Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility

Abstract

Psoriasis is a common inflammatory skin disease with complex genetics and different degrees of prevalence across ethnic populations. Here we present the largest trans-ethnic genome-wide meta-analysis (GWMA) of psoriasis in 15,369 cases and 19,517 controls of Caucasian and Chinese ancestries. We identify four novel associations at LOC144817, COG6, RUNX1 and TP63, as well as three novel secondary associations within IFIH1 and IL12B. Fine-mapping analysis of MHC region demonstrates an important role for all three HLA class I genes and a complex and heterogeneous pattern of HLA associations between Caucasian and Chinese populations. Further, trans-ethnic comparison suggests population-specific effect or allelic heterogeneity for 11 loci. These population-specific effects contribute significantly to the ethnic diversity of psoriasis prevalence. This study not only provides novel biological insights into the involvement of immune and keratinocyte development mechanism, but also demonstrates a complex and heterogeneous genetic architecture of psoriasis susceptibility across ethnic populations.

ARTICLE

Received 1 Sep 2014 | Accepted 13 Mar 2015 | Published 23 Apr 2015

DOI: 10.1038/ncomms7916 OPEN

Genome-wide meta-analysis identies multiple novel associations and ethnic heterogeneityof psoriasis susceptibility

Xianyong Yin1,2,3,4,*, Hui Qi Low5,*, Ling Wang5, Yonghong Li6, Eva Ellinghaus7, Jiali Han8,9,10, Xavier Estivill11,12,13,14,

Liangdan Sun1,2,3,4, Xianbo Zuo1,2,3,4, Changbing Shen1,2,3,4, Caihong Zhu1,2,3,4, Anping Zhang1,2,3,4, Fabio Sanchez15,

Leonid Padyukov16, Joseph J. Catanese6, Gerald G. Krueger17, Kristina Callis Dufn17, Sren Mucha7,Michael Weichenthal18, Stephan Weidinger18, Wolfgang Lieb19, Jia Nee Foo5, Yi Li5, Karseng Sim5, Herty Liany5, Ishak Irwan5, Yikying Teo20,21, Colin T.S. Theng22, Rashmi Gupta23, Anne Bowcock24, Philip L. De Jager25,26, Abrar A. Qureshi8,9,26, Paul I.W. de Bakker27,28, Mark Seielstad5,29,#, Wilson Liao23,#, Mona Sthle15,#,

Andre Franke7,#, Xuejun Zhang1,2,3,4,# & Jianjun Liu2,3,4,5,30,31,#

Psoriasis is a common inammatory skin disease with complex genetics and different degrees of prevalence across ethnic populations. Here we present the largest trans-ethnic genome-wide meta-analysis (GWMA) of psoriasis in 15,369 cases and 19,517 controls of Caucasian and Chinese ancestries. We identify four novel associations at LOC144817, COG6, RUNX1 and TP63, as well as three novel secondary associations within IFIH1 and IL12B. Fine-mapping analysis of MHC region demonstrates an important role for all three HLA class I genes and a complex and heterogeneous pattern of HLA associations between Caucasian and Chinese populations. Further, trans-ethnic comparison suggests population-specic effect or allelic heterogeneity for 11 loci. These population-specic effects contribute signicantly to the ethnic diversity of psoriasis prevalence. This study not only provides novel biological insights into the involvement of immune and keratinocyte development mechanism, but also demonstrates a complex and heterogeneous genetic architecture of psoriasis susceptibility across ethnic populations.

1 Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, Anhui 230032, China. 2 State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Anhui Medical University, Hefei, Anhui 230032, China. 3 Key Lab of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui 230032, China. 4 Collaborative Innovation Center for Complex and Severe Skin Diseases, Anhui Medical University, Hefei, Anhui 230032, China. 5 Department of Human Genetics, Genome Institute of Singapore, A*STAR, Singapore 138672, Singapore. 6 Celera, Alameda, California 94502, USA. 7 Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Schittenhelm Street 12, Kiel 24105, Germany. 8 Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana 46202, USA. 9 Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana 46202, USA.

10 Department of Dermatology, School of Medicine, Indiana University, Indianapolis, Indiana 46202, USA. 11 Genetic Causes of Disease Group, Centre for Genomic Regulation (CRG), Barcelona, Catalonia, E-08003, Spain. 12 Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, E-08003, Spain.

13 Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, E-08003, Spain. 14 CIBER in Epidemiology and Public Health (CIBERESP), Barcelona, Catalonia, E-08003, Spain. 15 Unit of Dermatology and Venereology, Department of Medicine, Karolinska Institutet, Stockholm 17176, Sweden. 16 Rheumatology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm 17177, Sweden. 17 Department of Dermatology, University of Utah, Salt Lake, Utah 84132, USA. 18 Department of Dermatology, University Hospital Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany. 19 Institute of Epidemiology and Biobank PopGen, Christian Albrechts University, Kiel, Germany. 20 Departments of Statistics and Applied Probability, National University of Singapore, Singapore 138672, Singapore. 21 Department of Epidemiology and Public Health, National University of Singapore, Singapore 138672, Singapore. 22 National Skin Centre, Singapore 308205, Singapore. 23 Department of Dermatology, University of California San Francisco, San Francisco, California 94115, USA. 24 National Heart and Lung Institute, Imperial College, London SW3 6LY, UK.

25 Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02138, USA. 26 Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham & Womens Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 27 Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands. 28 Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, 3584 CG, The Netherlands. 29 Institute for Human Genetics, University of California San Francisco, San Francisco, California 94143, USA.

30 Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, 138672, Singapore. 31 School of Life Sciences, Anhui Medical University, Hefei, Anhui 230032, China. * These authors contributed equally to this work. # These authors jointly supervised this work. Correspondence and requests for materials should be addressed to A.F. (email: mailto:[email protected]

Web End [email protected] ) or to X.J.Z. (email: mailto:[email protected]

Web End [email protected] ) or to J.J.L. (email:mailto:[email protected]

Web End [email protected] ).

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Psoriasis is a common immune-mediated chronic inammatory disease characterized by hyperplasia, altered proliferation and differentiation of keratinocytes, vascular

remodelling and inammation in the skin, with musculoskeletal inammation of the joints observed in up to 30% of patients1. Psoriasis shows a diverse prevalence across worldwide populations, 2.5% in Europeans, 0.053% in Africans and0.10.5% in Asians24. Phenotypic heterogeneity of psoriasis has also been reported among ethnic populations, such as clinical manifestation, response to treatments and disease progression36.

Psoriasis has a high genetic predisposition with estimated heritability up to 80% (refs 1,7). Forty-one susceptibility loci have been identied at genome-wide signicance (Po5 10 8)

mainly through genome-wide association studies (GWASs)816, but these susceptibility loci collectively only explain a limited fraction of the heritability of psoriasis16,17. In addition, few loci also show differential associations between ethnic populations, suggesting potential genetic heterogeneity of psoriasis, although most of the loci have not been systematically studied beyond the original population in which they are discovered2.

Recently, trans-ethnic genome-wide meta-analysis (GWMA) of the GWAS data sets of diverse populations have been performed to identify additional susceptibility loci and dissect validated loci through ne-mapping analysis, which can help to better understand the genetic architecture of complex diseases1826. Trans-ethnic GWMA across diverse populations has not been performed in psoriasis, and the genetic heterogeneity of psoriasis across diverse ancestries has not yet been systematically investigated.

Here, we describe a large-scale trans-ethnic GWMA of psoriasis using multiple GWAS data sets as well as independent validation samples from Caucasian and Chinese populations. We identify four novel associations at LOC144817, COG6, RUNX1 and TP63, as well as three novel secondary associations within IFIH1 and IL12B. In addition, our study suggests population-specic effect or allelic heterogeneity for 11 loci, and indicates that these population-specic effects contribute signicantly to the ethnic diversity of psoriasis prevalence. Together, these results provide novel biological insights into the involvement of immune and keratinocyte development mechanism, but also demonstrate

a complex and heterogeneous genetic architecture of psoriasis susceptibility across ethnic populations.

ResultsGenome-wide Meta-Analysis. We carried out GWMA of psoriasis using seven independent GWAS data sets, including ve published ones (four Caucasians and one Chinese)11,15,16 and two unpublished ones (one Caucasian and one Chinese; Table 1). The combined data set consists of a total number of 5,084 psoriatic cases and 8,732 controls, with 3,496 cases and 5,186 controls from Caucasian population and 1,588 cases and 3,546 controls from Chinese population. To combine these seven independent data sets that were generated by using different genotyping platforms and further enhance the coverage of genetic variants for association analysis, whole-genome imputation was performed in each of the seven data sets separately using the 1000 Genomes Project data set (Phase I, Dec. 2010 version) as the reference panel. After applying stringent quality controls (QCs), we tested 4,778,154, 4,562,294 and 3,621,551 single-nucleotide polymorphisms (SNPs) for association in the Caucasian, Chinese and combined cohorts, respectively (Table 1). Both the quantile quantile plots and genomic ination factors (lGC) of the genome-

wide test statistic (1.017 for Caucasian cohort, 1.01 for Chinese cohort and 1.011 for the combined cohort) demonstrated that the three genome-wide association analyses had negligible ination because of population stratication (Table 1 and Supplementary Fig. 1). Meanwhile, the quantilequantile and Manhattan plots of the three genome-wide analyses showed an excess of SNPs with small P values (o1 10 4 from the logistic regression analysis

in individual ethnic and combined data sets) after removing the SNPs within 41 known loci, suggesting additional associations (of more modest effect) may be identied with further validation (Fig. 1 and Supplementary Fig. 1). All the known loci showed supporting evidences of association with nominal signicance (Po0.05) in at least one genome-wide analysis, except CSMD1 and SERPINB8 that did not show any association, likely due to the insufcient statistical power (o30%) of the current study for detecting their associations (Supplementary Data 1).

Table 1 | Description of study samples.

Study Ethnicity Population sample Cases (N) Controls (N) Total kGC kGC_1000

GWAS data sets

UC European US 202 492 694 1.041* 1.143 KI European Sweden 727 1,198 1,925 1.032* 1.035 GAIN European US 1,336 1,372 2,708 1.032 1.024 Kiel European Germany 471 1,129 1,600 1.046* 1.069 Genizon European Canada 760 995 1,755 1.039 1.045 European 3,496 5,186 8,682 1.073w 1.017 Anhui Chinese China 1,139 1,112 2,251 1.016* 1.014 Singapore Chinese Singapore 449 2,434 2,883 1.031 1.041 Chinese 1,588 3,546 5,134 1.022w 1.01 TOTAL 5,084 8,732 13,816 1.071w 1.011

Replication cohorts

UC European US 896 636 1,532 KI European Sweden 1,025 510 1,535 Harvard European US 559 572 1,131 Spain European Spain 408 327 735 Celera European US 1,436 1,380 2,816 Kiel European Germany 810 2,208 3,018 Anhui Chinese China 5,151 5,152 10,303 TOTAL 10,285 10,785 21,070

*Adjust for principal components (PCs).

wAdjust for studies and PCs.

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Validation Analysis of Novel Associations. We carried out the validation study of novel associations suggested by GWMA. Forty-three novel SNPs with suggestive association (Po5.00 10 5 from the logistic regression analysis) in at least

one of the three discovery analyses (Caucasian, Chinese and

Combined) were genotyped in additional two independent validation cohorts totaling 10,285 psoriatic cases and 10,785 controls, with 5,134 cases and 5,633 controls from Caucasian and 5,151 cases and 5,152 controls from Chinese populations (Table 1 and Supplementary Data 2). In the Caucasian samples, four novel associations were conrmed at genome-wide signicance with consistent effects between the discovery and validation samples: rs9533962 (odds ratio (OR) 1.14, P 1.93 10 8,

LOC144817), rs34394770 (OR 1.16, P 2.65 10 8, COG6),

rs8128234 (OR 1.17, P 3.74 10 8, RUNX1) and

rs28512356 (OR 1.17, P 4.31 10 8, TP63; Table 2 and

Supplementary Figs 2 and 3). In the Chinese samples, all the four SNPs showed the consistent effect of association, but only rs34394770 and rs9533962 achieved nominal signicance (P 4.63 10 2, 2.25 10 4, respectively). Both SNPs

achieved genome-wide signicance in the combined cohort (P 4.87 10 8, 7.53 10 11; Table 2 and Supplementary

Figs 2 and 3). However, the other two novel SNPs did not achieve nominal signicant association in Chinese samples, although the Chinese samples have sufcient power (statistic power490%) to detect their effects observed in Caucasian population and was adequately powered to detect effect size (for example, ORs) as small as 1.151.25 (Supplementary Data 1 and Supplementary Table 1). No other SNPs selected from the discovery analyses in the Chinese and combined samples were validated.

Giving that all the newly discovered SNPs are noncoding variants, we have investigated the regulatory functions of these SNPs by analysing the information from the HaploReg (v2) and eQTL databases. The four novel SNPs were enriched in the enhancers of epidermal keratinocytes, and resided in known DNAse I hypersensitivity in multiple cell lines including normal human epidermal keratinocytes (Supplementary Table 2). And it was shown that three linked SNPs with rs34394770 conferred eQTL effect for COG6 in monocytes (Supplementary Table 3).

We also searched for independent secondary associations within 45 previously and newly conrmed susceptibility loci in the discovery samples through conditional logistic regression analysis using the leading SNP within each locus as covariate (Fig. 2,3,4 and Supplementary Figs 35). First, we discovered three independent associations within IL12B locus at two novel SNPs, rs4921493 (Pcondition 6.80 10 13) and rs2853694

(Pcondition 1.22 10 9) and the previously reported

rs7709212 (OR 1.38, P 2.44 10 30) in the combined

discovery samples, whose independent effects were also conrmed by multivariate analysis (Supplementary Table 4a). The three SNPs are in low or moderate linkage disequilibrium (LD) (Supplementary Fig. 3e and Supplementary Table 4b) and showed consistent associations between the Caucasian and Chinese samples (Supplementary Table 4a). In addition, haplotype analysis also supported independent effects of the three SNPs (Supplementary Table 4c). Second, we discovered a novel independent SNP within the IFIH1 locus. In Caucasian samples,

SLC45A1-TNFRSF9

RUNX3

IL28RA

IL23R

LCE

B3GNT2

IFIH1

ERAP1-ERAP2

IL13

TNIP1-ANXA6

IL12B

EXOC2-IRF4

MHC

TRAF3IP2

TNFAIP3

TAGAP

ELMO1

CSMD1

DDX58

KLF4

ZMIZ1

PRDX5

ZC3H12C

ETS1

IL23A-STAT2

GJB2

PSMA6-NFKBIA

PRM3-SOCS1

FBXL19

NOS2

PTRF-STAT3

CARD14

COG6

LOC144817

POL1-STARD6

SERPINB8

ILF3-CARM1

TYK2

ZNF816A

RNF114

UBE2L3

RUNX1

REL

TP63

a

35

P achieved 5E-118

Log 10 Pvalues

30 25 20 15 10

5 0

1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

Chromosome

SLC45A1-TNFRSF9

RUNX3

IL28RA

IL23R

LCE

REL

B3GNT2

IFIH1

ERAP1-ERAP2

IL13

TNIP1-ANXA6

IL12B

EXOC2-IRF4

MHC

TRAF3IP2

TNFAIP3

TAGAP

ELMO1

CSMD1

DDX58

KLF4

ZMIZ1

PRDX5

ZC3H12C

ETS1

IL23A-STAT2

GJB2

COG6

LOC144817

PSMA6-NFKBIA

PRM3-SOCS1

FBXL19

NOS2

PTRF-STAT3

CARD14

POL1-STARD6

SERPINB8

ILF3-CARM1

TYK2

ZNF816A

RNF114

UBE2L3

RUNX1

TP63

b

P achieved 5E-183

Log 10 Pvalues

35 30 25 20 15 10

5 0

1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

Chromosome

SLC45A1-TNFRSF9

RUNX3

IL28RA

IL23R

LCE

REL

B3GNT2

IFIH1

ERAP1-ERAP2

IL13

TNIP1-ANXA6

IL12B

EXOC2-IRF4

MHC

TRAF3IP2

TNFAIP3

TAGAP

ELMO1

CSMD1

DDX58

KLF4

ZMIZ1

PRDX5

ZC3H12C

ETS1

IL23A-STAT2

GJB2

PSMA6-NFKBIA

PRM3-SOCS1

FBXL19

NOS2

PTRF-STAT3

CARD14

COG6

LOC144817

POL1-STARD6

SERPINB8

ILF3-CARM1

TYK2

ZNF816A

RNF114

UBE2L3

RUNX1

TP63

c

35 30 25 20 15 10

5 0

P achieved 5E-265

Log 10 Pvalues

Figure 1 | Manhattan plot of single SNP association test results.(a) Chinese GWAS meta-analysis with 1,588 cases and 3,546 controls; (b) Caucasian GWAS meta-analysis with 3,496 cases and 5,186 controls; (c) trans-ethnic GWAS meta-analysis with 5,084 cases and 8,732 controls. The x-axis indicates the chromosomal position. The y-axis indicates the log10 P values of genome-wide SNP associations from each GWAS meta-analysis using logistic regression. The horizontal green line represents the genome-wide signicance threshold of P 5.0 10 8. Blue dots indicate

the association results of SNPs within the 41 known psoriasis risk loci; red dots indicate the association results of SNPs within the four new psoriasis risk loci; grey dots indicate the association results of SNPs outside the 45 psoriasis risk loci.

1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

Chromosome

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Table 2 | Meta-analysis results of the 4 novel psoriasis susceptibility loci.

Gene SNP Chr BP.B37 RA RAF Stage European Chinese Combined All

EUR CHN P OR(95%CI) P OR(95%CI) P OR(95%CI) PQ TP63 rs28512356 3 189615475 C 0.799 0.826 GWAS 9.99E 06 1.20 (1.111.30) 9.52E 01 1.00 (0.881.13) 7.70E 06 1.10 (1.061.15) 9.62E 04

Validation 9.30E 04 1.15 (1.061.25) 4.93E 01 1.03 (0.961.10)

Combined 4.31E 08 1.17 (1.111.24) 5.68E 01 1.02 (0.961.08)

COG6 rs34394770 13 40333369 T 0.643 0.763 GWAS 5.55E 07 1.18 (1.111.26) 1.22E 01 1.10 (0.981.23) 4.87E 08 1.11 (1.071.15) 2.04E 02

Validation 8.21E 03 1.12 (1.031.21) 1.43E 01 1.05 (0.981.11)

Combined 2.65E 08 1.16 (1.101.22) 4.63E 02 1.06 (1.001.12)

LOC144817 rs9533962 13 45334194 C 0.406 0.449 GWAS 1.76E 03 1.11 (1.041.18) 3.93E 05 1.23 (1.121.36) 7.53E 11 1.12 (1.081.15) 2.91E 01

Validation 1.60E 06 1.16 (1.091.24) 5.30E 02 1.06 (1.001.12)

Combined 1.93E 08 1.14 (1.091.19) 2.25E 04 1.09 (1.041.15)

RUNX1 rs8128234 21 36470865 T 0.197 0.111 GWAS 1.82E 05 1.19 (1.101.28) 8.98E 02 1.15 (0.981.34) 5.99E 08 1.13 (1.081.18) 4.23E 02

Validation 4.77E 04 1.15 (1.061.24) 3.35E 01 1.04 (0.961.13)

Combined 3.74E 08 1.17 (1.111.23) 9.88E 02 1.06 (0.991.14)

BP.B37, position based on NCBI build 37; CHN, Han Chinese samples; Chr, chromosome; GWAS, genome-wide association study; EUR, European samples; RA, risk alleles; RAF, risk alleles frequencies;

OR, odds ratio; P: association P values in logistic regression test (alleles dosages in GWAS studies); 95% CI, 95% condence interval; P : P values of heterogeneity test; SNP, single-nucleotide

polymorphism.

a b

ZMIZ1 regional association plot (Caucasian) ZMIZ1 regional association plot (Caucasian)

rs1250560

8 80

644832160 0

1.6

6.4

rs1250560

rs1250546

log 10 P

log 10 P

6.44.83.21.6 0

8

Recombination rate (cM/Mb)

rs1250544

rs1250544

log 10 P

8 80 64 48 32 16 0

Recombination rate (cM/mb)

4.8

3.2

rs1250546

ZMIZI

PPIF

ZCCHC24 ZCCHC24

PPIF

ZMIZ1

rs1250544

80,800 80,900 81,000 81,100 81,200

Chromosome 10 position (Kb) - B37

80,800 80,900 81,000 81,100 81,200

Chromosome 10 position (kb) - B37 ZMIZ1 regional association polt (Chinese)

c d

ZMIZ1 regional association plot (Chinese)

8

6.44.83.21.6 0

80,800 80,900 81,000 81,100 81,200

I 2< = 0.25 0.25 < I 2<=0.5

0.75 < I 2

0.5 < = I 2<=0.75

80 64 48 32

Recombination rate (cM/Mb)

80 64 48 32

Recombination rate (cM/Mb)

6.4

4.8

log 10 P

3.2

rs10762846

rs10762846

rs1250544

rs1250546

rs1250546

16 0

16

1.6

0

0

ZMIZI

PPIF

ZCCHC24 PPIF

ZMIZI ZCCHC24

Chromosome 10 position (kb)-B37

80,800 80,900 81,000 81,100 81,200

Chromosome 10 position (kb)-B37

Figure 2 | The regional association plots of ZMIZ1 showing locus heterogeneity. The relative location of annotated genes and the direction of transcription are shown in the lower portion of the gure, and the chromosomal position is shown on the x axis. The blue line shows the recombination rate (estimated from HapMap data of CEU, CHB and combined population) across the region (right y axis), and the left y axis shows the signicance of the SNP associations ( log10 P). The square indicates the SNPs for conditional analysis (these are the top or secondary SNPs); the circle labelled

with rs IDs are reported psoriasis susceptibility SNPs. All circles and squares are colour lled based on the heterogeneity results (I2) in our trans-ethnic meta analysis. (a,b) Unconditional and conditional logistic association results of Caucasian, (c, d) unconditional and conditional logistic association results of Chinese.

the conditional analysis on the top SNP rs1990760 (OR 1.20,

P 3.21 10 13) revealed a novel independent association at

rs3747517 (ORcondition 0.75, P

condition

2.87 10 11),

although rs3747517 did not show association by itself (OR 1.04, P 0.15; Supplementary Fig. 3f and Supplementary

Table 5a). Consistently, rs3747517 only showed signicant association (ORcondition 0.80, P

condition

5.16 10 9) in the

Chinese cohort after conditioning on rs1990760 (Supplementary Fig. 3f and Supplementary Table 5a). The interactive effect

between the two SNPs was conrmed by haplotype association analysis showing that only the haplotype 2 carrying the C alleles of the two SNPs showed strong protective effect (Supplementary Table 5b). In addition, our analysis showed that conditioning on rs1990760 can abolish the two previously reported independent associations at rs2111485 and rs17716942 (Supplementary Table 5a). These results suggest that both rs1990760 and rs3747517 tag the association effect of this haplotype in Caucasian and Chinese populations, and true causal variant carried by this

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haplotype remains to be discovered. In addition, we have also conrmed the previously reported independent associations at rs2910686 and rs30376 within ERAP1/ERAP2 locus16 in the Caucasian GWAS samples (Supplementary Table 6 and Supplementary Fig. 3g). However, we did not detect the independent association effect of rs2910688 (OR 0.99,

P 0.67) in the Chinese GWAS samples, although there was

sufcient power to detect the effect of the same size (power 0.98

at P 0.05). Another six suggestive secondary associations failed

to be validated or reach genome-wide signicance (Supplementary Data 3).

Fine Mapping Analysis of HLA Associations. To better understand the strong and extensive association within the major histocompatibility complex (MHC) region (chr6: 2040 Mb, build 36; Supplementary Fig. 5), we performed a ne-mapping analysis of the region by imputing classical alleles and coding variants of HLA molecules and untyped SNPs in the Caucasian and Chinese discovery cohorts separately. As expected, the HLA-C*06:02 allele showed the strongest association within the region in both Caucasian (P 3.05 10 167) and Chinese (P 2.09 10 101).

Stepwise conditional analysis revealed additional independent associations at the penta-allelic amino-acid polymorphism at the position 67 of HLA-B (HLA-B_AA_67) (Pcondition 4.31 10 25

after conditioning on HLA-C*06:02), HLA-A*02:01 (Pcondition 3.62 10 10 after conditioning on both HLA

C*0602 and HLA-B_AA_67) and rs9265656 (Pcondition 5.27

10 9 after conditioning on the three HLA variants) in the Caucasian cohort (Supplementary Table 7). Rs9265656 tags (r2 0.78) the classical allele HLA-B*07 (P

condition

1.13 10 5

after conditioning on the three HLA variants above) and did not show any eQTL effect. In the Chinese population, the stepwise conditional analysis revealed additional independent associations at HLA-A*02:07 (Pcondition 5.30 10 40 after conditioning on

HLA-C*06:02), amino-acid position 67 of HLA-B (Pcondition 8.96 10 25 after conditioning on HLA-C*06:02

and HLA-A*02:07), the quad-allelic amino-acid position 114 of HLA-A (Pcondition 6.04 10 13 after conditioning on three

HLA variants) and rs3131857 (Pcondition 9.76 10 8 after

conditioning on all four HLA variants; Supplementary Table 7). Rs3131857 tags (r2 0.72) the biallelic amino-acid polymorphism

at the position 144 of HLA-A (HLA-A_AA_144) (Pcondition 1.55 10 6 after conditioning on the prior four

HLA variants) and did not show any eQTL effect. Beyond these HLA variants, no other HLA variants or SNPs showed independent association with Po10 7 (Supplementary Table 7 and

Supplementary Fig. 5).

Although the HLA-C*06:02 and the AA position 67 of HLA-B appear to be shared between Caucasian and Chinese populations, the other independent HLA risk variants differ between the two populations. HLA-A*02:07 shows a strong association in Chinese but is very rare or absent in Europeans, whereas HLA-B*07 shows a strong association in Caucasians but is in turn very rare in Chinese. The other HLA variants are common (410%) in both

Caucasian and Chinese, but show population-specic associations, HLA-A*02:01 for Caucasian and the AA positions 114 and 144 of HLA-A for Chinese. Our ndings indicate that all three major HLA class I genes may play an important role in psoriasis, and more importantly, illustrate a complex and heterogeneous pattern of HLA associations between Caucasian and Chinese populations.

Analysis of Ethnic Heterogeneity. To further investigate the ethnic heterogeneity of psoriasis susceptibility, we compared the association signals of 44 conrmed (40 previously reported and 4 newly discovered) non-MHC loci between the Caucasian and

Chinese cohorts, searching for the loci where association effects are mapped to different independent SNPs in two populations (allelic heterogeneity) or only detected in one population (locus heterogeneity). Of the 44 loci, we did not observe evidence of genetic heterogeneity among the independent samples of each ethnic population, but found the evidence of population-specic effect at 10 loci (ELMO1, ERAP2, PRDX5, PRM3.SOCS1, RNF114, RUNX1, TP63, TRAF3IP2, TYK2 and ZMIZ1), where the association effect was only detected in the Caucasian samples (Pcorrectedo0.05 after correction for 44 independent SNPs tested),

but not in the Chinese GWAS samples (PZ0.1; Supplementary Data 1). For each of the nine loci, the Chinese samples provide sufcient statistical power (495%) for detecting the association effect observed in the Caucasian samples at nominal signicance (Po0.05), and the OR estimate was signicantly different between the two populations (PQo0.05). Regional plots of association results clearly demonstrate that besides the top Caucasian SNPs, there are no additional associations within the effective LD of these nine loci in the Chinese samples (Fig. 2 and Supplementary Figs 3 and 4). We also constructed a genetic risk score of these ten loci (count of risk alleles carried by an individual) and evaluated its association. Although this genetic risk score shows a highly signicant association in Caucasian (P 3.94 10 36), there is no evidence of association in the

Chinese population (P 0.21; Fig. 5). Taken together, these

results provide strong evidence for the population-specic effects of these loci in the Caucasian population.

We have also estimated the contribution of these Caucasian-specic loci to the prevalence difference of psoriasis between the Caucasian and the Chinese populations. By assuming the independent and multiplicative effects of all the ten loci in Caucasians and no genetic effects in Chinese population (OR 1), our analysis indicated that the cumulative effects of

these Caucasian-specic loci could explain up to 82.83% of the prevalence difference of psoriasis between the Caucasian and the Chinese populations. Because our Chinese samples only have sufcient power (statistic power490%) to detect genetic effect of

OR 1.15 or higher, our analysis may have overestimated the

contribution of these loci to the prevalence difference by assuming that the ORs of these loci in Chinese population equal to 1. Our analysis, however, has clearly demonstrated that the cumulative genetic effects of those Caucasian-specic loci make a signicant contribution to the prevalence difference of psoriasis between the Caucasian and the Chinese populations.

In addition, the association evidences within the IL23R locus were shown to be consistent with allelic heterogeneity between Caucasian and Chinese. There were two independent associations at rs2295359 and rs12564022 in the Caucasian samples (ORcondition 1.15, P

condition

8.7 10 8 and OR condition

1.23,

Pcondition 9.0 10 14 respectively), but only one association at

rs2295359 (OR 1.15, P 3.62 10 8) in the Chinese (Fig. 3

and Supplementary Data 4). The risk T allele of rs12564022 is more frequent in Chinese than Caucasians, but did not show independent association in Chinese. The haplotypes of the two SNPs showed different associations between the two populations: although the haplotype GC showed similar protective effect in both populations, the other two common haplotypes showed population-specic risk effect with the GT showing risk effect in Caucasian and the AC showing risk effect in Chinese (Supplementary Table 8). The diverse patterns of associations are consistent with different risk variants and thus allelic heterogeneity between the two populations.

For the remaining 27 loci, consistent association (OR in the same direction and without evidence of heterogeneity) was observed at either the same SNP or SNPs in high LD between the Caucasian and Chinese populations, and the meta-analysis of the

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a b

IL23R regional association plot (Caucasian)

rs12564022

IL23R regional association plot (Caucasian)

10 40

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10

8 6 4 2 0

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6,7900

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67,600 67,700 67,900

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Chromosome 1 position (kb) - B37

67,700 67,800

I 2<=0.25

0.5 < I 2<=0.75

0.25 < I 2<=0.5

0.75 < I 2

Figure 3 | The regional association plots of IL23R showing allelic heterogeneity. The relative location of annotated genes and the direction of transcription are shown in the lower portion of the gure, and the chromosomal position is shown on the x axis. The blue line shows the recombination rate (estimated from HapMap data of CEU, CHB and Combined population) across the region (right y axis), and the left y axis shows the signicance of the SNP associations ( log10 P). The square indicates the SNPs for conditional analysis (these are the top or secondary SNPs); the circle labelled with rs IDs are

reported psoriasis susceptibility SNPs. All circles and squares are colour lled based on the heterogeneity results (I2) in our trans-ethnic meta analysis. (a,b) Unconditional and conditional logistic association results of Caucasian, (c, d) unconditional and conditional logistic association results of Chinese.

combined Caucasian and Chinese samples revealed stronger evidence for association than the two individual samples (under a xed effects model; Fig. 4, Supplementary Data 1 and Supplementary Figs 3, 4 and 6). Although not evidence of heterogeneity for the OR estimate, we observed signicant frequency difference (PQo0.05) for three SNPs within COG6,

FBXL29 and IL23A/STAT2 between Caucasian and Chinese populations (Supplementary Fig. 7). In addition, we constructed a genetic risk score of the 30 SNPs in these 27 loci and found that the genetic risk score showed a signicant difference among 52 Human Genome Diversity Panel (HGDP) populations and a signicant correlation with the longitude of population, suggesting that these loci may be under selection and polygenetic adaptation to local environment (Supplementary Fig. 8). In addition, it is also interesting to note that the populations of European origin show higher risk score than the ones of Asian origin (Supplementary Fig. 8), which is consistent with the known higher disease prevalence in Europeans than Asians.

DiscussionTP63 is a strong candidate within the locus 3q11.2. TP63 is a key regulator of mammalian epidermal stratication and keratinocyte proliferation and differentiation process27,28 and is expressed signicantly in human skin and keratinocyte (Supplementary Tables 9 and 10). The discovery of TP63 as a novel susceptibility locus provides additional biological insight into the role of

keratinocyte proliferation and differentiation in the development of psoriasis. It is also interesting to note that TP63 has also been discovered as susceptibility gene for lung and bladder cancers as well as immune and inammatory response2933. Rs8128234 on 21q22 is located within RUNX1. RUNX1 encodes the alpha subunit of core-binding factor, which is a heterodimeric transcription factor that binds to the core element of many enhancers34. RUNX1 gene has been shown to regulate alpha-beta T-cell differentiation and epidermis development (Supplementary Table 9)35,36, which are both critical for psoriasis development, and has been implicated in multiple autoimmune-related disease phenotypes including psoriasis37,38. Rs9533962 is located in long intergenic non-protein coding RNA LOC144817 (ref. 39). And rs34394770 is an intronic variant within COG6 that encodes a subunit of conserved oligomeric Golgi complex that is required for maintaining normal structure and activity of the Golgi apparatus40. Rs34394770 is in high LD with previously reported rs7993214 in Caucasian population (r240.98, D040.99)41, and our study has therefore conrmed the previously suggested association of COG6 at genome-wide signicance. The enrichment analysis of the previously and newly discovered loci has implicated a number of biological processes (gene ontology (GO) terms), which suggest the important roles of immune system and transcriptional regulation in psoriasis development (Supplementary Table 11).

We conrmed that there were multiple susceptible signals for psoriasis in MHC region. Besides HLA-C*0602, it was shown that common risk amino acid of HLA-B at position 67 in both

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a b

IL28RA regional association plot (Caucasian)

40 32 24 16 8 0

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IL28RA regional association plot (Caucasian)

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IL28RA regional association polt (Chinese)

Chromosome 1 position (kb) - B37 IL23RA regional association polt (Chinese)

86.44.83.21.6 0

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Chromosome 1 position (kb) - B37

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IL28RA regional association plot (Combined)

rs10794648

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IL28RA regional association plot (Combined)

rs10794648

rs4649203

86.4

40 32 24

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86.4

40 32 24

Recombination rate (cM/Mb)

4.83.21.6 0

16 8 0

4.83.21.6

16 8 0

0

24,400 24,500

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24,600 24,700

24,400 24,500

Chromosome 1 position (kb) - B37

24,600 24,700

l 2<=0.25

0.5 < l 2<=0.75 0.75 < l 2

0.25 < l 2<=0.5

Figure 4 | The regional association plots of IL28RA shared locus without heterogeneity. The relative location of annotated genes and the direction of transcription are shown in the lower portion of the gure, and the chromosomal position is shown on the x axis. The blue line shows the recombination rate (estimated from HapMap data of CEU, CHB and combined population) across the region (right y axis), and the left y axis shows the signicance of the SNP associations ( log10 P). The square indicates the SNPs for conditional analysis (these are the top or secondary SNPs); the circle labelled

with rs IDs are reported psoriasis susceptibility SNPs. All circles and squares are colour lled based on the heterogeneity results (I2) in our trans-ethnic meta analysis. (a,b) Unconditional and conditional logistic association results of Caucasian, (c,d) unconditional and conditional logistic association results of Chinese, (e,f) unconditional and conditional logistic association results of trans-ancestry combined sample.

Caucasian and Chinese, which was also consistent with the nding in a recent study42. We observed the signicant allele frequency difference of some HLA risk alleles, such as HLAA*02:07 and HLA-B*07, between Caucasian and Chinese populations, which could be a result of strong selection against diverse pathogens between the two ethnic populations43.

We also observed the population-specic effects of the AA positions 114 and 144 of HLA-A as well as another 10 non-MHC loci. These population-specic effects suggest a substantial genetic heterogeneity of psoriasis susceptibility between ethnic populations, which could be a result of different evolutionary process and environmental exposures that were experienced by two populations. Such population-specic effects could also be a reection of potential clinical differences (such as clinical

subtypes and family history) between Caucasian and Chinese patients used in the current study, but such a impact should be limited due to the fact that all the Caucasian and Chinese patients used in the current study were recruited by using the same criteria. Further stratied studies, such as by clinical subtypes and family history, can help to gain better understanding on the genetic heterogeneity of psoriasis susceptibility between Caucasian and Chinese populations.

In summary, we have conducted the rst large-scale trans-ethnic GWMA in psoriasis and discovered seven novel associations, including four novel susceptibility loci and three novel independent associations within previous known loci. The new loci implicate additional players of the immune and keratinocyte development mechanism of psoriasis. In addition, we have

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8

Chinese

Caucasian

0 1 2 3 4 5

Decile

6

5

Odds ratio

7

4

3

2

1

0

6 7 8 9 10

Figure 5 | Odds ratios by the decile of polygenic risk score estimated based the top SNPs within the ten Caucasian-specic loci. The polygenic risk score (PRS) was calculated based on the ten Caucasian-specic SNPs as described (online Methods). The PRS were converted to deciles(1 lowest, 10 highest RPS), and nine dummy variables created to

contrast deciles 210 to decile 1 as the reference. Odds ratios and 95% condence intervals (error bars) were estimated using logistic regression with principal components to control for population stratication.

investigated the genetic heterogeneity of psoriasis susceptibility by directly comparing the evidences of association of all the 45 conrmed loci between Caucasian and Chinese populations. Although many loci show consistent associations between the two ethnic populations, Caucasian-specic effect was observed for ten loci whose effects contribute signicantly to the higher prevalence of psoriasis in Caucasian than in Chinese population. And the comparison of the shared loci across world populations has provided further evidence for the contribution of genetic susceptibility loci to disease prevalence difference between populations. In conclusion, our trans-ethnic genome-wide study has advanced the understanding of the genetic architecture of psoriasis susceptibility by discovering novel associations as well as revealing genetic heterogeneity across different ethnic populations.

Methods

Study subjects. The samples included in the ve GWAS (University of California (UC), The Genetic Association Information Network (GAIN), Kiel, Genizon and Anhui) were previously published15,16. The UC data set included 202 psoriatic cases and 492 controls of European ancestry. The data set used for the analyses in GAIN of GWAS stage were obtained from the database of Genotype and Phenotype at http://www.ncbi.nlm.nih.gov/gap

Web End =http://www.ncbi.nlm.nih.gov/gap through database of Genotype and Phenotype accession number (phs000019.v1.p1). The Kiel sample included 471 cases and 1,129 controls, and the Genizon data from Canada included 1,755 samples. Anhui data set was comprised of 1,139 cases and 1,112 healthy controls in Han Chinese (Table 1). KI and Singapore data sets included samples of European descent from Sweden and Han Chinese from Singapore, respectively. The Singapore study included additional controls from Singapore Prospective Study Program (SP2). Replication analysis were performed in seven independent samples, six from populations of European descent (UC, KI, Harvard, Spain, Celera, Kiel) and one from Chinese population (Anhui). Recruitment of participants for all studies was approved by the local institutional ethics review boards, Institute of Dermatology, Anhui Medical University, National Skin Centre and National

University of Singapore, Christian-Albrechts-University of Kiel, University of California San Francisco, Department of Medicine, Karolinska Institutet, Celera, Brigham & Womens Hospital, Harvard Medical School and Centre for Genomic Regulation (CRG), in adherence with the Declaration of Helsinki Principles. DNA was isolated from blood using standard methods.

Genome-wide genotyping analysis. The genotyping and the QC procedures of the ve published GWAS data sets can be found in the previous publications15,16. The cases of the Singapore GWAS data set were genotyped by using the Illumina Human550 BeadChip, whereas the controls were genotyped by using Illumina Human550, Illumina Human610 Quad and 1Mduo3 BeadChip. The cases and controls of the KI data set were genotyped by using Illumina Human Hap550 BeadChip. Standard QC procedures were applied as previously in Singapore and KI cohorts15. Briey, samples with call rate of o0.95 and SNPs with mean BeadStudio GenCall score of o0.7, call rate ofo0.95, HardyWeinberg equilibrium (HWE) P value of o1 10 6 or minor allele frequency (MAF) of

o0.01 were excluded. Cryptic relatedness between individuals was identied through a full identity-by-state matrix. Population substructure of the Singapore and KI data sets was ascertained using principal components analyses (PCAs) with the EIGENSTRAT programme44 with respect to four population panels in the HapMap samples (CEU, CHB, JPT, YRI). The nal Singapore and KI data sets consisted of 461,696 SNPs in 2,883 samples and 522,758 SNPs in 1,925 samples, respectively (Table 1).

Whole-genome imputation. Whole-genome imputation was performed by using the IMPUTE version 2 (ref. 45), and the East Asia (ASN) and Europe (EUR) haplotypes of the 1000 Genomes Project reference panel (Dec 2010 version) were used as reference for imputation of Chinese (ASN) and European (EUR) samples, respectively 46. Imputation was performed in each of the seven GWAS data sets individually and by using those SNPs that passed QC thresholds (mean call rate 40.95, MAF40.01, HWE P41 10 6 in controls). SNPs with impute

information less than 80%, MAF less than 1% and HWE P in controls less than 1 10 6 were excluded from further analysis.

To determine whether specic coding variants within HLA genes contribute to the diverse association signals, we imputed classical HLA alleles and coding variants across the HLA region (chr6:2040 Mb build 36) in each of the seven data sets. The ve European data sets were imputed using a reference panel constructed using 2,767 individuals from the MHC Working Group of the Type 1 Diabetes Genetics Consortium (T1DGC)47. The two Chinese data sets were imputed using a reference panel constructed using genotypes from 89 Chinese (CHB) and Japanese (JPT) samples from the HapMap project48. Each reference panel was constructed using dense SNP genotype data and classical HLA alleles determined to four-digit resolution. Based on the EMBL-EBI Immunogenetics HLA Database, amino-acid variants were coded in as binary markers (present/absent) in the reference panel. Imputation of classical HLA alleles and their corresponding amino acids were performed using BEAGLE as previously described47,49. A total of 253 classical HLA alleles (two- and four-digit resolution) and 357 amino-acid positions were imputed in the European samples, and 167 classical alleles and 305 amino-acid positions were imputed in the Chinese samples.

Genomic controls. We used PCA-based method to analyse and detect population stratication in individual GWAS data set. Principal component adjustments were performed in four data sets, respectively (one, one, two, three principal components (PCs) for Anhui, KI, UC and Kiel cohorts, respectively), to adjust for population stratication. This reduced the extent of genome-wide ination to o1.05 in all individual GWAS study (Table 1), a level considered acceptable by conventional GWAS standard. The cases and controls of the Singapore data set were well matched, and no PCA-based correction was performed.

The nal ination factors of the test statistics were modest (l1,000 1.017, 1.01

and 1.011 for the Caucasian, Chinese and combined discovery cohorts, respectively; Table 1), which suggested that the nal association results from the three genome-wide analysis were largely free from major ination because of population stratication.

Association analysis. Genome-wide meta analyses (GWMA) were performed in this study: (i) Caucasian data (N 8,682, 4,778,154 SNPs); (ii) Han Chinese data

(N 5,134, 4,562,294 SNPs); (iii) combined data (N 13,816, 3,621,551 SNPs).

The association analyses were done in the imputed dosage data using SNPtest v2 using score test, with the study and principal components (for four data sets) included as covariates to adjust for study effect and population stratication (Table 1). We also applied DerSimonian-Laid random effect model to detect ethnic heterogeneity by using odds ratio estimates and standard errors from GWMA of Caucasian and GWMA of Chinese data as the inputs. Association evidence from replication study was tested using an inverse variance meta-analysis assuming xed-effects, with a Cochrans Q test and I2 to assess between-study heterogeneity. A Po5 10 8 was used as threshold for genome-wide signicance.

In HLA region, association tests were performed on genotype dosages as determined from the imputed genotype probabilities, which would account for any uncertainties in the imputed genotypes. We conducted Wald tests on all the

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imputed bi-allelic variants, adjusting for study variables and principal components as covariates. For multiallelic amino-acid sites, we performed the global omnibus test at each site taking into account all alleles at each multiallelic position. The omnibus test was performed as previously described50, in summary: convert k-alleles to k-1 bi-alleles, invoke the glm function in R to estimate the multivariate model, and use the likelihood ratio test to compute the global multiallelic test P-values. All conditional logistic regression analyses were done using R.

SNP selection and replication. We selected 43 SNPs with suggestive evidence of association (Po5 10 5 from the single variant logistic regression analysis) in at

least one meta-analysis (Caucasian, Chinese and Combined) for the independent validation analysis. The Sequenom MassArray system was used for most of the replication studies, except for Celera samples, where 30 SNPs were genotyped using TaqMan assays (Life Technologies), and Han Chinese samples, where 12 SNPs were genotyped using TaqMan assays.

Conditional analysis. To detect novel secondary association signals, we performed conditional analysis in 45 regions (41 known GWAS loci and 4 novel loci). Secondary SNPs with conditional Po5 10 8 was then assumed to be independent

from the lead SNP in this region. The P values in multivariate analysis were reported after adjusting for the leading SNPs.

Functional annotation and eQTL analysis. We annotated the four novel SNPs and their linked SNPs in 1000 Genomes Project Phase 1 CEU panel (r240.8) using HaploReg 2 (http://www.broadinstitute.org/mammals/haploreg/haploreg.php

Web End =http://www.broadinstitute.org/mammals/haploreg/haploreg.php). We further performed their eQTL analyses using the following recently published eQTL data sets: (i) a meta-analysis of the transcriptional proles from the peripheral blood cells of 5,311 Europeans (http://genenetwork.nl/bloodeqtlbrowser

Web End =http://genenetwork.nl/bloodeqtlbrowser) 51;(ii) the transcriptional prole from primary B cells and monocytes in 288 healthy Europeans52; (iii) the eqtl.uchicago browser with compiled data across diverse tissues.

GO enrichment analysis. We submitted the 54 implicated genes (Supplementary Data 1) to the Database for Annotation, Visualization and Integrated Discovery (DAVID)53 for GO term enrichment analysis based on GO level. Fishers exact test was implemented in DAVID to compute the enrichment P value for each GO term. We excluded the HLA region in this analysis. And we only reported the signicant results at Po10 4.

Ethnic heterogeneity. To compare the ethnic heterogeneity of susceptibility in psoriasis, we selected from the 44 non-MHC conrmed SNPs or proxy SNPs with considerate LD for validation mostly in our GWAS samples. Based on the comparisons between reference allele frequency and the association results, ethnic heterogeneity was categorized into three types: (i) loci with common variants, in which the same susceptible signals existed in Caucasian and Chinese Han; (ii) loci ethnic specic, in which there was signicant association evidence only in one specic race (locus heterogeneity); (iii) Loci with allelic heterogeneity, in which the association in diverse populations showed obvious different causal variants.

Geospatial risk analysis for 27 shared loci (30 SNPs) on HGDP. To assess the geospatial pattern of genetic risk, we used the publicly available genotype data from the HGDP with 1,043 individuals of 52 worldwide populations. The genotypes were generated on Illumina 650Y arrays for 660,918 markers (Stanford University). This high-quality genotype data (overall sample call rate 498.5%) was available for 10 out of 30 shared SNPs. The QC had been done before imputing the missing genotyped SNPs. Eighty-eight individuals were removed because of low heterozygosity rate and rst-degree relatives and markers excluded with low call rate (o95%) and MAF (o0.01). Using the remaining data set with 955 individuals (52 populations) and 630,559 markers, the whole-genome imputation was performed using 1000 Genome Projects data set (Phase I, Dec. 2013 version) as reference panel. All 30 SNPs were imputed with high condence (impute info 0.90 or above), and MAF bigger than 0.05.

For each individual (j), the genetic risk score of multiple locus (m) was estimated by

Xm

i1

^

bijGij 1

where ^

bij was the log of adjusted meta-OR for each locus, Gij was the number of risk alleles at each locus. The standardized risk score was calculated using a z-score method across populations by

Standardized risk score

3

Where KCA was the overall prevalence in Caucasian and KCH the overall prevalence in Chinese.

Co-expression-driven gene functional prediction. We used a described method to shed insight into the putative functions of the biological implicated genes in our study (http://genenetwork.nl:8080/GeneNetwork/

Web End =http://genenetwork.nl:8080/GeneNetwork/) 56. Gene function prediction is based on the idea that genes with shared expression proles are likely to have related biological functions. The method uses data on co-expression proles to predict the likely functions of as-of-yet uncharacterized genes and rene our understanding of the function of other genes. To apply the method, we queried the co-expression database with our four novel genes. The query for each gene returned the probable function of the gene or the reconstituted pathway in which it operates. The database was generated by linking information about gene expression obtained from published data on approximately 80,000 gene expression proles (from the database Gene Expression Omnibus).

References

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2. Chandran, V. & Raychaudhuri, S. P. Geoepidemiology and environmental factors of psoriasis and psoriatic arthritis. J. Autoimmun. 34, J314J321 (2010).

3. Ding, X. et al. Prevalence of psoriasis in China: a population-based study in six cities. Eur. J. Dermatol. 22, 663667 (2012).

4. Gelfand, J. M. et al. The prevalence of psoriasis in African Americans: results from a population-based study. J. Am. Acad. Dermatol. 52, 2326 (2005).5. Christophers, E. Explaining phenotype heterogeneity in patients with psoriasis. Br. J. Dermatol. 158, 437441 (2008).

6. Heredia, E. E. et al. Heterogeneity of response to biologic treatment: perspective for psoriasis. J. Invest. Dermatol. (2013).

7. Zhang, X., Wang, H., Te-Shao, H., Yang, S. & Chen, S. The genetic epidemiology of psoriasis vulgaris in Chinese Han. Int. J. Dermatol. 41, 663669 (2002).

8. de Cid, R. et al. Deletion of the late cornied envelope LCE3B and LCE3C genes as a susceptibility factor for psoriasis. Nature Genet. 41, 211215 (2009).

9. Ellinghaus, E. et al. Genome-wide association study identies a psoriasis susceptibility locus at TRAF3IP2. Nature Genet. 42, 991995 (2010).10. Huffmeier, U. et al. Common variants at TRAF3IP2 are associated with susceptibility to psoriatic arthritis and psoriasis. Nature Genet. 42, 996999 (2010).

11. Nair, R. P. et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappaB pathways. Nature Genet. 41, 199204 (2009).

Individual risk score-Worldwide meanWorldwide standard deviation 2

The median of standardized risk score was compared among 52 worldwide populations by KruskalWallis test. The standardized risk score was correlated with longitude, latitude of the population.

Odds ratio by risk score prole of ten population-specic loci. For this purpose, the polygenic risk score (PRS) for each individual was calculated as counts of risk alleles of ten population-specic loci54. The OR was estimated using logistic regression on PRS with adjustment of PCs to control for population stratication. There are seven cohorts (two for Chinese and ve for Caucasian) in the discovery stage of this study. The adjusted ORs were estimated individually for seven cohorts. Meta-analysis was performed separately in Chinese and Caucasian using PLINK v1.07.

To analyse the ORs by risk score prole, the PRS was converted to deciles (1 lowest, 10 highest PRS). Nine dummy variables were created to compare

210 to decile 1 as the reference. ORs by deciles were estimated by logistic regression with PCs adjusted. The ORs were combined separately in Chinese and Caucasian using META v1.5 with xed effects model. The 95% condence intervals were calculated by exp(BETA1.96*SE).

Difference in prevalence between Chinese and Caucasian accounted for by ten population-specic loci. The method was modied based on Supplementary

Methods published by William et al.55. We modelled the psoriasis prevalence accounted for by the ten Caucasian-specic loci in both Caucasian and Han Chinese and determined the amount of psoriasis prevalence would be reduced if the ten SNPs were absent from each population. The psoriasis prevalence was about ten times higher in Caucasian (2.5%) than in Chinese (0.2%). So we calculated estimates of reduction relative to the impact sum of the ten SNPs on Chinese prevalence. We applied a standard log-additive effect model and used population-based ORs from either Chinese (OR 1) or Caucasian (ORs from the

current study) and assumed the OR was a good estimate of relative risk, R. We also assumed these ten loci contributed to the risk of psoriasis independently, and they exerted additive risk effect in total. Then, the overall psoriasis prevalence in population P was modelled as: KP KPA Qmi1 pi qiRi

2, where KPA was the

disease prevalence if all loci were absent from the population, m was the number of loci, pi was the frequency of non-risk allele for ith loci, qi the frequency of risk allele for ith loci. We then calculated the proportion of prevalence in difference in Caucasian (CA) and Chinese (CH) accounted for by ten loci by

KCA KCAA

KCH KCHA

KCA KCH

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Acknowledgements

We thank all the volunteers who took part in this work. We also acknowledge efforts of Helen Kuo, Patrick Dominguez, Mary ODonnell, Rebecca Briskin, Irene Wood, Lotus Mallbris, Portia Chipendo and Yonghu Sun. This study was funded by the Key Project of Natural Science Foundation of China (81130031, 30530670), National Basic Research Program of China (973 Plan) (2012CB722404, 2007B516801), Anhui Provincial Special Scientic Program (2007-7), the National Science Fund for Excellent Young Scholars (81222022), the Outstanding Talents of Organization Department of the CPC (Communist Party of China) Central Committee Program, the Local Universities Characteristics and Advantages of Discipline Development Program of the Ministry of Finance of China, General Program of National Natural Science Foundation of China (81370044, 81000692,81172866, 30771196, 30800990, 81072461), High-Tech Research and Development Program of China (863 Plan) (2007AA02Z161), Anhui High Education Youth Talent Fund (X. Yin), Anhui Medical University PhD Fund (XJ201429) and the Agency for Science, Technology and Research of Singapore. Funding for the US contribution was provided in part by grants from the National Institutes of Health to W.L. (R01AR065174 and K08AR057763) and A.M.B. (R01AR050266). We thank the National Psoriasis Foundation for provision of US replication samples. The contribution from Sweden was funded through the Swedish Medical Research Council, the Psoriasis Association and Karolinska Institutet. The Psoriatic Arthritis and Psoriasis and Follow-up Study was supported in part by a grant from the Department of Dermatology, BWH; and the BWH PhenoGenetic project was supported in part by a grant from the Brigham Research Institute. P.L.D. is a Harry Weaver Neuroscience Scholar of National Multiple Sclerosis Society. Genotyping of samples in GAIN of GWAS stage was provided through the Genetic Association Information Network. Samples and associated phenotype data for the Collaborative Association Study of Psoriasis were provided by Gonalo R. Abecasis, James T. Elder, Anne M. Bowcock, Gerald G. Krueger. The project received infrastructure support through the DFG Clusters of Excellence Inammation at Interfaces (grants EXC306 and EXC306/2), and was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (sysINFLAME, grant # 01ZX1306A), and the PopGen 2.0 network (01EY1103). The research of Xavier Estivill is supported by the Generalitat de Catalunya AGAUR 2014 SGR-1138.

Author contributions

J.J.L., X.J.Z., M. Seielstad, W.L., M. Sthle and A.F. organized and designed the study. J.J.L., X.J.Z., M. Seielstad, W.L., M. Sthle and A.F. conducted and supervised the genotyping of samples. X.Y.Y., H.Q.L., L.W., J.N.F, Y.L., P.D.B. and J.J.L. contributed to the design and execution of statistical analyses. X.Y.Y., H.Q.L., L.W. and J.J.L. wrote the rst draft of the manuscript. Y.H.L., E.E., J.L.H., X.E., L.D.S., X.B.Z., C.B.S., C.H.Z., A.P.Z., F.S., L.P., J.J.C., G.G.K., K.P.C., S.M., M.W., S.W., W.L., K.S., Y.Y.T., C.T.S.T., R.G., A.B.,

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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7916 ARTICLE

How to cite this article: Yin, X. et al. Genome-wide meta-analysis identies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility. Nat. Commun. 6:6916 doi: 10.1038/ncomms7916 (2015).

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P.L.D.J. and A.A.Q. contributed samples to the GWAS and/or follow-up genotyping. All authors contributed to the writing of the manuscript.

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