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Complex genetic signatures in immune cells underlie autoimmunity and inform therapy

Nature Genetics volume 52, pages 1036–1045 (2020)Cite this article

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An Author Correction to this article was published on 18 September 2020

This article has been updated

Abstract

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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Fig. 1: Schematic representation of the main leukocyte subsets assessed by GWAS.
Fig. 2: Genetic associations of the immune traits assessed.
Fig. 3: Coincident genetic association between immune traits and diseases.
Fig. 4: Regional association plots in the CD40 region.
Fig. 5: Drug target prioritization (priority index, Pi) score of our drug target candidates.

Data availability

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.

Change history

  • 18 September 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Valeria Orrù, Maristella Steri, Carlo Sidore.

Authors and Affiliations

  1. Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy

    Valeria Orrù, Maristella Steri, Carlo Sidore, Michele Marongiu, Valentina Serra, Stefania Olla, Gabriella Sole, Sandra Lai, Mariano Dei, Antonella Mulas, Francesca Virdis, Maria Grazia Piras, Monia Lobina, Mara Marongiu, Maristella Pitzalis, Francesca Deidda, Annalisa Loizedda, Stefano Onano, Magdalena Zoledziewska, Matteo Floris, Mauro Pala, Edoardo Fiorillo & Francesco Cucca

  2. Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy

    Stefano Onano, Matteo Floris & Francesco Cucca

  3. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

    Stephen Sawcer

  4. Division of Genetics, The Children’s Hospital of Philadelphia, and Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA

    Marcella Devoto

  5. Dipartimento di Medicina Traslazionale e di Precisione, Sapienza Università di Roma, Rome, Italy

    Marcella Devoto

  6. Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Myriam Gorospe & David Schlessinger

  7. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    Gonçalo R. Abecasis

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  1. Valeria Orrù
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  2. Maristella Steri
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Contributions

F.C. conceived and supervised the study. F.C., V.O., M.S. and C.S. drafted the manuscript; M. Pala, S. Olla and M.F. contributed to the writing of specific sections; D.S., M.G., M. Devoto, M.Z. and G.R.A. revised the manuscript; V.O., E.F. and F.C. designed flow cytometric panels; M. Dei., S.L., V.S. and F.V. measured immunophenotypes; V.O., V.S. and E.F. analyzed raw immunophenotype data; A.M., S.L., M. Dei, M.L., M.G.P., M. Pitzalis, F.D. and A.L. isolated DNA; M.Z. and A.M. performed array genotyping; M.S., C.S., Michele Marongiu and G.S. carried out genetic analysis; M. Pala, S. Onano and Mara Marongiu performed RNA data analyses; M.F. and S. Olla carried out bioinformatics and drug target analyses; Michele Marongiu managed the SardiNIA project database; S.S. shared genetic data on MS; F.C., G.R.A. and D.S. provided funds and reagents. All authors read the paper and contributed to its final form.

Corresponding author

Correspondence to Francesco Cucca.

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Extended data

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).

Extended Data Fig. 3 Phenotypic correlation among expression level of surface markers.

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).

Source data

Extended Data Fig. 4 Genetic correlation among expression level of surface markers.

Heatmap of genetic correlations for MFIs pairs calculated as previously described1. The description of the figure is as for Extended Data Fig. 3.

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Extended Data Fig. 5 Phenotypic correlation among cell levels.

Heatmap of phenotypic correlations for cell counts and T/B and CD4/CD8br ratios, calculated using the Spearman coefficient. The description of the figure is as for Extended Data Fig. 3.

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Extended Data Fig. 6 Genetic correlation among absolute cell counts.

Heatmap of genetic correlations for cell counts and T/B and CD4/CD8br ratios, calculated as previously described1. The description of the figure is as for Extended Data Fig. 3.

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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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Supplementary information

Supplementary Information

Supplementary table list, results and Fig. 1.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8.

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Source Data Fig. 5

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Source Data Extended Data Fig. 3

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Source Data Extended Data Fig. 5

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Source Data Extended Data Fig. 6

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Source Data Extended Data Fig. 7

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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

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