HLA associations and reveals two new susceptibility loci. Nat. Commun. 6, 5966 (2015).
International Multiple Sclerosis Genetics Consortium (IMSGC) et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).
Jin, Y. et al. Variant of TYR and autoimmunity susceptibility loci in generalized vitiligo. N. Engl. J. Med. 362, 1686–1697 (2010).
Cooper, J. D. et al. Seven newly identified loci for autoimmune thyroid disease. Hum. Mol. Genet. 21, 5202–5208 (2012).
Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–1201 (2011).
Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
Qiu, F. et al. A genome-wide association study identifies six novel risk loci for primary biliary cholangitis. Nat. Commun. 20, 14828 (2017).
Fortune, M. D. et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).
Jin, Y. et al. Genome-wide association studies of autoimmune vitiligo identify 23 new risk loci and highlight key pathways and regulatory variants. Nat. Genet. 48, 1418–1424 (2016).
Bradfield, J. P. et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet. 7, e1002293 (2011).
Morris, D. E. et al. Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus. Nat. Genet. 48, 940–946 (2016).
Roychoudhuri, R. et al. BACH2 represses effector programs to stabilize T(reg)-mediated immune homeostasis. Nature 498, 506–510 (2013).
Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).
Floris, M., Olla, S., Schlessinger, D. & Cucca, F. Genetic-driven druggable target identification and validation. Trends Genet. 34, 558–570 (2018).
Fang, H. et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat. Genet. 51, 1082–1091 (2019).
Cytlak, U. et al. Ikaros family zinc finger 1 regulates dendritic cell development and function in humans. Nat. Commun. 9, 1239 (2018).
Presto, J. K. & Werth, V. P. Cutaneous lupus erythematosus: current treatment options. Curr. Treat. Options Rheumatol. 2, 36–48 (2016).
Kim, J. M., Park, S. H., Kim, H. Y. & Kwok, S. K. A plasmacytoid dendritic cells-type I interferon axis is critically implicated in the pathogenesis of systemic lupus erythematosus. Int. J. Mol. Sci. 16, 14158–14170 (2015).
Dzionek, A. BDCA-2, a novel plasmacytoid dendritic cell-specific type II C-type lectin, mediates antigen capture and is a potent inhibitor of interferon alpha/beta induction. J. Exp. Med. 194, 1823–1834 (2001).
Blomberg, S., Eloranta, M. L., Magnusson, M., Alm, G. V. & Rönnblom, L. Expression of the markers BDCA-2 and BDCA-4 and production of interferon-alpha by plasmacytoid dendritic cells in systemic lupus erythematosus. Arthritis Rheumatol. 48, 2524–2532 (2003).
Cao, W. et al. BDCA2/Fc epsilon RI gamma complex signals through a novel BCR-like pathway in human plasmacytoid dendritic cells. PLoS Biol. 5, e248 (2007).
Gottlieb, P. A. Failure to preserve β-cell function with mycophenolate mofetil and daclizumab combined therapy in patients with new- onset type 1 diabetes. Diabetes Care 33, 826–832 (2010).
Du, F. H. et al. Next-generation anti-CD20 monoclonal antibodies in autoimmune disease treatment. Autoimmun. Highlights 8, 12 (2017).
Labrijn, A. F., Janmaat, M. L., Reichert, J. M. & Parren, P. W. H. I. Bispecific antibodies: a mechanistic review of the pipeline. Nat. Rev. Drug Discov. 18, 585–608 (2019).
Gateva, V. et al. A large-scale replication study identifies TNIP1, PRDM1, JAZF1, UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus. Nat. Genet. 41, 1228–1233 (2009).
McGovern, D. et al. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nat. Genet. 42, 332–337 (2010).
Pala, M. et al. Population- and individual-specific regulatory variation in Sardinia. Nat. Genet. 49, 700–707 (2017).
de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Pistis, G. et al. Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs. Eur. J. Hum. Genet. 23, 975–983 (2015).
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).
Ferreira, M. A. et al. Quantitative trait loci for CD4:CD8 lymphocyte ratio are associated with risk of type 1 diabetes and HIV-1 immune control. Am. J. Hum. Genet. 86, 88–92 (2010).
Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).
Censing, J. C. et al. Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study. PLoS Med. 14, e1002362 (2017).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Palmer, T. M. et al. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am. J. Epidemiol. 173, 1392–1403 (2011).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
Teumer, A. Common methods for performing Mendelian randomization. Front. Cardiovasc. Med. 5, 51 (2018).
We thank all of the volunteers who generously participated in this study; S. Naitza and J. Todd for helpful suggestions; the Consortium ‘Sa Corona Arrubia della Marmilla’ for making available equipment and scientific instruments; and the IMSGC and WTCCC2 consortia for access to the summary statistics from the key disease GWAS. We acknowledge support by grants (2011/R/13 and 2015/R/09, to F.C.) from the Italian Foundation for Multiple Sclerosis; contracts (HHSN271201600005C, to F.C.) from the Intramural Research Program of the National Institute on Aging, National Institutes of Health (NIH); a grant (FaReBio2011 ‘Farmaci e Reti Biotecnologiche di Qualità’, to F.C.) from the Italian Ministry of Economy and Finance; a grant (633964, to F.C.) from the Horizon 2020 Research and Innovation Program of the European Union; grants (U1301.2015/AI.1157. BE Prat. 2015-1651, to F.C. and U965.2014/AI.847.MGB Prat. 2014.0597, to M.S.) from Fondazione di Sardegna; grants (‘Centro per la Ricerca di Nuovi Farmaci per Malattie Rare, Trascurate e della Povertà’ and ‘Progetto Collezione di Composti Chimici ed Attività di Screening’ to F.C.) from Ministero dell’Istruzione, dell’Università e della Ricerca.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Flow cytometry gating strategy of TBNK, regulatory T cell, maturation stages of T cell, and dendritic cell antibody panel.
TBNK panel. a, Lymphocytes (violet) and granulocytes (blue). b, CD14+ monocytes (light blue). c, HLA DR++CD14+ monocytes. d, CD3+ (T cells, purple) and CD3- (green) lymphocytes. e, B and NK cells are CD19+ and CD16/CD56+, respectively. f, HLA DR+ NK cells. g, T cells are divided based on CD4 and CD8 expression. h, TCR-ϒδ+ T cells. i, NKT are CD3+ and CD16/CD56+. j, HLA DR+ T cells. HLA DR+CD4 and HLA DR+CD8 subsets are obtained by intersecting HLA DR+ T cell with CD4+ and CD8br lymphocytes. Regulatory T cell panel. a, CD4+ (blue) and CD8+ (violet) lymphocytes. b, CD4+ Tregs (green) are CD25high CD127low. c, Resting (CD45RA+CD25++, pink), activated (CD45RA-CD25+++, orange) and secreting (CD45RA-CD25++, purple) CD4+ Tregs. D-E) CD25hiCD4+ lymphocytes are divided based on CD45RA expression. F) CD4-CD8- T cells (DN, black) are divided in CD28+ and CD28-. G-H-I-J-K) CD39 expression on Treg subsets, CD4 and CD8br T cells, respectively. L) CD8br cells division based on CD45RA vs CD28 expression. M-N) CD25++CD28-CD8br and CD127-CD28-CD8br identification. Maturation stages of T cell panel. a, CD4+ (blue), CD8br (violet) and CD4-CD8- (black) T cells are analyzed for CD45RA vs CCR7 (plots B-C-D, respectively) identifying naïve (CCR7+CD45RA+), central memory (CM, CCR7+CD45RA-), effector memory (EM, CCR7-CD45RA-), and terminally differentiated (TD, CCR7-CD45RA+) subsets. Dendritic cell antibody panel. a, Monocytes (pink). b, c, DCs are Lineage (Lin) negative and HLA DR+. d, Myeloid (green) and plasmacytoid (violet) DCs are CD11c+ and CD123+, respectively. e, f, CD86 and CD62L expression on cDC. G-H-I) CD11c, CD62L and HLA DR expression on monocytes.
Extended Data Fig. 2 Flow cytometry gating strategy of B cell, monocyte, myeloid cell antibody panel.
B cell panel. a-b, Lymphocyte (red). c, B lymphocytes (violet) are CD19+. d, IgD+ B cells. B cells classification based on e) CD24 vs CD38; f) CD27 vs IgD; g) IgD vs CD38; h) IgD vs CD24; i) CD24+CD27+ memory B cells. j, Plasma blasts/plasma cells (PB/PC) are CD20-CD38hi B cells. Monocyte panel. A-B-C) Monocyte (blue). D) Monocytes division into CD14+CD16- (classical), CD14-CD16+ (non-classical) and CD14+CD16+ (intermediate). Myeloid panel. A-B-C) Lympho-monocytes (red). d, Viable and myeloid-enriched cells (green) are obtained excluding lymphoid cells, which are lineage1 (Lin1) positive, and dead cells, which are 7-aminoactinomycin-D (7AAD) positive. e, Hematopoietic stem cells (HSC). f, CD14 vs CD66b expression and g) CD33 vs HLA DR expression on myeloid-enriched cells. The intersection of CD33dim/br HLA DRdim/- cells in g) with CD14+ monocytes (orange) in f) results in monocytic myeloid-derived dendritic cells (Mo MDSC). h, The deletion of CD14+ monocytes (orange) from cells in g) discriminates five subsets using CD33 vs HLA DR markers. i, CD66b+ cells were excluded from the CD33dim HLA DR- cells (blue) and j) the resulting CD33dimHLA DR-CD66- population was further divided into basophils and immature MDSC (Im MDSC) based on CD45 and CD11b expression. k, CD33br HLA DR+ cells (black) division into CD14 dim and CD14-. l, CD11b expression on CD33dim HLA DR+ cells (purple). Intersection of CD33dim HLA DR- in h) with CD66b++ cells in f) corresponds to granulocytic myeloid-derived dendritic cells (Gr MDSC).
Heatmap of phenotypic correlations for MFI pairs calculated using the Spearman coefficient. Dendrograms represent the clustering: short branches indicate strong phenotypic correlation between traits, whereas long branches weak correlation. Color gradations represent the correlation strength, with red indicating direct correlation (from 0 to +1) and blue inverse correlation (from 0 to -1).
Extended Data Fig. 7 Drug target prioritization (Priority index, Pi) score of our drug targets candidates segmented by gene categories.
It is shown the distributions of the Pi-rating (computed in Fang et al., 42) of our candidate genes (colored boxplots) segmented for different gene categories (“All” genes, “eGenes”, “Seed” genes and cell surface genes) with the relative background distributions (grey boxplots, that is that consider all the genes belonging to the respective category). eGenes means that the gene has an eQTL colocalizing with the disease; seed gene means that the genes have a genetic link to the disease (by eQTL, gene proximity or chromatin conformation) as defined in Fang et al., 42. The boxplot inside the violin plot reports a white circle indicating the median value, with the box limits indicating the upper and the lower quartiles. The whisker at the upper side of the box extends to the minimum between the interquartile range (IQR) x 1.5 and the overall maximum value of the data. The whisker at the bottom side of the box extends to the maximum between IQR x 1.5 and the overall minimum value of the data.
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Orrù, V., Steri, M., Sidore, C. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet 52, 1036–1045 (2020). https://doi.org/10.1038/s41588-020-0684-4
Nature Reviews Rheumatology (2021)