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Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
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MedLine Citation:
PMID:  23143594     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
To gain further insight into the genetic architecture of psoriasis, we conducted a meta-analysis of 3 genome-wide association studies (GWAS) and 2 independent data sets genotyped on the Immunochip, including 10,588 cases and 22,806 controls. We identified 15 new susceptibility loci, increasing to 36 the number associated with psoriasis in European individuals. We also identified, using conditional analyses, five independent signals within previously known loci. The newly identified loci shared with other autoimmune diseases include candidate genes with roles in regulating T-cell function (such as RUNX3, TAGAP and STAT3). Notably, they included candidate genes whose products are involved in innate host defense, including interferon-mediated antiviral responses (DDX58), macrophage activation (ZC3H12C) and nuclear factor (NF)-κB signaling (CARD14 and CARM1). These results portend a better understanding of shared and distinctive genetic determinants of immune-mediated inflammatory disorders and emphasize the importance of the skin in innate and acquired host defense.
Authors:
Lam C Tsoi; Sarah L Spain; Jo Knight; Eva Ellinghaus; Philip E Stuart; Francesca Capon; Jun Ding; Yanming Li; Trilokraj Tejasvi; Johann E Gudjonsson; Hyun M Kang; Michael H Allen; Ross McManus; Giuseppe Novelli; Lena Samuelsson; Joost Schalkwijk; Mona Ståhle; A David Burden; Catherine H Smith; Michael J Cork; Xavier Estivill; Anne M Bowcock; Gerald G Krueger; Wolfgang Weger; Jane Worthington; Rachid Tazi-Ahnini; Frank O Nestle; Adrian Hayday; Per Hoffmann; Juliane Winkelmann; Cisca Wijmenga; Cordelia Langford; Sarah Edkins; Robert Andrews; Hannah Blackburn; Amy Strange; Gavin Band; Richard D Pearson; Damjan Vukcevic; Chris C A Spencer; Panos Deloukas; Ulrich Mrowietz; Stefan Schreiber; Stephan Weidinger; Sulev Koks; Külli Kingo; Tonu Esko; Andres Metspalu; Henry W Lim; John J Voorhees; Michael Weichenthal; H Erich Wichmann; Vinod Chandran; Cheryl F Rosen; Proton Rahman; Dafna D Gladman; Christopher E M Griffiths; Andre Reis; Juha Kere; ; ; ; ; Rajan P Nair; Andre Franke; Jonathan N W N Barker; Goncalo R Abecasis; James T Elder; Richard C Trembath
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Publication Detail:
Type:  Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-11-11
Journal Detail:
Title:  Nature genetics     Volume:  44     ISSN:  1546-1718     ISO Abbreviation:  Nat. Genet.     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-29     Completed Date:  2013-02-13     Revised Date:  2014-06-10    
Medline Journal Info:
Nlm Unique ID:  9216904     Medline TA:  Nat Genet     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1341-8     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
CARD Signaling Adaptor Proteins / genetics,  immunology
Core Binding Factor Alpha 3 Subunit / genetics,  immunology
DEAD-box RNA Helicases / genetics,  immunology
European Continental Ancestry Group / genetics
GTPase-Activating Proteins / genetics,  immunology
Genetic Loci*
Genetic Predisposition to Disease*
Genome-Wide Association Study
Guanylate Cyclase / genetics,  immunology
Humans
Immunity, Innate / genetics*
Membrane Proteins / genetics,  immunology
Oligonucleotide Array Sequence Analysis
Polymorphism, Single Nucleotide
Psoriasis / genetics*,  immunology
STAT3 Transcription Factor / genetics,  immunology
Skin / immunology
T-Lymphocytes / immunology
Grant Support
ID/Acronym/Agency:
068545/Z/02//Wellcome Trust; 083948/Z/07/Z//Wellcome Trust; 085475/B/08/Z//Wellcome Trust; 085780//Wellcome Trust; 090532//Wellcome Trust; 098439//Wellcome Trust; 17552//Arthritis Research UK; AR054966/AR/NIAMS NIH HHS; G0000934//Medical Research Council; G0601387//Medical Research Council; G060I1387//Medical Research Council; G0700314//Medical Research Council; HG007022/HG/NHGRI NIH HHS; K08 AR057763/AR/NIAMS NIH HHS; K08 AR060802/AR/NIAMS NIH HHS; MR/J006742/1//Medical Research Council; R01 AR042742/AR/NIAMS NIH HHS; R01 AR042742/AR/NIAMS NIH HHS; R01 AR050266/AR/NIAMS NIH HHS; R01 AR050266/AR/NIAMS NIH HHS; R01 AR050511/AR/NIAMS NIH HHS; R01 AR050511/AR/NIAMS NIH HHS; R01 AR054966/AR/NIAMS NIH HHS; RC1 AR058315/AR/NIAMS NIH HHS; //Canadian Institutes of Health Research
Chemical
Reg. No./Substance:
0/CARD Signaling Adaptor Proteins; 0/Core Binding Factor Alpha 3 Subunit; 0/GTPase-Activating Proteins; 0/Membrane Proteins; 0/Runx3 protein, human; 0/STAT3 Transcription Factor; 0/STAT3 protein, human; 0/TAGAP protein, human; EC 3.6.1.-/DDX58 protein, human; EC 3.6.4.13/DEAD-box RNA Helicases; EC 4.6.1.-/CARD14 protein, human; EC 4.6.1.2/CARD11 protein, human; EC 4.6.1.2/Guanylate Cyclase
Investigator
Investigator/Affiliation:
Kristina Callis Duffin / ; Cindy Helms / ; David Goldgar / ; Yun Li / ; Justin Paschall / ; Mary J Malloy / ; Clive R Pullinger / ; John P Kane / ; Jennifer Gardner / ; Amy Perlmutter / ; Andrew Miner / ; Bing Jian Feng / ; Ravi Hiremagalore / ; Robert W Ike / ; Enno Christophers / ; Tilo Henseler / ; Andreas Ruether / ; Steven J Schrodi / ; Sampath Prahalad / ; Stephen L Guthery / ; Judith Fischer / ; Wilson Liao / ; Pui Kwok / ; Alan Menter / ; G Mark Lathrop / ; C Wise / ; Ann B Begovich / ; Alexandros Onoufriadis / ; Michael E Weale / ; Angelika Hofer / ; Wolfgang Salmhofer / ; Peter Wolf / ; Kati Kainu / ; Ulpu Saarialho-Kere / ; Sari Suomela / ; Petra Badorf / ; Ulrike Hüffmeier / ; Werner Kurrat / ; Wolfgang Küster / ; Jesús Lascorz / ; Rotraut Mössner / ; Funda Schürmeier-Horst / ; Markward Ständer / ; Heiko Traupe / ; Judith G M Bergboer / ; Martin den Heijer / ; Peter C van de Kerkhof / ; Patrick L J M Zeeuwen / ; Louise Barnes / ; Linda E Campbell / ; Caitriona Cusack / ; Ciara Coleman / ; Judith Conroy / ; Sean Ennis / ; Oliver Fitzgerald / ; Phil Gallagher / ; Alan D Irvine / ; Brian Kirby / ; Trevor Markham / ; W H Irwin McLean / ; Joe McPartlin / ; Sarah F Rogers / ; Anthony W Ryan / ; Agnieszka Zawirska / ; Emiliano Giardina / ; Tiziana Lepre / ; Carlo Perricone / ; Gemma Martín-Ezquerra / ; Ramon M Pujol / ; Eva Riveira-Munoz / ; Annica Inerot / ; Åsa T Naluai / ; Lotus Mallbris / ; Katarina Wolk / ; Joyce Leman / ; Anne Barton / ; Richard B Warren / ; Helen S Young / ; Isis Ricano-Ponce / ; Gosia Trynka / ; Fawnda J Pellett / ; Andrew Henschel / ; Marin Aurand / ; Bruce Bebo / ; Christian Gieger / ; Thomas Illig / ; Susanne Moebus / ; Karl-Heinz Jöckel / ; Raimund Erbel / ; Peter Donnelly / ; Leena Peltonen / ; Jenefer M Blackwell / ; Elvira Bramon / ; Matthew A Brown / ; Juan P Casas / ; Aiden Corvin / ; Nicholas Craddock / ; Audrey Duncanson / ; Janusz Jankowski / ; Hugh S Markus / ; Christopher G Mathew / ; Mark I McCarthy / ; Colin N A Palmer / ; Robert Plomin / ; Anna Rautanen / ; Stephen J Sawcer / ; Nilesh Samani / ; Ananth C Viswanathan / ; Nicholas W Wood / ; Céline Bellenguez / ; Colin Freeman / ; Garrett Hellenthal / ; Eleni Giannoulatou / ; Matti Pirinen / ; Zhan Su / ; Sarah E Hunt / ; Rhian Gwilliam / ; Suzannah J Bumpstead / ; Serge Dronov / ; Matthew Gillman / ; Emma Gray / ; Naomi Hammond / ; Alagurevathi Jayakumar / ; Owen T McCann / ; Jennifer Liddle / ; Marc L Perez / ; Simon C Potter / ; Radhi Ravindrarajah / ; Michelle Ricketts / ; Matthew Waller / ; Paul Weston / ; Sara Widaa / ; Pamela Whittaker /
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Journal Information
Journal ID (nlm-journal-id): 9216904
Journal ID (pubmed-jr-id): 2419
Journal ID (nlm-ta): Nat Genet
Journal ID (iso-abbrev): Nat. Genet.
ISSN: 1061-4036
ISSN: 1546-1718
Article Information
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nihms-submitted publication date: Day: 19 Month: 10 Year: 2012
Electronic publication date: Day: 11 Month: 11 Year: 2012
Print publication date: Month: 12 Year: 2012
pmc-release publication date: Day: 01 Month: 6 Year: 2013
Volume: 44 Issue: 12
First Page: 1341 Last Page: 1348
PubMed Id: 23143594
ID: 3510312
DOI: 10.1038/ng.2467
ID: NIHMS416128

Identification of fifteen new psoriasis susceptibility loci highlights the role of innate immunity
Lam C Tsoi1*
Sarah L Spain2*
Jo Knight34*
Eva Ellinghaus5*
Philip E Stuart6
Francesca Capon2
Jun Ding1
Yanming Li1
Trilokraj Tejasvi6
Johann E. Gudjonsson6
Hyun M Kang1
Michael H Allen2
Ross McManus78
Giuseppe Novelli910
Lena Samuelsson11
Joost Schalkwijk12
Mona Ståhle13
A. David Burden14
Catherine H Smith15
Michael J Cork16
Xavier Estivill17
Anne M Bowcock18
Gerald G. Krueger19
Wolfgang Weger20
Jane Worthington21
Rachid Tazi-Ahnini16
Frank O Nestle2
Adrian Hayday22
Per Hoffmann2324
Juliane Winkelmann252627
Cisca Wijmenga28
Cordelia Langford29
Sarah Edkins29
Robert Andrews29
Hannah Blackburn29
Amy Strange30
Gavin Band30
Richard D Pearson30
Damjan Vukcevic30
Chris CA Spencer30
Panos Deloukas29
Ulrich Mrowietz31
Stefan Schreiber53233
Stephan Weidinger31
Sulev Koks34
Külli Kingo35
Tonu Esko36
Andres Metspalu36
Henry W Lim37
John J Voorhees6
Michael Weichenthal31
H. Erich Wichmann383940
Vinod Chandran41
Cheryl F Rosen42
Proton Rahman43
Dafna D Gladman41
Christopher EM Griffiths44
Andre Reis45
Juha Kere464748
Collaborative Association Study of Psoriasis
Genetic Analysis of Psoriasis Consortium
Psoriasis Association Genetics Extension
Wellcome Trust Case Control Consortium 2
Rajan P Nair6
Andre Franke5
Jonathan NWN Barker215
Goncalo R Abecasis1
James T Elder650
Richard C Trembath251
1Department of Biostatistics, Center for Statistical Genetics, University of Michigan Ann Arbor, MI 48109, USA
2Division of Genetics and Molecular Medicine, King’s College London, London, UK
3Neuroscience Research, Centre for Addiction and Mental Health, Toronto, ON, Canada M5T 1R8
4National Institute for Health Research (NIHR), Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust
5Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24105 Kiel, Germany
6Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
7Department of Clinical Medicine Trinity College Dublin, Ireland
8Institute of Molecular Medicine, Trinity College Dublin, Ireland
9National Agency for Evaluation of Universities and Research Institutes (ANVUR)
10Research Center San Pietro Hospital, Rome, Italy
11Department of Medical and Clinical Genetics, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
12Department of Dermatology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
13Dermatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
14Department of Dermatology, Western Infirmary, Glasgow, UK
15St John’s Institute of Dermatology, King’s College London, London, UK
16Academic Unit of Dermatology Research, Department of Infection and Immunity, The University of Sheffield, Sheffield, UK
17Genes and Disease Programme, Centre for Genomic Regulation (CRG) and UPF, Hospital del Mar Research Institute (CRG) and Public Health and Epidemiology Network Biomedical Research Centre (CIBERESP), Barcelona, Spain
18Division of Human Genetics, Department of Genetics, Washington University School of Medicine, St. Louis, MO
19Department of Dermatology, University of Utah, Salt Lake City, UT
20Department of Dermatology, Medical University of Graz, Graz, Austria
21Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
22Division of Immunology, Infection and Inflammatory Disease; King’s College London, London, UK
23Institute of Human Genetics, University of Bonn, 54127 Bonn, Germany
24Department of Genomics, Life & Brain Center, University of Bonn, 54127 Bonn, Germany
25Department of Neurology, Technische Universität München, Munich, Germany
26Institute of Human Genetics, Technische Universität München, Munich, Germany
27Institute of Human Genetics, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Munich, Germany
28Genetics Department, University Medical Center and University of Groningen, Groningen, The Netherlands
29Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
30Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7LJ, UK
31Department of Dermatology, University Hospital, Schleswig-Holstein, Christian-Albrechts-University, 24105 Kiel, Germany
32Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24105 Kiel, Germany
33PopGen biobank, University Hospital S.-H., Kiel, Germany
34Department of Physiology, Centre of Translational Medicine and Centre for Translational Genomics, University of Tartu, 50409 Tartu, Estonia
35Department of Dermatology and Venerology, University of Tartu, 50409 Tartu, Estonia
36Estonian Genome Center, University of Tartu, 51010 Tartu, Estonia
37Department of Dermatology, Henry Ford Hospital, Detroit, MI, 48202, USA
38Institute of Epidemiology I, Helmholtz Centre Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany
39Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University, 81377 Munich, Germany
40Klinikum Grosshadern, 81377 Munich, Germany
41Department of Medicine, Division of Rheumatology, University of Toronto, Toronto Western Hospital, Toronto, Ontario M5T 2S8, Canada
42Department of Medicine, Division of Dermatology, University of Toronto, Toronto Western Hospital, Toronto, Ontario M5T 2S8
43Department of Medicine, Memorial University, St. John’s, Newfoundland A1C 5B8, Canada
44Dermatological Sciences, Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
45Institute of Human Genetics, University of Erlangen-Nuremberg, Erlangen, Germany
46Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
47Folkhälsan Institute of Genetics, Helsinki, Finland
48Department of Medical Genetics, University of Helsinki, Finland
50Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, 48105, USA
51Queen Mary University of London, Barts and the London School of Medicine and Dentistry, London, UK
Corresponding authors: Richard C. Trembath, Division of Genetics and Molecular Medicine, King’s College London School of Medicine, Guy’s Hospital, London SE1 9RT. UK; Queen Mary University of London, Barts and the London School of Medicine and Dentistry, London E1 2AD, UK, vp-health@qmul.ac.uk. Goncalo R. Abecasis, Department of Biostatistics, School of Public Health M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029, USA, phone (734) 763-4901, goncalo@umich.edu. James T. Elder, 7412 Medical Sciences Building 1, University of Michigan Medical School, 1301 E. Catherine, Ann Arbor, Michigan 48109-5675, USA, phone (734) 647-8070, jelder@umich.edu
*These authors contributed equally to this work
49A list of members and affiliations appears in the Supplementary Note

Psoriasis is a chronic, potentially disfiguring, immune-mediated inflammatory disease of the skin with a prevalence of 0.2 to 2%, depending on the population of origin. About one-quarter of psoriatics develop a painful and debilitating arthritis, and there is increasing awareness of co-morbidities, including metabolic syndrome and cardiovascular disease1,2. Current evidence suggests that a dysregulated cutaneous immune response characterized by tumor necrosis factor-α (TNF) dependence and exaggerated Th1 and Th17 activation occurs in genetically susceptible individuals1,2. Recent large-scale association studies have identified 26 loci that are associated with psoriasis310, 21 of which show association in Caucasians36,10. Several of these signals overlap with other autoimmune diseases (e.g. Crohn’s disease, ankylosing spondylitis, and celiac disease), particularly those near genes involved in Th17 differentiation and IL-17 responsiveness (e.g. IL23R, IL12B, IL23A, TRAF3IP2)11. To accelerate our understanding of the genetic architecture of this disease, we helped design a custom single-nucleotide polymorphism (SNP) array (the “Immunochip”). The aims of the Immunochip are to fine-map genome-wide significant (i.e. P<5×10−8) susceptibility loci and to explore replication of thousands of SNPs representing additional promising signals12,13. In this study, we use Immunochip data to identify new genetic determinants of psoriasis, and to relate them to other autoimmune disorders.

We combined three existing GWAS datasets (hereafter referred to as Kiel3, CASP4 and WTCCC25) with two independent European-descent case-control datasets genotyped on the Immunochip: the Psoriasis Association Genetics Extension (PAGE: 3,580 cases and 5,902 controls) and the Genetic Analysis of Psoriasis Consortium (GAPC: 2,997 cases and 9,183 controls) (datasets are described in Supplementary Tables 1 and 2). After quality control, the combined dataset consisted of 10,588 patients with psoriasis and 22,806 healthy controls. For each GWAS, we increased the SNP density through imputation by using European haplotype sequences generated by the 1000 Genomes Project as templates (20100804 release). Overall, our analysis includes 111,236 SNPs that were genotyped in both Immunochip datasets and also had good imputation quality in at least two of the three GWAS (see Online Methods).

Meta-analysis of all five datasets yielded genome-wide significance for 19 of the 21 known psoriasis loci (Supplementary Fig 1, Table 1, Supplementary Table 3). We found nominal evidence for the remaining two loci in the combined analysis (ZMIZ1 and PRDX5, each with P < 3×10−6) as well as nominal evidence for all loci in separate analyses including only GWAS (all with P < 5×10−3) or Immunochip data (all with P < 4×10−4). In addition, we identified 15 new risk loci at P < 5×10−8 (Supplementary Fig 1, Table 1, and Supplementary Table 3). Nine of the new signals were submitted, during design of the Immunochip, as genome-wide significant Immunochip loci by at least one other disease consortium (see “Disease Overlap” column in Supplementary Table 4), though we also submitted three of these (rs11121129, rs10865331, and rs9504361) based on a preliminary meta-analysis of our GWAS datasets. Notably, of the remaining six signals, four were submitted as genome-wide significant loci for psoriasis (SNPs rs11795343, rs4561177, rs11652075, and rs545979). The strongest new association was observed for rs892085 at 19p13.2, near the ILF3 and CARM1 genes (combined Pvalue (Pcomb) = 3.0 × 10−17; OR = 1.17). Despite its proximity (< 500kb) to TYK2, conditional analysis demonstrated that this is an independent signal (Supplementary Table 5). Other associated loci included 1p36.11 near RUNX3; 6p25.3 near EXOC2 and IRF4; 9p21.1 near DDX58; 11q22.3 near ZC3H12C, 11q24.3 in the ETS1 gene and 17q21.2 near STAT3, STAT5A and STAT5B. Box 1 summarizes the functional characteristics of notable genes from the newly identified loci, and the regional association plots are shown in Supplementary Fig 2.

Box 1  The function of notable genes in the regions of newly identified associations
RERE, SLC45A1, ERRFI1, TNFRSF9 (1p36.23)

This signal falls between the RERE, SLC45A1, ERRFI1, and TNFRSF9 genes. RERE encodes an arginine-glutamic acid dipeptide repeat-containing protein that controls retinoic acid signalling38. ERRFI1 encodes a feedback inhibitor of the EGF receptor39. SLC45A1 encodes a solute carrier protein that mediates the uptake of glucose40. The TNFRSF9 gene encodes a co-stimulatory molecule that has a role in generation of memory CD8+ T-cells.

RUNX3 (1p36.11)

RUNX3 is a member of the Runt domain-containing family of transcription factors and has an essential role in T-cell biology, particularly in the generation of CD8+ cells. RUNX3 also has a role in promoting Th1 differentiation through binding with T-bet41.

B3GNT2 (2p15)

B3GNT2 is a member of the beta-1,3-N-acetylglucosaminyl transferase family. It catalyzes the initiation and elongation of poly-N-acetyllactosamine chains42. Deficiency has shown to results in hyperactivation of lymphocytes43.

EXOC2, IRF4 (6p25.3)

EXOC2 encodes a component of the multi-protein complex which mediates the docking of exocytic vesicles to the plasma membrane44. IRF4 encodes a transcription factor that regulates IL17A promoter activity and controls RORyt-dependent Th17 colitis in vivo45,46. IRF4 also plays a role in stabilization of the Th17 phenotype through IL-2147 and may regulate CD4/CD8 differentiation through regulation of RUNX3 expression48.

TAGAP (6q25.3)

This gene is a Rho-GTPase activating protein that is involved in T-cell activation49.

ELMO1 (7p14.2-7p14.1)

ELMO1 is a member of the engulfment and cell motility protein family, which binds to DOCK2, and is essential for TLR7- and TLR9-mediated IFNinduction by plasmacytoid dendritic cells50 and plasmacytoid dendritic cell migration.51 DOCK2 also has a role in antigen-uptake and presentation, and lymphocyte trafficking51.

DDX58 (9p21.1)

DDX58 encodes the RIG-I innate antiviral receptor, which recognizes cytosolic double-stranded RNA.52 It is induced by IFN-γ53 and regulates type I and type II IFN production54.

KLF4 (9p31.2)

KLF4 is a Kruppel-like transcription factor, which is required for the establishment of skin barrier function55 and regulates key signaling pathways related to macrophage activation56. KLF4 also binds to the promoter of IL17A and positively regulates its expression.

ZC3H12C (11q22.3)

Zinc-finger protein regulating macrophage activation57.

ETS1 (11q24.3)

Transcription factor activated downstream of the Ras-MAPK pathway, involved in homeostasis of squamous epithelia58. Involved in CD8 lineage differentiation and acts in part by promoting RUNX3 expression59. Negative regulator of Th17 differentiation60

SOCS1 (16p13.13)

SOCS1 is a member of the suppressor of cytokine signalling family of proteins and inhibits signalling events downstream of IFN-γ61. It regulates Th17 differentiation by maintaining STAT3 transcriptional activity62 and interacts with TYK2 in cytokine signalling63.

STAT3 , STAT5A/5B (17q21.2)

STAT3 and STAT5A/5B are members of the STAT family of transcription activators. STAT3 participates in signalling downstream of multiple cytokines implicated in psoriasis such as IL-6, IL-10, IL-20, IL-22 and IL-23 and may have a role in mediating the innate immune response in psoriatic epidermis64. STAT3 is required for the differentiation of Th17 cells65. STAT5A/5B participate in signalling downstream of the IL-2 family of cytokines, including IL-2, IL-7, IL-15 and IL-21. Both proteins contribute to the development of Treg cells and inhibit the differentiation of Th17 cells66.

CARD14 (17q25.3)

Member of a family of Caspase Recruitment Domain containing scaffold proteins, known as CARD- and membrane-associated guanylate kinase-like domain-containing protein (CARMA). CARD14/CARMA2 is primarily expressed in epithelial tissues and mediates recruitment and activation of the NF-κB pathway67.

MBD2,POLI ,STARD6 (18q21.2)

MBD2 is a transcriptional repressor that binds to methylated DNA and has a role in the generation of memory CD8+ T-cells68. POLI is an error-prone DNA polymerase, which contributes to the hyper-mutation of immunoglobulin genes69. Sterol transport is mediated by vesicles or by soluble protein carriers, such as steroidogenic acute regulatory protein (STAR; MIM 600617). STAR is homologous to a family of proteins containing a 200- to 210-amino acid STAR-related lipid transfer (START) domain, including STARD6.

ILF3, CARM1 (19p13.2)

ILF3 encodes a double-stranded RNA (dsRNA) binding protein that complexes with other proteins, dsRNAs, small noncoding RNAs, and mRNAs to regulate gene expression and stabilize mRNAs. It is a subunit of the nuclear factor of activated T-cells (NFAT); a transcription factor required for T-cell expression of IL-2. CARM1 is a transcriptional coactivator of NF-κB and functions as a promoter-specific regulatory of NF-κB recruitment to chromatin.

To identify independent secondary signals, we performed conditional analysis using as covariates the strongest signals from the 34 loci achieving genome-wide significance in this study. We identified secondary signals in five loci: 2q24.2, 5q15, 5q33.3, 6p21.33, and 19q13.2 (Supplementary Figs. 3 and 4, Supplementary Tables 6 and 7). The strongest signal from the conditional analysis maps to the MHC region near the MICA gene (rs13437088: P=3.1 × 10−40; OR = 1.32), in agreement with a previous conditional analysis14. The 5q15 conditional signal is in the ERAP2 gene (rs2910686: P = 2.0 × 10−8), which did not show any evidence of association in the unconditional analysis (P = 0.46). Further investigation revealed that the risk-increasing alleles at ERAP1 and the risk-decreasing alleles at ERAP2 preferentially appear on the same haplotype, and the signal near ERAP2 is thus masked by ERAP1 prior to conditional analysis (Supplementary Note). The strongest conditional signal in the 19q13.2 region was rs12720356 in the TYK2 gene (OR=1.25, MAFcontrols=0.09, P = 3.2 × 10−10). The association of this SNP with psoriasis has been previously reported5 and is independent of the strongest TYK2 signal identified by our meta-analysis (rs34536443, OR=1.88, MAFcases=0.03, P = 1.5 × 10−39). As rs34536443 was a low-frequency imputed SNP and manifested the highest effect size outside of the MHC, we directly genotyped this SNP in 3,390 independent Michigan samples (1,844 cases and 1,546 controls), robustly replicating the association (OR = 2.80, MAFcases= 0.02, P = 7.8 × 10−14) and experimentally confirming the validity of our imputation procedures.

We next tested for statistical interaction among the top SNPs in the 34 significant loci (Supplementary Note; Supplementary Table 8). We identified two significant pairwise interactions after correction for multiple testing (P < 5 × 10−5): HLA-C (rs4406273)-LCE (rs6677595) and HLA-C (rs4406273)-ERAP1 (rs27432). These interactions confirm results of previous studies5,15,16.

In order to identify potential causal alleles in coding sequence, we looked for missense variants in tight LD (r2>0.9 in 1000 Genomes Project European samples) with the lead SNPs from each of the 34 identified loci (Table 1 and Supplementary Table 6). We found 10 potentially causal SNPs (Table 2), nine of which were included in our meta-analysis. For the known loci near TRAF3IP2 and TYK2, damaging non-synonymous substitutions were themselves the index SNPs in our initial and conditional analyses. Among the newly identified loci, the index SNP from CARD14, a gene that harbors Mendelian variants predisposing to psoriasis17, was also a common and damaging variant as has been described elsewhere18. For the remaining loci, we could account for essentially all index SNP signals by conditioning on nearby missense SNPs, consistent with the possibility that they are causal. Notable non-synonymous variants include the protective c.R381Q polymorphism in IL23R19; a SNP in the PRSS53 gene20, which is also the most highly over-expressed gene in psoriatic skin in this locus6; and a variant in YDJC that also increases risk for celiac disease21, rheumatoid arthritis22 and Crohn’s disease23.

Utilizing the results of a large-scale study of gene expression in psoriatic vs. normal skin24 , we found 14 up-regulated genes (IL12RB2, LCE3D, REL, PUS10, CDSN, PRSS53, PRSS8, NOS2, DDX58, ZC3H12C, SOCS1, STAT3, CARD14, IFIH1) and 4 down-regulated genes (MICA, RNF114, PTRF, POLI) in the 34 associated regions (FDR<0.05 and fold-change>1.5 or <0.67; Supplementary Table 9). The number of differentially expressed genes in psoriasis susceptibility loci was not greater than expected by chance (P=0.39). None of the 34 top SNPs met the Bonferroni corrected (P < 1×10−7) threshold as expression quantitative trait loci (eQTL) in skin tissue, as assessed by microarray analysis of mRNA levels25. However, rs2910686, one of the five SNPs identified by conditional analysis, was a cis-eQTL for ERAP2 in both normal and psoriatic skin (see Supplementary Note for details). Genetic control of ERAP2 expression has been noted previously26,27 and has been suggested as a determinant of balancing selection at this locus28.

This study increases the number of psoriasis-associated regions in European ancestry samples to 36, with conditional analysis increasing the number of independent signals to 41. The 39 independent signals with P < 5×10−8 in the current study collectively account for 14.3% of the total variance in psoriasis risk, or approximately 22% of its estimated heritability29 (see Supplementary Table 10 for details), indicating that further genetic studies, including fine mapping studies and searches for uncommon susceptibility variants are in order.

Sharing of susceptibility loci between autoimmune diseases has been demonstrated previously11 and we find similar patterns in this study. Notably, ten of the psoriasis susceptibility loci reported here overlap with Crohn’s disease and ten others with celiac disease, two diseases that are enriched in individuals with psoriasis30,31 (Supplementary Table 4; illustrated in Supplementary Fig. 5). We caution that the statistical significance of these overlaps is hard to assess, given the ongoing process of gene discovery for many autoimmune disorders and biases in the list of SNPs evaluated for association in this experiment.

As the primary interface with the external environment, the skin provides a critical first line of host defense to microbial pathogens. Consistent with this function, it possesses a diverse and well-conserved set of innate immune mechanisms32,33, which emerged long before the development of adaptive immunity34. In this context, we found it interesting that five of the six newly identified loci that are thus far uniquely associated with psoriasis are involved in innate immune responses (DDX58, KLF4, ZC3H12C, CARD14 and CARM1, Supplementary Table 4 and Box 1). Among all confirmed psoriasis susceptibility loci, 11 out of 14 psoriasis specific loci (the five listed above along with IL28RA, LCE3D, NOS2, FBXL19, NFKBIA and RNF114) encode plausible regulators of innate host defense1,2,35. Conversely, only 6 out of 20 loci shared with other autoimmune diseases contain genes (REL, IFIH1, TNIP1, TNFAIP3, IRF4 and ELMO1) that contribute to innate immunity. These provisional comparisons further illustrate the insights that can be gained by developing and comparing complete and well-annotated sets of risk loci for autoimmune disorders.

The known and newly identified psoriasis susceptibility loci implicated by this study encode several proteins engaged in the TNF, IL-23, and IL17 signaling pathways targeted by highly effective biologic therapies36. Interestingly, our strongest non-MHC signal directly implicates TYK2, a druggable target that contributes to several autoimmune diseases. Agents targeting the closely related JAK kinases are showing encouraging results in clinical trials37. Our findings will help prioritize and interpret the results of sequencing and gene expression studies. Further genomic studies will allow us to identify the underlying causal variants within psoriasis susceptibility loci and lead to increased understanding of pathogenetic mechanisms and new therapeutic targets.


Online Methods
Sample Collections

The samples used in the 3 GWAS data sets (Kiel, CASP and WTCCC2) were previously described35. Samples of the Psoriasis/Arthritis Genetics Extension (PAGE) and the Genetic Analysis of Psoriasis Consortium (GAPC) datasets (Supplementary Table 1 and 2) were collected from subjects of European Caucasian descent at the participating institutions after obtaining informed consent in adherence with the Declaration of Helsinki Principles. DNA was isolated from blood or EBV-immortalized lymphoblastoid cell lines using standard methods.

The collections used in the GAPC and PAGE ImmunoChip studies are described in Supplementary Table 2.

The samples from GAPC substantially overlapped with those described as replication datasets in Strange et al. 20105. All cases had been diagnosed as having psoriasis vulgaris. The GAPC cases and the Irish and Spanish controls were genotyped at the Wellcome Trust Sanger Institute (WTSI) and all samples were provided by the relevant groups given in Supplementary Table 2 and listed in the GAP consortium members list (Supplementary Note 2). The UK controls were the WTCCC common controls that did not overlap with samples included in the original GWA studies (the dataset consisted of 6,740 1958 British Birth cohort and 2,900 UK Blood Service samples genotyped at the WTSI and the University of Virginia). The German controls were obtained from the PopGen biobank and genotyped at the Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel. The Finland control data were from the DILGOM (Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome) collection70. The Irish controls were provided by the Irish Blood Transfusion Service / TCD Biobank and the Irish cases collected with the aid of the Dublin Centre for Clinical Research. We did not include specific controls from Austria or Sweden, but PCA analysis suggested that the cases from these cohorts were well matched to the controls from the Netherlands and Germany.

For the PAGE Immunochip study, samples also substantially overlapped with previously published replication datasets. The German cases (described as a replication dataset in Ellinghaus et al. 20103), all samples from the United States and Canada, as well as 439 Estonian cases from the University of Tartu were genotyped at the Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel. The respective samples were provided by the groups given in Supplementary Table 2 and listed in the PAGE members list (Supplementary Note). The German controls were obtained from a population-based sample from the general population living in the region of Augsburg, Southern Germany (Collaborative Health Research in the Region of Augsburg; KORA S4/F471), which was genotyped at the Helmholtz Center in Munich, and from the population-based epidemiological Heinz-Nixdorf Recall study (HNR), which was genotyped at the Life and Brain Center at the University Clinic in Bonn. The remaining Estonian samples were obtained from and genotyped at the Estonian Genome Center University of Tartu (EGCUT).

Genotyping panel and SNPs

The Immunochip is a custom Illumina Infinium high-density array consisting of 196,524 variants (after Illumina quality control) compiled largely from variants identified in previous GWAS of 12 different immune-mediated inflammatory diseases, including psoriasis13. The main aims of the Immunochip were deeper replication and fine-mapping of genome-wide significant loci, as well as increasing power to promote promising but less significant SNPs to genome-wide significance. For fine mapping, SNPs within 0.2 cM on either side of the GWAS top SNPs for 186 loci were selected from 1000 Genomes Project72 low coverage pilot CEU sequencing data as well as additional variants identified by resequencing from groups involved in the chip design. For promotion of promising signals and those not quite reaching genome-wide significance, each disease-focused group was allowed to submit approximately 3,000 additional SNPs. We submitted 17 of the 19 confirmed genome-wide significant psoriasis regions (Table 1) for fine mapping based on a preliminary meta-analysis of our data, while one of the confirmed signals (IL28RA) and nine of the new psoriasis signals (indicated in the “disease overlap” column of Supplementary Table 5) were submitted for fine mapping by other disease groups (though we also submitted three of them as part of our additional SNP allocation SNPs: rs11121129, rs10865331, and rs9504361). Six additional signals were detected based on individual groups additional SNP allocation; four of these (rs11795343, rs4561177, rs11652075, and rs545979) were submitted by our group. All Immunochip samples were genotyped as described in Illumina’s protocols.

Genotype calling

For the PAGE dataset genotype calling was performed using Illumina’s GenomeStudio Data Analysis software and the custom-generated cluster file of Trynka et al. (based on an initial clustering of 2000 UK samples with the GenTrain2.0 algorithm and subsequent manual readjustment and quality control)13. The genotype calling for the GAPC dataset was performed using GenoSNP73 from allele intensities, except for the German, Italian, Dutch and Finnish controls, which were called using the same method described for the PAGE dataset.

Imputation

To increase the number of overlapping SNPs between datasets, we performed imputation on the 3 GWAS datasets using minimac74 (Kiel and CASP) and IMPUTE275,76 (WTCCC2) based on CEU reference haplotypes from the 1000 Genomes Project72; December 2010 version of the 10/08/04 sequence and alignment release containing 629 individuals of European descent). SNPs with low imputation quality (r2 ≤0.3 for minimac, info score < 0.5 for IMPUTE2) were removed. For all 3 datasets, cases and controls were imputed together.

Sample and genotype quality control

For the Immunochip datasets, we first excluded SNPs with a call rate below 95% or with a Hardy-Weinberg p-value < 1 × 10−6. Samples with less than 98% SNP call rates were then excluded. Because the Immunochip includes a large proportion of fine-mapping SNPs that are associated with autoimmune disease, we used a set of independent SNPs which have p-values > 0.5 from the meta-analysis of the 3 GWAS datasets as a quality control tool for each individual Immunochip dataset. Using the HapMap 3 samples as reference77, we performed principal component (PC) analysis to identify and remove samples with non-European ancestry. We also removed samples with extreme inbreeding coefficients or heterozygosity values computed by PLINK78.

To assess possible stratification in the datasets, principal components analysis was also performed in each of the Immunochip datasets separately (excluding HapMap). There was no evidence of stratification between the cases and controls of each sample group. However, as expected, the top principal components (PCs) do separate the samples well by country of origin. The use of the top 10 eigenvectors as covariates in the analysis did not completely correct for this stratification and so a linear mixed model method (Efficient Mixed-Model Association eXpedited (EMMAX)) was used for the association analysis instead. These methods have been shown to outperform PCs at correcting for this type of population stratification and cryptic relatedness79, which is becoming more common as sample sizes get larger and studies comprise of more collaborative efforts.

To identify duplicate pairs or highly related individuals among datasets, we used a panel of 873 independent SNPs that were genotyped in both the GWAS and Immunochip samples, and performed pairwise comparisons using the ‘genome’ function in PLINK78 , with the criterion Pi-HAT≥ 0.5. We identified 1,142 (885 from GAPC and 257 from PAGE) related sample pairs (mostly duplicates) and removed one sample from each pair. We also removed 4,828 controls from the UK common ImmunoChip controls owing to duplication in the WTCCC2 GWAS sample. For GWAS samples that were duplicated in the Immunochip datasets (the majority of duplicates), we removed the samples from the Immunochip datasets to keep the previously published datasets intact.

The GWAS datasets underwent quality control as previously described and were analysed for association using the top PCs from the previous analyses, as covariates35.

We visually checked the signal intensity cluster plots for all SNPs meeting genome-wide significance to confirm high quality genotype calling.

Genomic Control

Genomic control inflation factors for the five datasets were 1.09 (Kiel), 1.06 (CASP), 1.04 (WTCCC2), 0.99 (PAGE), and 0.96 (GAPC), indicating that population structure and cryptic relatedness were adequately controlled for in these datasets. Because the Immunochip was designed for deep replication and fine mapping of loci associated with autoimmune diseases12, using all independent SNPs from the chip would not give an accurate estimate of the genomic control80GC) value. Therefore, we selected common (MAF > 0.05) SNPs from the Immunochip that had p-values > 0.5 based on a meta-analysis combining the CASP, Kiel, and the WTCCC2 GWAS, and then performed LD-pruning to identify an independent SNP set to compute λGC for the association results from the Immunochip datasets. Due to the SNP selection bias, the genomic control of the final meta-analysis was computed using a set of independent SNPs associated with “reading and writing ability” (personal communication, J.C. Barrett). We further removed SNPs that were within ±500 kb of previously detected psoriasis loci (±3 Mb was used for the MHC region), and the remaining 1,426 SNPs yielded a λGC value of 1.11 for the meta-analysis overall. By using the λ100081, the genomic control inflation factor for an equivalent study of 1000 cases and 1000 controls, the rescaled λ equals 1.01.


Supplementary Material 1

Notes

FN4AUTHOR CONTRIBUTIONS

J.T.E., R.C.T. and G.R.A. designed and directed the study. R.P.N., M.W., J.D., J.V., J.T.E., F.C., J.N.B., M.A., C.S., A.D.B., C.G., A.R., J.Ke., X.E., W.W., J.Wo., R.T-A., M.S., G.N., L.S., R.M., M.C., J.S., A.F., S.W., S.K., K.K., T.E., A.M., A.B., G.K., D.G., P.R., U.M., F.N., A.H., J.W., S.S., C.W., C.L., S.E., R.A., V.C., and C.F.R., and H.B. contributed to sample collection and phenotyping. J.K. coordinated the GAP consortium’s samples and datasets. J.T.E. coordinated the PAGE samples and datasets. P.De., A.S., G.B., R.D.P., D.V., and C.C.A.S. contributed to the design of the Immunochip. J.K., P.E.S, G.R.A., and H.M.K. advised on the statistical analysis. C.L., S.E., R.A., H.B., E.E., P.H., and R.P.N. performed genotyping. E.E., S.L.S., L.C.T., and H.M.K. performed the genotype calling. S.L.S., L.C.T., Y.L., and D.J. performed genotype imputation and statistical analysis. F.C., J.N.B., J.E.G., T.T., J.T.E., and A.F. prepared Box 1. L.C.T., S.L.S., F.C. and J.T.E. drafted the manuscript, and prepared the figures and tables. E.E., J.E.G., J.K., P.E.S., R.P.N., R.C.T., T.T., G.R.A., J.N.K., and A.F. edited and revised the manuscript. All authors approved the final draft.

FN5COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

FN6Note: Supplementary information is available on the Nature Genetics website.

FN7URLs

WTCCC common controls: http://www.wtccc.org.uk 1000 Genomes Project data are available at: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/ National Human Genome Research Institute (NHGRI) GWAS catalog: http://www.genome.gov/gwastudies eQTL database: http://www.sph.umich.edu/csg/junding/eQTL/TableDownload/

FN8METHODS

Methods and any associated references are available in the online version of the paper at http://www.nature.com/naturegenetics/.

52Dept Statistics, University of Oxford, Oxford OX1 3TG, UK;

53Telethon Institute for Child Health Research, Centre for Child Health Research, University of Western Australia, 100 Roberts Road, Suciaco, Western Australia 6008;

54Genetics and Infection Laboratory, Cambridge Institute of Medical Research, Addenbrooke’s Hospital, Cambridge CB2 0XY, UK;

55Division of Psychological Medicine and Psychiatry, Biomedical Research Centre for Mental Health at the Institute of Psychiatry, King’s College London

56The South London and Maudsley NHS Foundation Trust, Denmark Hill, London SE5 8AF, UK;

57Diamantina Institute of Cancer, Immunology and Metabolic Medicine, Princess Alexandra Hospital, University of Queensland, Brisbane, Queensland, Australia;

58Dept Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK;

59Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin 2, Eire;

60Dept Psychological Medicine, Cardiff University School of Medicine, Heath Park, Cardiff CF14 4XN, UK;

61Molecular and Physiological Sciences, The Wellcome Trust, London NW1 2BE;

62Centre for Gastroenterology, Bart’s and the London School of Medicine and Dentistry, London E1 2AT, UK;

63Clinical Neurosciences, St George’s University of London, London SW17 0RE;

64 Oxford Centre for Diabetes, Endocrinology and Metabolism (ICDEM), Churchill Hospital, Oxford OX3 7LJ, UK;

65Biomedical Research Centre, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK;

66Social, Genetic and Developmental Psychiatry Centre, King’s College London Institute of Psychiatry, Denmark Hill, London SE5 8AF, UK;

67University of Cambridge Dept Clinical Neurosciences, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK;

68Dept Cardiovascular Science, University of Leicester, Glenfield Hospital, Leicester LE3 9QP;

69Glaucoma Research Unit, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD,UK;

70Dept Genetics, University College London Institute of Ophthalmology, London EC1V 9EL, UK;

71Dept Molecular Neuroscience, Institute of Neurology, Queen Square, London WC1N 3BG, UK;

72Department of Dermatology and Venerology, University of Helsinki, Helsinki, Finland;

73Asklepios Nordseeklinik, Westerland/Sylt, Germany;

74TOMESA Clinics, Bad Salschlirf, Germany;

75Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany;

76Department of Dermatology, University of Göttingen, Göttingen, Germany;

77Department of Dermatology, University of Münster, Münster, Germany;

78Psoriasis Rehabilitation Hospital, Bad Bentheim, Germany;

79Department of Endocrinology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;

80Department of Epidemiology and Biostatistics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;

81University of Dundee, Dundee, UK;

82University College Hospital Galway, Galway, Ireland;

83St Vincent’s University Hospital, Dublin, Ireland;

84 Department of Clinical Medicine, Trinity College Dublin, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland;

85Dermatology Service, Hospital del Mar-IMAS, Barcelona, Spain;

86Department of Dermatology and Venereology, Sahlgrenska University Hospital, Gothenburg, Sweden;

87National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA;

88Cardiovascular Research Institute and Center for Human Genetics, University of California-San Francisco, CA;

89Department of Psychiatry, Washington University School of Medicine, St. Louis, MO;

90Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI;

91Celera, 1401 Harbor Bay Parkway, Alameda, CA;

92Departments of Pediatrics, Rheumatology and Gastroenterology, University of Utah, Salt Lake City, UT;

93Centre National de Génotypage, Institut Génomique, Commissariat á l’Énergie Atomique, Evry, France;

94Department of Dermatology, University of California, San Francisco;

95Department of Dermatology, Baylor University Medical Center, Dallas, TX;

96Seay Center for Musculoskeletal Research, Texas Scottish Rite Hospital for Children, Dallas, TX;

97National Psoriasis Foundation, Portland, OR 97223 USA;

98Institute of Genetic Epidemiology, Helmholtz Centre Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany,

99Research Unit Molecular Epidemiology, Helmholtz Centre Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany;

100Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany;

101Clinic of Cardiology, West German Heart Centre, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.

Major support for this study was provided by the National Institutes of Health, the Wellcome Trust, and the German Research Foundation. We acknowledge use of the British 1958 Birth Cohort DNA collection, funded by the Medical Research Council (G0000934) and the Wellcome Trust (068545/Z/02), and of the UK National Blood Service controls funded by the Wellcome Trust. We acknowledge the Collaborative Association Study of Psoriasis (CASP) for the contribution of GWAS data, as well as the provision of control DNA samples by the Cooperative Research in the Region of Augsburg (KORA) and Heinz Nixdorf Recall (Risk Factors, Evaluation of Coronary Calcification, and Lifestyle) study (HNR), and genotyping data generated by the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) consortium. We thank the Barbara and Neal Henschel Charitable Foundation for their support of the National Psoriasis Victor Henschel BioBank. We thank Jeffrey C Barrett for contribution to the design of the Immunochip and helpful analytical discussion, as well as Emma Gray, Suzannah Bumpstead, Douglas Simpkin and staff of the Wellcome Trust Sanger Institute Sample Management and Genotyping teams for their genotyping and analytical contributions. We acknowledge the Genetic repository in Ireland for Psoriasis and Psoriatic Arthritis (GRIPPsA), the Irish blood transfusion service / TCD Biobank and the Dublin Centre for Clinical Research (funded by HRB and the Wellcome Trust). Detailed consortium contributorship lists and relevant funding support are detailed in the Supplementary Note.


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Consortia

Wellcome Trust Case Control Consortium 2

Peter Donnelly30,52, Leena Peltonen29, Jenefer M Blackwell53,54, Elvira Bramon55,56, Matthew A Brown57, Juan P Casas58, Aiden Corvin59, Nicholas Craddock60, Audrey Duncanson61, Janusz Jankowski62, Hugh S Markus63, Christopher G Mathew2, Mark I McCarthy64, Colin NA Palmer65, Robert Plomin66, Anna Rautanen30, Stephen J Sawcer67, Nilesh Samani68, Ananth C Viswanathan69,70, Nicholas W Wood71, Céline Bellenguez30, Colin Freeman30, Garrett Hellenthal30, Eleni Giannoulatou30, Matti Pirinen30, Zhan Su30, Sarah E Hunt29, Rhian Gwilliam29, Suzannah J Bumpstead29, Serge Dronov29, Matthew Gillman29, Emma Gray29, Naomi Hammond29, Alagurevathi Jayakumar29, Owen T McCann29, Jennifer Liddle29, Marc L Perez29, Simon C Potter29, Radhi Ravindrarajah29, Michelle Ricketts29, Matthew Waller29, Paul Weston29, Sara Widaa29, Pamela Whittaker29.

Genetic Analysis of Psoriasis Consortium (GAPC)

Alexandros Onoufriadis2, Michael E Weale2, Angelika Hofer20, Wolfgang Salmhofer20, Peter Wolf20, Kati Kainu72, Ulpu Saarialho-Kere72, Sari Suomela72, Petra Badorf45, Ulrike Hüffmeier45, Werner Kurrat73, Wolfgang Küster74, Jesus Lascorz75, Rotraut Mössner76, Funda Schürmeier-Horst77, Markward Ständer78, Heiko Traupe77, Judith G M Bergboer12, Martin den Heijer79,80, Peter C. van de Kerkhof12, Patrick L J M Zeeuwen12, Louise Barnes7,8, Linda E Campbell81, Catriona Cusack82, Ciara Coleman7,8, Judith Conroy7, 8, Sean Ennis7, 8, Oliver Fitzgerald83, Phil Gallagher83, Alan D Irvine84, Brian Kirby83, Trevor Markham82, WH Irwin McLean81, Joe McPartlin7, 8, Sarah F Rogers83, Anthony W Ryan7, 8, Agnieszka Zawirska83, Emiliano Giardina9, Tiziana Lepre9, Carlo Perricone9, Gemma Martín-Ezquerra85, Ramon M Pujol85, Eva Riveira-Munoz17, Annica Inerot86, Åsa T Naluai11, Lotus Mallbris13, Katarina Wolk13, Joyce Leman14, Anne Barton21, Richard B Warren44, Helen S Young44, Isis Ricano-Ponce28, Gosia Trynka28

Collaborative Association Study of Psoriasis (CASP)

Kristina Callis Duffin19, Cindy Helms18, David Goldgar19, Yun Li1, Justin Paschall87, M. J. Malloy88, C. R. Pullinger88, J. P. Kane88, J. Gardner18, A. Perlmutter89, A. Miner89, Bing Jian Feng19, Ravi Hiremagalore6, Robert W. Ike90, Enno Christophers31, Tilo Henseler31, Andreas Ruether5, Steven J. Schrodi91, Sampath Prahalad92, Stephen L Guthery92, Judith Fischer93, Wilson Liao94, Pui Kwok94, Alan Menter95, G. Mark Lathrop93, C. Wise96, Ann B. Begovich91

Psoriasis Association Genetics Extension (PAGE)

Fawnda J Pellett41, Andrew Henschel97, Marin Aurand97, Bruce Bebo97

Cooperative Research in the Region of Augsburg (KORA)

Christian Gieger98, Thomas Illig99

Heinz Nixdorf Recall (Risk Factors, Evaluation of Coronary Calcification, and Lifestyle) study (HNR)

Susanne Moebus100, Karl-Heinz Jöckel100, Raimund Erbel101


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