The Genetic Architecture of the Human Immune System: A ...

Resource

The Genetic Architecture of the Human Immune System: A Bioresource for Autoimmunity and Disease Pathogenesis

Graphical Abstract

Authors

Mario Roederer, Lydia Quaye, ..., Tim D. Spector, Frank O. Nestle

Correspondence

roederer@ (M.R.), tim.spector@kcl.ac.uk (T.D.S.)

In Brief

The study of a large and homogenous population of human twins identifies numerous genetic loci controlling the phenotype or number of functionally important immune subsets in the blood, providing a database to test associations of any genetic locus with more than 78,000 different immune traits.

Highlights

d Resource of heritabilities and genetic associations of 80,000 immune traits in 669 twins

d Genetic associations with immune cell frequencies and surface protein expression levels

d Of the top 150 traits, 11 genetic loci explained up to 36% of variation of 19 traits

d Loci include autoimmune susceptibility genes, providing etiological hypotheses

Roederer et al., 2015, Cell 161, 387?403 April 9, 2015 ?2015 Elsevier Inc.

Resource

The Genetic Architecture of the Human Immune System: A Bioresource for Autoimmunity and Disease Pathogenesis

Mario Roederer,1,7,* Lydia Quaye,2,7 Massimo Mangino,2,4,7 Margaret H. Beddall,1 Yolanda Mahnke,1,5 Pratip Chattopadhyay,1 Isabella Tosi,3,4 Luca Napolitano,3 Manuela Terranova Barberio,3 Cristina Menni,2 Federica Villanova,3,4 Paola Di Meglio,3,6 Tim D. Spector,2,8,* and Frank O. Nestle3,4,8 1ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892, USA 2Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK 3Cutaneous Medicine Unit, St. John's Institute of Dermatology, King's College London, London SE1 9RT, UK 4NIHR Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust, London SE1 9RT, UK 5Present address: Translational and Correlative Sciences Laboratory, Translational Research Program, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, USA 6Present address: Division of Molecular Immunology, MRC National Institute for Medical Research, Mill Hill, London NW1 7AA, UK 7Co-first author 8Co-senior author *Correspondence: roederer@ (M.R.), tim.spector@kcl.ac.uk (T.D.S.)

SUMMARY

Despite recent discoveries of genetic variants associated with autoimmunity and infection, genetic control of the human immune system during homeostasis is poorly understood. We undertook a comprehensive immunophenotyping approach, analyzing 78,000 immune traits in 669 female twins. From the top 151 heritable traits (up to 96% heritable), we used replicated GWAS to obtain 297 SNP associations at 11 genetic loci, explaining up to 36% of the variation of 19 traits. We found multiple associations with canonical traits of all major immune cell subsets and uncovered insights into genetic control for regulatory T cells. This data set also revealed traits associated with loci known to confer autoimmune susceptibility, providing mechanistic hypotheses linking immune traits with the etiology of disease. Our data establish a bioresource that links genetic control elements associated with normal immune traits to common autoimmune and infectious diseases, providing a shortcut to identifying potential mechanisms of immune-related diseases.

INTRODUCTION

The immune system has evolved over millions of years into a remarkable defense mechanism with rapid and specific protection of the host from major environmental threats and pathogens. Such pathogen encounters have contributed to a selection of immune genes at the population level that determine not only host-specific pathogen responses but also susceptibility to autoimmune disease and immunopathogenesis. Understanding how such genes interplay with the environment to determine im-

mune protection and pathology is critical for unravelling the mechanisms of common autoimmune and infectious diseases and future development of vaccines and immunomodulatory therapies.

Studies of rare disease established major genes, and their associated pathways, that regulate pathogen-specific immune responses (Casanova and Abel, 2004) and genome-wide association studies (GWAS) of autoimmune disease have also been productive for finding common variants (Cotsapas and Hafler, 2013; Parkes et al., 2013; Raj et al., 2014). Despite this progress, there are still major limitations in our understanding of the genetics of complex autoimmune or infectious diseases. A key missing piece is the elucidation of the genes controlling critical components of a normal human immune system under homeostatic conditions. These include the relative frequencies of circulating immune cell subsets and the regulation of cell-surface expression of key proteins that we expect have strong regulatory mechanisms.

Previous studies in humans and rodents have shown that variation in the levels of circulating blood T cells is in part heritable (Amadori et al., 1995; Kraal et al., 1983). Identifying the underlying genetic elements would help us understand the mechanisms of homeostasis--and its dysregulation. Twin studies are ideal to quantify the heritability of immune traits in healthy humans by allowing adjustment for the influence of genes, early environment, age, and cohort, plus a number of known and unknown confounders (van Dongen et al., 2012). Early studies from our group demonstrated genetic control of CD8 and CD4 T cell levels in twins (Ahmadi et al., 2001), and others have shown similar heritable effects in non-twins and rodents and with broad white cell phenotypes (Amadori et al., 1995; Clementi et al., 1999; Damoiseaux et al., 1999; Evans et al., 1999; Ferreira et al., 2010; Hall et al., 2000; Kraal et al., 1983; Nalls et al., 2011; Okada et al., 2011). A recent study, with a family design, was the first to perform GWAS on a larger range of immune subtypes. The authors analyzed 272 correlated immune traits derived from 95

Cell 161, 387?403, April 9, 2015 ?2015 Elsevier Inc. 387

A

Stem cells

CD34+Lin

1 CSF 5 SPELs

PBMC

NK Cells

Early NK

CD56+CD16

Effector NK

CD56+CD16+

Terminal NK

CD56 CD16+

Lymphocytes

Panel 4

CD2 CD158a CD158b CD314 CD335 CD337 CCR7

6,651 CSFs 49 SPELs

Cytotoxic Immunomodulatory Activating KIR variants Anergic

T Cells

CD3+

Panel 7

Monocytes

CD14+

CD11c CD16 CD32 CD64 CD123 CD141 CD274 HLA-DR

6,561 CSFs

NKT

CD1d Tetramer+

Dendritic Cells

Myeloid DC

CD11c+ CD123

APC for CD8 APC for CD4 CD1c

CD141+

CD1c+

CD11c+ CD123+

Plasmacytoid DC

CCDD11213c+

CD16 Inflammatory

CD16+

CD32 CD64 CD274

162 CSFs 135 SPELs

Panel 7

T T

CD4 T

Panel 3

Panel 2

Panel 1

CD8 DN DP

T

T

T

V 1+ V 2+ V 2 V 9+ V 9+

CD27 CD28 CD31 CD45RA CD57 CD95 CD127 CD244

CD25 CD38 CD39 CD45RO CD73 CD127 HLA-DR PD-1

RTE

Naive

26,244 CSFs 96 SPELs

TSCM TCM TTM

TEM

TTE

Senescent

Panel 5

CD8 CD27 CD28 CD45RA CCR5 CCR7

Activated Memory 26,244 CSFs Long-lived Memory 96 SPELs Exhausted

Treg

CD161 PD1 CCR4 CCR6 CCR10 CXCR3 CXCR5

8,748 CSFs 84 SPELs

TFH TH1 TH2 TH9 TH17 TH21 TH22

Naive Early Effector Terminal

CD4+ CCR5

CCCDR45++

CD4 CD8

CCDD84+

Panel 5

2,187 CSFs 54 SPELs

CD27 CD28 CD45RA CCR7

324 CSFs 67 SPELs

B Cells

CD19+

Immature B

CD10+

Naive B

1 CSF

CD21+CD95

4 SPELs 1 CSF

3 SPELs

Panel 6

CD95 IgM+

Memory B CD95+

CD5 CD24 CD27 CD38 IgD

IgM+ 243 CSFs

Panel 6

IgG+

CD20 CD24 CD27 CD38

IgA+ IgE+

324 CSFs 88 SPELs

Memory Activated Exhausted Plasmablasts

10 P-Value

B 40 FcGR Cluster 20

10

ENTDP1

C 30 FcGR Cluster 25

ENTDP1 KLR Cluster

FAS SLC18A1

10 P-Value

15

NT5E

NFIA PRKC1

10

NXRN1

FTO

5

0 1 2 3 4 5 6 7 8 9 10 12 14 16 20

Chromosome

0 1 2 3 4 5 6 7 8 9 10 12 14 16 20

Chromosome

Figure 1. Schematic Representation of Leukocyte Populations Analyzed and Summary Manhattan Plot (A) This diagram illustrates the approach to analyzing the immunophenotyping data obtained by flow cytometry. It is not meant to convey differentiation stages of leukocyte populations, though that property is largely reflected in this diagram. Each ``lineage'' of a subset of leukocytes was identified through hierarchical gating. Within each of these lineages, all possible combinations of markers with heterogeneous expression within the lineage were analyzed. The number of subsets identified by this combinatorial approach is shown in various lineages; the trait analyzed was the CSF within its parent lineage. In addition, the cell SPEL was quantified by the median fluorescence intensity of the antibody staining on a given cell subset; the number of SPEL traits is indicated as well.

(legend continued on next page)

388 Cell 161, 387?403, April 9, 2015 ?2015 Elsevier Inc.

cell types and described 23 independent genetic variants within 13 independent loci (Orru` et al., 2013).

Here, we report a comprehensive and high-resolution deep immunophenotyping flow cytometry analysis in 669 female twins using 7 distinct 14-color immunophenotyping panels that captured nearly 80,000 cell types (comprising $1,800 independent phenotypes) to analyze both immune cell subset frequency (CSF) and immune cell-surface protein expression levels (SPELs). This gave us a roughly 30-fold richer view of the healthy immune system than was previously achievable. Taking advantage of the twin model, we used a pre-specified analysis plan that prioritized 151 independent immune traits for genomewide association analysis and replication.

We find 241 genome-wide significant SNPs within 11 genetic loci, 9 of which are previously unreported. Importantly, they explain up to 36% of the variation of 19 immune traits (18 previously unexplored). We identify pleiotropic ``master'' genetic loci controlling multiple immune traits and key immune traits under tight genetic control by multiple genetic loci. In addition, we show the importance of quantifying cell-surface antigen expression rather than just cell-type frequency.

Critically, we show overlap between these genetic associations of normal immune homeostasis with previously established autoimmune and infectious disease associations. This rich database provides a vital, publicly accessible bioresource as a bridge between genetic and immune discoveries that will expedite the identification of disease mechanisms in autoimmunity and infection.

RESULTS

Subjects The discovery stage comprised 497 female participants from the UK Adult Twin Register (TwinsUK). There were 75 complete monozygotic (MZ) twin pairs, 170 dizygotic (DZ) pairs, and 7 singletons (arising from quality control [QC] failures in one co-twin). The mean age was 61.4 years (range: 40?77). The replication stage comprised a further 172 participants, mean age 58.2 years (range: 32?83), with 46 MZ, 118 DZ, and 8 singletons. We stained cryopreserved peripheral blood mononuclear cells (PBMC) from each, using a set of 7 14-color immunophenotyping panels that delineate a large range of immune subsets (Figures 1A, S1, S2, and S3 and Table S1). Immune traits analyzed included the CSF (i.e., the proportionate representation of a given phenotype) and the SPEL (i.e., a quantitative measure of gene expression on a per-cell basis). The variability of all traits was assessed using longitudinal sampling on a small cohort of individuals as described in the Experimental Procedures; of the 50,000 traits meeting the first filter criterion (Figure S4), the mean covariance across samples drawn 6 months apart is 0.86. All trait values and summary analyses, including variability, are available for download. Data and statistical analysis of the discovery stage was completed per a pre-defined statistical

analysis plan before samples from the replication stage were thawed.

GWAS analysis of all 78,000 immune traits is computationally prohibitive and would require a multiple comparisons correction that dramatically reduces sensitivity. The ability to infer heritability (proportion of variance explained solely by genetic factors) by the use of twins dramatically enhanced our ability to focus on those that are most likely to be informative. Co-variation of all traits was computed; about 1,800 were independent at r < 0.7 (Figure S4).

We found no significant association of the analyzed traits with self-reported tobacco use or alcohol consumption and so did not include those behaviors as covariates. We identified many traits associated with age and included age as a covariate in all analyses. Notably, an advantage of using a twin-based cohort is to render age and other cohort effects minimally impactful. The age range of our cohort was optimal for our goal of identifying immune traits associated with genetic elements that show a risk for autoimmune diseases. Because incidence for such diseases often increases with age, the greatest power for such correlations will be obtained using samples measurements most proximal to the common onset of disease.

Heritability Falconer's traditional formula (twice the difference in intraclass correlations) was used to roughly estimate the heritabilities of all 78,000 immune traits; after ranking, traits were selected for further pre-specified analyses (Figure S4). Variance components analysis (additive genetics, common environment, and unique environment, or ACE model) was used to more precisely estimate heritabilities of chosen traits. The heritabilities ranged widely from 0%--suggesting purely environmental or stochastic influences--to 96% (e.g., CD32 expression on dendritic cells), indicating a strong genetic effect. Figure S5 shows the range of heritabilities for selected traits, and the components of the model are tabulated in Table S2 with full trait descriptions.

GWAS of Immune Traits Single-variant associations were performed on 151 immune traits selected for high heritability or biological interest, comprising cell frequency (129 CSFs) and cell-surface protein expression (22 SPELs). Many significant associations were found despite the stringent Bonferroni multiple testing threshold of p < 3.3 3 10?10. We also performed a conditional analysis, including the top SNP of each locus as a covariate, to identify potential independent secondary signals. This analysis did not reveal any significant evidence for additional independent signals.

Six SPELs were significant (Table 1), with the strongest between MFI:516 (CD39 SPEL on CD4 T cells) and the ENTPD1 (CD39 gene) SNP rs7096317 (p = 9.4 3 10?40). Many other variants of ENTPD1 were also associated with this trait (Table S3). Expression of five others (MFI:189, MFI:212, MFI:231, MFI:504, and MFI:552, which include CD27 expression on B and T cell

(B and C) Summary Manhattan plots: green dots, genome-wide significant associations (p < 5 3 10?8). The red line indicates the significance threshold of p < 3.3 3 10?10, which corresponds to the standard genome-wide threshold after further adjustment for 151 independent tests. The variants shown are MAF R 0.1; call rate R 0.9; HWE p value R 1 3 10?8. Shown are separate plots for SPEL associations (B) and CSF

associations (C).

Cell 161, 387?403, April 9, 2015 ?2015 Elsevier Inc. 389

390 Cell 161, 387?403, April 9, 2015 ?2015 Elsevier Inc.

Table 1. Discovery and Replication Results for the Top Significant SNPs at Each Locus for Each Immune Trait

Locus:Genes 1: FCGR2A, FCGR2B, FCRLA 1: FCGR2A, FCGR2B, FCRLA

Trait ID MFI:189 MFI:212

Trait Phenotype CD27 on IgA+ B CD27 on IgG+ B

Marker rs1801274 rs1801274

Chr EA/NEA EAF Beta (SE)

p Value Beta (SE) p Value Beta (SE) p Value

1 A/G

0.49 0.128 (0.02) 6.48E?11 0.07 (0.03) 3.70E?02 0.11 (0.02) 2.8E?11

1 A/G

0.49 0.136 (0.02) 5.38E?12 0.12 (0.03) 1.11E?04 0.13 (0.02) 2.9E?15

1: FCGR2A, FCGR2B, FCRLA

MFI:231 CD161 on CD4 T

rs1801274 1 A/G

0.49 0.131 (0.02) 2.64E?11 0.12 (0.03) 2.17E?04 0.13 (0.02) 2.7E?14

1: FCGR2A, FCGR2B, FCRLA

MFI:504 CD27 on CD4 T

rs1801274 1 A/G

0.49 0.145 (0.02) 5.42E?14 0.14 (0.03) 4.20E?05 0.14 (0.02) 1.1E?17

1: FCGR2A, FCGR2B, FCRLA 1: FCGR2A, FCGR2B, FCRLA 1: FCGR2A, FCGR2B, FCRLA 2: NFIA 3: NRXN1 4: PRKCI 5: NT5E RP11-30P6 6: SLC18A1 7: SLC25A16

MFI:552 CD27 on CD8 T

rs1801274 1 A/G

P7:110 iMDC: %CD32+

rs10494359 1 C/G

P7:224 CD1c+ mDC: %CD32 rs4657090 1 A/G

P4:3551 NK: %CD314?CD158a+ rs12072379 1 G/C

P4:3551 NK: %CD314?CD158a+ rs17040907 2 T/C

P4:3551 NK: %CD314?CD158a+ rs2650220 3 G/A

P2:4195 CD4 T: %CD39-CD73+ rs9444346 6 G/A

P2:4204 CD4 T: %CD73+

rs9444346 6 G/A

P4:3551 NK: %CD314?CD158a+ rs1390942 8 T/C

P4:3551 NK: %CD314?CD158a+ rs3017072 10 T/C

0.49 0.186 (0.02) 1.26E?21 0.12 (0.03) 1.72E?04 0.17 (0.02) 4.2E?24 0.12 0.343 (0.03) 2.52E?29 0.43 (0.05) 1.05E?15 0.36 (0.03) 5.9E?43 0.27 ?0.174 (0.02) 1.30E?14 ?0.19 (0.04) 3.86E?06 ?0.18 (0.02) 2.7E?19 0.16 ?0.131 (0.02) 1.73E?10 ?0.10 (0.05) 4.87E?02 ?0.13 (0.02) 2.7E?11 0.07 ?0.208 (0.03) 2.68E?10 ?0.16 (0.08) 4.22E?02 ?0.02 (0.03) 3.9E?11 0.16 ?0.15 (0.02) 3.18E?10 ?0.10 (0.05) 4.55E?02 ?0.14 (0.02) 6.0E?11 0.19 ?0.2 (0.03) 1.18E?14 ?0.12 (0.04) 4.98E?03 ?0.18 (0.02) 8.8E?16 0.19 ?0.195 (0.03) 5.85E?14 ?0.12 (0.04) 2.82E?03 ?0.18 (0.02) 1.8E?15 0.15 ?0.163 (0.02) 1.39E?15 ?0.20 (0.05) 1.70E?04 ?0.17 (0.02) 1.4E?18 0.15 ?0.153 (0.02) 2.75E?13 ?0.15 (0.05) 2.08E?03 ?0.15 (0.02) 2.2E?15

8: FAS, ACTA2

P1:6601 CD8 T: %TSCM

rs7097572 10 C/T

0.48 ?0.168 (0.02) 8.51E?16 ?0.18 (0.03) 2.72E?06 ?0.17 (0.02) 1.3E?20

9: ALDH18A1, ENTPD1, ENTPD1-AS1, MFI:516 CD39 on CD4 T

rs7096317 10 G/A

0.42 ?0.255 (0.02) 9.40E?40 ?0.30 (0.04) 9.92E?17 ?0.27 (0.02) 1.6E?54

RP11-7D5, SORBS1, TCTN3

9: ALDH18A1, ENTPD1, ENTPD1-AS1, P2:10491 CD8 T: %CD39+

rs4074424 10 G/C

0.42 ?0.219 (0.02) 4.11E?27 ?0.19 (0.04) 2.40E?07 ?0.21 (0.02) 8.2E?33

RP11-7D5, SORBS1, TCTN3

9: ALDH18A1, ENTPD1, ENTPD1-AS1, P2:3460 CD4 T:%CD39+CD38+ rs4582902 10 C/T

RP11-7D5, SORBS1, TCTN3

PD1?

0.47 ?0.164 (0.02) 4.55E?16 ?0.19 (0.03) 2.58E?08 ?0.17 (0.02) 9.2E?23

9: ALDH18A1, ENTPD1, ENTPD1-AS1, P2:4159 CD4 T:%CD39+CD73? rs6584027 10 G/A

0.47 ?0.212 (0.02) 1.54E?25 ?0.20 (0.04) 1.09E?08 ?0.21 (0.02) 1.1E?32

RP11-7D5, SORBS1, TCTN3

9: ALDH18A1, ENTPD1, ENTPD1-AS1, P2:4186 CD4 T:%CD39+

rs6584027 10 G/A

0.47 ?0.215 (0.02) 2.20E?26 ?0.21 (0.04) 5.27E?09 ?0.21 (0.02) 7.8E?34

RP11-7D5, SORBS1, TCTN3

9: ALDH18A1, ENTPD1, ENTPD1-AS1, P2:4213 CD4 T:%CD39+CD73+ rs10882676 10 A/C

0.47 ?0.195 (0.02) 6.76E?22 ?0.19 (0.04) 2.19E?07 ?0.19 (0.02) 9.0E?28

RP11-7D5, SORBS1, TCTN3

10: KLRC1, KLRC2, KLRC4, KLRK1, P4:3551 NK: %CD314?CD158a+ rs2734565 12 C/T

0.3 ?0.144 (0.02) 1.34E?10 ?0.18 (0.04) 1.67E?05 ?0.15 (0.02) 1.4E?14

RP11-277P12

10: KLRC1, KLRC2, KLRC4, KLRK1, P4:4832 NK: %CD314?CCR7? rs2734565 12 C/T

0.3 ?0.233 (0.02) 1.27E?24 ?0.33 (0.04) 3.95E?15 ?0.26 (0.02) 2.7E?37

RP11-277P12

10: KLRC1, KLRC2, KLRC4, KLRK1, P4:5538 NK: %CD314?CD335+ rs2734565 12 C/T

0.3 ?0.275 (0.02) 3.40E?34 ?0.38 (0.04) 2.27E?19 ?0.30 (0.02) 6.4E?51

RP11-277P12

11: FTO

P4:3551 NK: %CD314?CD158a+ rs1420318 16 A/G

0.1 ?0.146 (0.02) 9.34E?11 ?0.19 (0.06) 4.05E?02 ?0.14 (0.02) 1.2E?11

For the Discovery stage, we used a significance threshold of p < 3.3 3 10?10. This threshold corresponds to the standard genome-wide threshold of 5 3 10?8 after further adjustment for 151 independent tests. Orru` et al. (2013) also identified Locus 1 (associated with a single trait, CD62L? dendritic cells, not measured in our panel), and Locus 9 (associated with a single trait: CD39+

CD4 T cell frequency, P2:4186 in our list). The trait ID is fully described in Table S2.

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