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Genome-Wide Pathway Analysis Identifies Genetic Pathways Associated with Psoriasis

Open ArchivePublished:December 29, 2015DOI:https://doi.org/10.1016/j.jid.2015.11.026
      Psoriasis is a chronic inflammatory disease with a complex genetic architecture. To date, the psoriasis heritability is only partially explained. However, there is increasing evidence that the missing heritability in psoriasis could be explained by multiple genetic variants of low effect size from common genetic pathways. The objective of this study was to identify new genetic variation associated with psoriasis risk at the pathway level. We genotyped 598,258 single nucleotide polymorphisms in a discovery cohort of 2,281 case-control individuals from Spain. We performed a genome-wide pathway analysis using 1,053 reference biological pathways. A total of 14 genetic pathways (PFDR ≤ 2.55 × 10–2) were found to be significantly associated with psoriasis risk. Using an independent validation cohort of 7,353 individuals from the UK, a total of 6 genetic pathways were significantly replicated (PFDR ≤ 3.46 × 10–2). We found genetic pathways that had not been previously associated with psoriasis risk such as retinol metabolism (Pcombined = 1.84 × 10–4), the transport of inorganic ions and amino acids (Pcombined = 1.57 × 10–7), and post-translational protein modification (Pcombined = 1.57 × 10–7). In the latter pathway, MGAT5 showed a strong network centrality, and its association with psoriasis risk was further validated in an additional case-control cohort of 3,429 individuals (P < 0.05). These findings provide insights into the biological mechanisms associated with psoriasis susceptibility.

      Abbreviations:

      BC (betweenness centrality), DC (degree centrality), FDR (false discovery rate), GWAS (genome-wide association studies), SNP (single nucleotide polymorphism)

      Introduction

      Psoriasis is a common chronic inflammatory disease of the skin that affects approximately 2% of the worldwide population (
      • Nestle F.O.
      • Kaplan D.H.
      • Barker J.
      Psoriasis.
      ). In psoriasis, immune cells infiltrate the skin leading to an increased proliferation of keratinocytes (
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      • Burack L.
      • Pope M.
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      CD69, HLA-DR and the IL-2R identify persistently activated T cells in psoriasis vulgaris lesional skin: blood and skin comparisons by flow cytometry.
      ,
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      Psoriasis: epidemiology.
      ). It is a genetically complex disease with a complex mode of inheritance (
      • Vyse T.J.
      • Todd J.A.
      Genetic analysis of autoimmune disease.
      ). HLA class I gene HLA-C*0602 haplotype association explains the largest part of the known heritability of psoriasis (
      • Nair R.P.
      • Stuart P.E.
      • Nistor I.
      • et al.
      Sequence and haplotype analysis supports HLA-C as the psoriasis susceptibility 1 gene.
      ,
      • Strange A.
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      • Spencer C.C.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ).
      Genome-wide association studies (GWAS) have been successful in the characterization of the genetic architecture of many complex human diseases (
      • Manolio T.A.
      Genomewide association studies and assessment of the risk of disease.
      ). To date, more than 15 GWAS have been performed using large psoriasis cohorts from Caucasian and Asian populations and have collectively identified more than 50 susceptibility loci for psoriasis (
      • Bowes J.
      • Budu-Aggrey A.
      • Huffmeier U.
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      Dense genotyping of immune-related susceptibility loci reveals new insights into the genetics of psoriatic arthritis.
      ,
      • Tsoi L.C.
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      • Ellinghaus E.
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      Enhanced meta-analysis and replication studies identify five new psoriasis susceptibility loci.
      ,
      • Yin X.
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      Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility.
      ,
      • Zuo X.
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      Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis.
      ). Despite progress in characterizing psoriasis genetic etiology, loci outside the HLA region only explain less than 25% of the estimated psoriasis heritability (
      • Tsoi L.C.
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      • Knight J.
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      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ,
      • Yin X.
      • Wineinger N.E.
      • Cheng H.
      • et al.
      Common variants explain a large fraction of the variability in the liability to psoriasis in a Han Chinese population.
      ).
      Recent research has shown that the missing heritability of complex human diseases can be explained by common genetic variants, rare variants or a combination of genetic, epigenetic, and environmental interactions (
      • Gibson G.
      Rare and common variants: twenty arguments.
      ). From these, common genetic variants could explain more than 60% of the heritability of the most prevalent autoimmune diseases (
      • Golan D.
      • Lander E.S.
      • Rosset S.
      Measuring missing heritability: inferring the contribution of common variants.
      ). Importantly, most of these common genetic variants are characterized by having low effect sizes (
      • Park J.H.
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      • Gail M.H.
      • et al.
      Estimation of effect size distribution from genome-wide association studies and implications for future discoveries.
      ).
      Although GWAS based on single markers have successfully identified disease-susceptibility variants, this strategy is not adequate to identify genetic variants with low effect sizes that are genuinely associated with disease risk (
      • Du Y.
      • Xie J.
      • Chang W.
      • et al.
      Genome-wide association studies: inherent limitations and future challenges.
      ). In single-marker GWAS, a large number of genetic variants are tested for association with a complex trait. To avoid false positive results, a stringent genome-wide significant threshold must be used (
      • Johnson R.C.
      • Nelson G.W.
      • Troyer J.L.
      • et al.
      Accounting for multiple comparisons in a genome-wide association study (GWAS).
      ). This conservative threshold, however, does not allow the identification of modest effect risk loci, unless extremely large samples sizes of cases and controls are used (
      • Wang K.
      • Li M.
      • Hakonarson H.
      Analysing biological pathways in genome-wide association studies.
      ). Importantly, single-marker GWAS consider only the individual effect of each single nucleotide polymorphism and ignore the joint effect of multiple causal genetic variants as well as the biological context where disease genes operate (
      • Zhang K.
      • Cui S.
      • Chang S.
      • et al.
      i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study.
      ).
      Functionally related genes have been shown to collectively contribute to disease susceptibility, including those loci that do not reach individually the genome-wide significant threshold (
      • Zhong H.
      • Yang X.
      • Kaplan L.M.
      • et al.
      Integrating pathway analysis and genetics of gene expression for genome-wide association studies.
      ). Recently, new methods that are able to analyze genetic associations at the pathway level have been developed (
      • Gui H.
      • Li M.
      • Sham P.C.
      • et al.
      Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset.
      ). Pathway-based approaches are robust statistical methodologies that integrate genetic and biological knowledge to test whether sets of functionally related genes are jointly associated with a complex trait (
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      • Shen L.
      • Moore J.H.
      • et al.
      Pathway analysis of genomic data: concepts, methods, and prospects for future development.
      ). Therefore, pathway-based methods increase the statistical power of the association analysis by reducing the number of association tests that must be performed and allow a functional interpretation of the results (
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      Powerful SNP-set analysis for case-control genome-wide association studies.
      ).
      Pathway-based analyses have been recently performed to study the genetic basis of cancer subtypes using either selected candidate pathways, but also at a genome-wide scale (
      • Chen D.
      • Enroth S.
      • Ivansson E.
      • et al.
      Pathway analysis of cervical cancer genome-wide association study highlights the MHC region and pathways involved in response to infection.
      ,
      • Koster R.
      • Mitra N.
      • D'Andrea K.
      • et al.
      Pathway-based analysis of GWAs data identifies association of sex determination genes with susceptibility to testicular germ cell tumors.
      ). Although the genome-wide pathway analysis can have a high computational cost, this approach is able to identify novel genetic pathways associated with disease risk. The identification of new pathways associated with disease risk could increase the probability to develop new therapeutic strategies in complex diseases such as psoriasis. To date, however, the genome-wide pathway analysis approach has not been performed in psoriasis.
      To gain a better understanding of the genetic risk basis of psoriasis, we performed a genome-wide pathway analysis on a large multicenter cohort of patients with psoriasis. In this study, we analyzed the association of 1,053 reference biological pathways using 1,263 patients with psoriasis and 1,558 controls from Spain. Using an independent cohort of 2,178 cases and 5,175 controls from the UK, we then performed a validation study of the significantly associated pathways in the discovery cohort. With this approach, we identified genetic pathways that had not been previously associated with psoriasis risk such as retinol metabolism, transport of inorganic ions and amino acids, and post-translational protein modification. These results provide important insights into the genetic etiology of psoriasis.

      Results

      Identification of genetic pathways associated with psoriasis risk

      In the discovery stage, the genome-wide pathway analysis identified a total of 26 genetic pathways significantly associated with psoriasis risk after multiple test correction (PFDR < 0.05, Supplementary Table S1 online). The complete results of the genome-wide pathway analysis performed in the discovery study are shown in Supplementary Table S2 online.
      From the 26 significantly associated pathways, we found that 14 pathways included IL12B gene. After HLA-C*0602, IL12B is one of the strongest known genetic risk factors for psoriasis. To confirm that the observed pathway associations were the result of the joint effect of multiple genes and not the result of a single risk locus strongly associated with the disease, we removed IL12B from these genetic pathways and tested again for association. After extracting IL12B, two genetic pathways—“Inflammatory response” and “Natural killer T cell”—remained significantly associated with psoriasis risk (PFDR < 0.05). Consequently, only these two pathways from the group containing IL12B gene were selected for replication. Together with the other 12 pathways, a total of 14 different genetic pathways were finally tested for validation in the UK population. Using this independent case-control cohort we significantly validated the association of 9 genetic pathways with psoriasis risk (PFDR < 0.05, Table 1).
      Table 1Pathways associated with psoriasis risk and validated in the replication stage
      PathwayDatabaseGenesSNPsD
      Number of single nucleotide polymorphisms mapping to a particular pathway.
      PDFDRDPDEFDRDESNPsR
      Number of single nucleotide polymorphisms mapping to a particular pathway.
      PRFDRRPREFDRREPC
      Inflammatory response
      Increased permutations to refine the P-value (n = 10,000,000).
      Biocarta29628<9.99 × 10–85.25 × 10–51.35 × 10–24.71 × 10–2606<3.33 × 10–75.77 × 10–71.53 × 10–21.58 × 10–21.06 × 10–12
      Natural killer T cell
      Increased permutations to refine the P-value (n = 10,000,000).
      Biocarta29638<9.99 × 10–85.25 × 10–51.17 × 10–24.71 × 10–2603<3.33 × 10–75.77 × 10–71.59 × 10–21.59 × 10–21.06 × 10–12
      DNA repair
      Increased permutations to refine the P-value (n = 10,000,000).
      Reactome1122,0501.33 × 10–49.36 × 10–31,962<3.33 × 10–75.77 × 10–71.10 × 10–9
      Amino acid transport across the plasma membraneReactome311,0252.00 × 10–41.24 × 10–29934.00 × 10–55.63 × 10–51.57 × 10–7
      Post-translational protein modificationReactome1885,9652.00 × 10–41.24 × 10–25,7254.00 × 10–55.63 × 10–51.57 × 10–7
      Transport to the Golgi and subsequent modificationReactome331,5573.33 × 10–41.95 × 10–21,5163.20 × 10–34.38 × 10–31.57 × 10–5
      Asparagine N-linked glycosylationReactome812,7604.00 × 10–42.11 × 10–22,6398.67 × 10–31.13 × 10–24.72 × 10–5
      Transport of inorganic ions and amino acidsReactome944,0104.00 × 10–42.11 × 10–23,8722.00 × 10–53.25 × 10–51.57 × 10–7
      Retinol metabolismKEGG641,5125.33 × 10–42.55 × 10–21,4062.79 × 10–23.46 × 10–21.84 × 10–4
      Abbreviations: C, combined; D, discovery cohort; E, exclusion IL12B gene; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; P, empirical set-based P-value; R, replication cohort.
      1 Number of single nucleotide polymorphisms mapping to a particular pathway.
      2 Increased permutations to refine the P-value (n = 10,000,000).

      Characterization of the genetic pathways associated with psoriasis risk

      To discard the presence of redundant pathways, we evaluated the level of gene overlap between all associated pathways. From the nine validated genetic pathways, we found that the “Amino acid transport across the plasma membrane” and “Transport of inorganic ions and amino acids” pathways, as well as the “Asparagine N-linked glycosylation,” “Transport to the Golgi and subsequent modification,” and “Post-translational protein modification” pathways had a high degree of overlap between them (>95% of shared genes, Figure 1a). Consequently, and to avoid redundancy, only the pathway showing the highest level of significance was selected to represent each biological process. The “Transport of inorganic ions and amino acids” (Pcombined = 1.57 × 10–7, Figure 1b) and “Post-translational protein modification” (Pcombined = 1.57 × 10–7, Figure 1c) pathways were therefore selected from each overlapping pathway group. The “Inflammatory response” (Pcombined = 1.06 × 10–12), “Natural killer T cell” (Pcombined = 1.06 × 10–12), “DNA repair” (Pcombined = 1.10 × 10–9), and “Retinol metabolism” (Pcombined = 1.84 × 10–4) pathways did not show a significant degree of overlap and were therefore considered as independent biological processes.
      Figure 1
      Figure 1Gene overlap of genetic pathways associated with psoriasis risk. (a) Heat map representing the percentage of genes that are shared between each pathway pair. (b) Venn diagram of the overlapping pathways representing the transport of inorganic ions and amino acids process as well as the number of genes shared between them. (c) Venn diagram of the overlapping pathways representing the post-translational protein modification process as well as the number of genes shared between them.
      Within the final group of six genetic pathways associated with disease risk and representing independent biological processes, we analyzed the association between each particular gene and psoriasis risk (Table 2). We found 37 small-effect genes that were nominally associated with psoriasis risk both in the discovery and replication cohorts (P ≤ 1.29 × 10–2, Table 3). The complete list of genetic associations obtained from each genetic pathway is shown in Supplementary Table S3 online. The linkage disequilibrium pattern between the SNPs mapping to each genetic pathway associated with psoriasis risk is shown in Supplementary Figure S1 online.
      Table 2Association results of the top five genes involved in each pathway associated with psoriasis risk
      Pathway
      The detailed description of the “Inflammatory response” and “Natural killer T cell” pathways corresponds to the association results after excluding the IL12B gene from the genome-wide pathway analysis.
      DatabaseSNPDCOORDA1A2ORDPDGeneDSNPRCOORDA1A2ORRPRGeneR
      Inflammatory responseBiocartars205415:131995964AG0.724.18 × 10–5IL13,IL4rs29650121:218786549AC0.837.56 × 10–4TGFB2
      rs117396235:131864152AG1.211.79 × 10–3IL5rs22431233:159709651GA1.141.06 × 10–3IL12A
      rs27990831:218581617GA1.222.82 × 10–3TGFB2rs258905:131437562GA0.881.09 × 10–3CSF2
      rs23664083:159696099AC1.193.35 × 10–3IL12Ars205415:131995964AG0.862.41 × 10–3IL13,IL4
      rs20698377:22768027GA1.333.93 × 10–3IL6rs496351712:6947800AG0.902.94 × 10–3CD4
      Natural killer T cellBiocartars205415:131995964AG0.724.18 × 10–5IL4rs42972651:67852335GA0.834.01 × 10–7IL12RB2
      rs117396235:131864152AG1.211.79 × 10–3IL5rs7498732:136817088GA0.842.61 × 10–5CXCR4
      rs27990831:218581617GA1.222.82 × 10–3TGFB2rs29650121:218786549AC0.837.56 × 10–4TGFB2
      rs21148082:137249556GA0.813.09 × 10–3CXCR4rs22431233:159709651GA1.141.06 × 10–3IL12A
      rs23664083:159696099AC1.193.35 × 10–3IL12Ars258905:131437562GA0.881.09 × 10–3CSF2
      Retinol metabolismKEGGrs21732014:100250970AC0.775.82 × 10–5ADH1C,ADH1Brs718892316:81336356AG0.891.81 × 10–3BCMO1
      rs41482954:70475866CA1.233.41 × 10–4UGT2A1rs1088214410:94852448AG0.872.55 × 10–3CYP26A1
      rs176149394:70360229GA0.785.21 × 10–4UGT2B4rs431954612:57346828AG0.894.96 × 10–3RDH16
      rs227934519:41515702AG0.842.44 × 10–3CYP2B6rs44057882:72235688AG0.905.48 × 10–3CYP26B1
      rs178646862:234591339AG1.253.37 × 10–3UGT1A8rs1167076019:41336795GA1.125.73 × 10–3CYP2A6
      DNA repairReactomers2409566:111616051AC1.463.16 × 10–6REV3Lrs4580176:111696091GA1.651.40 × 10–13REV3L
      rs205415:131995964AG0.724.18 × 10–5RAD50rs22401169:35094373AG1.365.22 × 10–4FANCG
      rs22131788:48816716AG1.296.11 × 10–5PRKDCrs709912010:131015367AG1.159.51 × 10–4MGMT
      rs298568914:50098031CA1.281.66 × 10–3POLE2rs378381914:61316264AG0.891.13 × 10–3MNAT1
      rs188718110:131594850GA1.461.86 × 10–3MGMTrs116937312:58887650AG0.891.13 × 10–3FANCL
      Post-translational protein modificationReactomers10071081:26104973AG1.432.74 × 10–6MAN1C1rs98863027:70751484AG0.817.29 × 10–6WBSCR17
      rs108653312:62551472AG1.257.88 × 10–5B3GNT2rs722046417:7210836AC0.852.09 × 10–5EIF5A
      rs37913122:135183045GA0.718.04 × 10–5MGAT5rs45289323:118941441AG1.175.28 × 10–5B4GALT4
      rs14950868:15378013AG0.781.02 × 10–4TUSC3rs77804617:151641016AG1.248.26 × 10–5GALNTL5
      rs9779053:5882683GA1.241.69 × 10–4EDEM1rs1226271810:17343706AG1.318.68 × 10–5ST8SIA6
      Transport of inorganic ions and amino acidsReactomers126617046:111560890AG1.602.34 × 10–7SLC16A10rs126617046:111560890AG1.422.38 × 10–9SLC16A10
      rs102054022:40710953AG0.778.78 × 10–6SLC8A1rs23858442:220839453GA0.859.23 × 10–6SLC4A3
      rs53223720:48467560GC1.311.09 × 10–4SLC9A8rs601275020:48430680AG0.866.81 × 10–5SLC9A8
      rs53838513:30229665GA0.826.91 × 10–4SLC7A1rs18743611:205908186AC1.151.63 × 10–4SLC26A9
      rs170504414:139402774GA1.271.14 × 10–3SLC7A11rs1166887819:47268373AC1.271.65 × 10–4SLC1A5
      Abbreviations: A1, minor allele; A2, major allele; COORD, SNP coordinates in build GRCh37/hg19; D, discovery cohort; KEGG, Kyoto Encyclopedia of Genes and Genomes; OR, odds ratio; P, P-value; R, replication cohort.
      1 The detailed description of the “Inflammatory response” and “Natural killer T cell” pathways corresponds to the association results after excluding the IL12B gene from the genome-wide pathway analysis.
      Table 3Genes associated with psoriasis risk in the discovery and replication stages for each validated pathway
      Pathway
      The detailed description of the “Inflammatory response” and “Natural killer T cell” pathways corresponds to the association results before excluding the IL12B gene from the genome-wide pathway analysis.
      DatabaseGene
      Genes contained in the genetic pathways that were nominally associated with psoriasis risk in the discovery and replication stages.
      PDPR
      Inflammatory responseBiocartaIL12A3.35 × 10–31.06 × 10–3
      IL12B3.02 × 10–101.69 × 10–18
      IL134.18 × 10–52.41 × 10–3
      IL44.18 × 10–52.41 × 10–3
      TGFB22.82 × 10–37.56 × 10–4
      Natural killer T cellBiocartaCXCR43.09 × 10–32.61 × 10–5
      IL12A3.35 × 10–31.06 × 10–3
      IL12B3.02 × 10–101.69 × 10–18
      IL44.18 × 10–52.41 × 10–3
      IL4R7.35 × 10–31.37 × 10–3
      TGFB22.82 × 10–37.56 × 10–4
      Retinol metabolismKEGGADH1B5.82 × 10–51.21 × 10–2
      UGT2B45.21 × 10–46.13 × 10–3
      RPE655.10 × 10–37.34 × 10–3
      DNA repairReactomeFANCL3.22 × 10–31.13 × 10–3
      MGMT1.86 × 10–39.51 × 10–4
      RAD504.18 × 10–52.41 × 10–3
      REV3L3.16 × 10–61.40 × 10–13
      RFC32.14 × 10–31.94 × 10–3
      Transport of inorganic ions and amino acidsReactomeSLC16A102.34 × 10–72.38 × 10–9
      SLC1A42.04 × 10–37.44 × 10–3
      SLC38A11.34 × 10–39.44 × 10–3
      SLC43A21.29 × 10–21.15 × 10–2
      SLC7A16.91 × 10–46.62 × 10–3
      SLC7A111.14 × 10–36.22 × 10–3
      SLC7A75.09 × 10–32.77 × 10–3
      SLC8A18.75 × 10–61.30 × 10–3
      SLC9A81.09 × 10–46.81 × 10–5
      SLC9A94.37 × 10–33.22 × 10–3
      Post-translational protein modificationReactomeALG103.89 × 10–34.15 × 10–3
      B3GNT27.88 × 10–51.01 × 10–3
      EDEM11.69 × 10–45.81 × 10–4
      EIF5A3.42 × 10–42.09 × 10–5
      FUT85.30 × 10–31.87 × 10–4
      GALNT15.22 × 10–49.72 × 10–4
      MAN1A12.82 × 10–41.04 × 10–2
      MAN2A17.29 × 10–33.53 × 10–3
      MGAT58.04 × 10–59.34 × 10–3
      SEMA6D2.06 × 10–31.46 × 10–3
      ST8SIA68.17 × 10–38.68 × 10–5
      TUSC31.02 × 10–46.07 × 10–3
      Abbreviations: D, discovery cohort; KEGG, Kyoto Encyclopedia of Genes and Genomes; OR, odds ratio; P, P-value; R, replication cohort.
      1 The detailed description of the “Inflammatory response” and “Natural killer T cell” pathways corresponds to the association results before excluding the IL12B gene from the genome-wide pathway analysis.
      2 Genes contained in the genetic pathways that were nominally associated with psoriasis risk in the discovery and replication stages.

      Functional-based networks associated with psoriasis risk

      To understand the relevance of each particular gene within the genetic pathway associated with psoriasis risk, we used biological knowledge to build the associated functional-based network (Figure 2). Using known or predicted functional associations between the pathway genes, functional-based networks are a powerful approach to represent and analyze the topological structure of a biologic pathway.
      Figure 2
      Figure 2Functional-based network of each genetic pathway associated with psoriasis risk. (a) “Inflammatory response.” (b) “Natural killer T cell.” (c) “Retinol metabolism.” (d) “DNA repair.” (e) Transport of inorganic ions and amino acids.” (f) Post-translational protein modification.” The color of each gene represents the P-value of its association with psoriasis in the negative logarithmic scale, ranging from the lowest significance (green) to the strongest (red). The gene shape represents the association with the disease in neither the discovery nor the replication study (square), only in either the discovery or the replication study (circle) and the association found in both discovery and replication studies (rhombus). The edge width is proportional to the confidence of the functional association between two genes. Disconnected genes are hidden.
      To characterize the network properties of the resulting functional-based networks, we determined the betweenness centrality (BC) and degree centrality (DC) statistics (Supplementary Table S4 online). These two measures are useful to identify those network elements (genes in this case) that are likely to be more influential in the structure of the network. BC and DC have been widely used to identify the genes that are more likely to be essential for pathway functionality (
      • Hahn M.W.
      • Kern A.D.
      Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks.
      ,
      • Joy M.P.
      • Brock A.
      • Ingber D.E.
      • et al.
      High-betweenness proteins in the yeast protein interaction network.
      ,
      • Vallabhajosyula R.R.
      • Chakravarti D.
      • Lutfeali S.
      • et al.
      Identifying hubs in protein interaction networks.
      ). We found that SLC7A11 from the “Transport of inorganic ions and amino acids” pathway and MGAT5 from the “Post-translational protein modification” pathway had markedly high BC values (BC ≥ 0.1). From these, MGAT5 gene also showed a much stronger DC value than SLC7A11 (DCMGAT5 = 19, DCSLC7A11 = 3).
      Given the strong network centrality properties found for MGAT5 gene in the “Post-translational protein modification” pathway, we decided to further test the association of this key gene with psoriasis risk in an independent case-control cohort. Using this additional replication cohort, we significantly validated the association of MGAT5 gene with psoriasis risk (P = 1.3 × 10–2; odds ratio [95% confidence interval] = 0.85 [0.74–0.96]).

      Functional analysis of MGAT5 variation

      MGAT5 encodes for a key enzyme in the N-glycosylation pathway, a post-translational process that is directly implicated in T-cell activation and differentiation (
      • Demetriou M.
      • Granovsky M.
      • Quaggin S.
      • et al.
      Negative regulation of T-cell activation and autoimmunity by Mgat5 N-glycosylation.
      ). To assess the functional role of MGAT5 in psoriasis pathogenesis, we evaluated the association between genetic variation at MGAT5 gene and the levels of T-cell surface glycosylation. Flow cytometry analysis of in vitro activated CD4+ and CD8+ T cells obtained from 27 patients with psoriasis showed an increase in N-glycosylation levels in patients carrying one or two copies of the protective allele (G) compared with homozygous individuals for the risk allele (A) (Figure 3). The increased glycosylation levels in individuals carrying at least one copy of (G) allele was observed both in activated CD8+ and CD4+ T cells. In CD4+ T lymphocytes, the glycosylation level was significantly higher in GG homozygotes compared with AA homozygotes (P = 0.01, Figure 3).
      Figure 3
      Figure 3N-Glycosylation on activated T lymphocytes according to MGAT5 genotype. Boxplots of mean fluorescence intensity (MFI) of cell membrane glycosylation of in vitro activated CD4+ (left) and CD8+ (right) T cells from patients with psoriasis. Patients with one and two copies of the protective (G) allele of MGAT5 SNP rs3791318 tend to have higher glycosylation levels, thus increasing the threshold for T-cell receptor-mediated response as well as lowering the threshold for cytotoxic T-lymphocyte-associated antigen-4-mediated arrest of T-cell proliferation.

      Discussion

      Genome-wide association analyses have successfully identified more than 50 loci associated with psoriasis susceptibility. To date, however, the genetic basis of psoriasis is still not completely understood. In this study, we have performed a genome-wide pathway analysis of psoriasis genetic risk. Using a discovery cohort from Spain and an independent cohort from the UK, we have identified and validated the association of six genetic pathways with psoriasis susceptibility. Importantly, these validated pathways include biological processes such as retinol metabolism, transport of inorganic ions and amino acids, and post-translational protein modification that had not been previously associated with psoriasis risk at the genetic level. In addition, analyzing the network properties of these validated pathways we have found that MGAT5 gene has a strong centrality in the post-translational protein modification pathway. Using an additional independent case-control cohort from Spain, we have further replicated the association of MGAT5 with psoriasis risk. Taken together, these findings contribute to a better understanding of the genetic risk basis of psoriasis and provide important insights into the biological mechanisms associated with the disease pathogenesis.
      Retinol has been demonstrated to inhibit inflammatory processes in dermatological diseases (
      • Balato A.
      • Schiattarella M.
      • Lembo S.
      • et al.
      Interleukin-1 family members are enhanced in psoriasis and suppressed by vitamin D and retinoic acid.
      ). In particular, retinol inhibits the regulatory activity of the nuclear factor kappa B (NFKB) in the skin (
      • Austenaa L.M.
      • Carlsen H.
      • Ertesvag A.
      • et al.
      Vitamin A status significantly alters nuclear factor-kappaB activity assessed by in vivo imaging.
      ). NFKB is an established transcriptional factor that regulates multiple proinflammatory genes that are key in psoriasis pathogenesis like tumor necrosis factor and interleukin-17 (
      • Goldminz A.M.
      • Au S.C.
      • Kim N.
      • et al.
      NF-kappaB: an essential transcription factor in psoriasis.
      ). The NFKB signaling pathway has also been associated with the regulation of the proliferation of epidermal keratinocytes (
      • Tsuruta D.
      NF-kappaB links keratinocytes and lymphocytes in the pathogenesis of psoriasis.
      ). These findings are consistent with the elevated levels of NFKB that have been found in lesional and non-lesional psoriatic skin samples compared with non-psoriatic skin (
      • Lizzul P.F.
      • Aphale A.
      • Malaviya R.
      • et al.
      Differential expression of phosphorylated NF-kappaB/RelA in normal and psoriatic epidermis and downregulation of NF-kappaB in response to treatment with etanercept.
      ). Therefore, genetic variation in the retinol metabolism pathway could reduce the retinol production leading to a weakened NFKB signaling and, consequently, promoting both inflammatory and proliferative hallmarks of psoriasis.
      Psoriasis risk was also associated with the genetic pathway implicated in the transport of both inorganic ions and amino acids. An increased transport of inorganic ions in CD4+ helper T cells has been shown to contribute to autoimmune and inflammatory diseases (
      • Lang F.
      • Stournaras C.
      • Alesutan I.
      Regulation of transport across cell membranes by the serum- and glucocorticoid-inducible kinase SGK1.
      ). In particular, the intracellular transport of calcium is crucial for controlling the expression of proinflammatory genes in immune cells (
      • Khananshvili D.
      The SLC8 gene family of sodium-calcium exchangers (NCX)—structure, function, and regulation in health and disease.
      ,
      • Vig M.
      • Kinet J.P.
      Calcium signaling in immune cells.
      ). Accordingly, the transport of inorganic ions and amino acids pathway associated with psoriasis risk includes the SLC8A1 gene, which modulates the cytoplasmic calcium concentration (
      • Clapham D.E.
      Calcium signaling.
      ). The transport of amino acids into T cells is essential to maintain the increased production of proinflammatory cytokines in activated human T cells (
      • Hayashi K.
      • Jutabha P.
      • Endou H.
      • et al.
      LAT1 is a critical transporter of essential amino acids for immune reactions in activated human T cells.
      ). Importantly, the expression of amino acid transporters has been found to be differentially regulated in psoriatic inflammatory processes (
      • Jaeger K.
      • Paulsen F.
      • Wohlrab J.
      Characterization of cationic amino acid transporters (hCATs) 1 and 2 in human skin.
      ). These results therefore suggest that genetic variation in the transport of amino acids and inorganic ions pathway could increase the risk to develop psoriasis by modulating T-cell functionality.
      The post-translational protein modification pathway is responsible for the N-linked glycosylation of the asparagine residues in the HLA molecules (
      • Rudd P.M.
      • Elliott T.
      • Cresswell P.
      • et al.
      Glycosylation and the immune system.
      ). This post-translational modification pathway has been found to be necessary for the immune system tolerance to self-antigens (
      • Ryan S.O.
      • Cobb B.A.
      Roles for major histocompatibility complex glycosylation in immune function.
      ). Previous studies have found that a deficient or aberrant asparagine glycosylation can induce autoimmune diseases (
      • Green R.S.
      • Stone E.L.
      • Tenno M.
      • et al.
      Mammalian N-glycan branching protects against innate immune self-recognition and inflammation in autoimmune disease pathogenesis.
      ). Also, post-translationally modified autoantigens have been associated with psoriasis (
      • Iversen O.J.
      • Lysvand H.
      • Hagen L.
      The autoantigen Pso p27: a post-translational modification of SCCA molecules.
      ). In patients with psoriasis, the peptide glycosylation activity has been found to be markedly increased in comparison with healthy controls (
      • Damasiewicz-Bodzek A.
      • Wielkoszynski T.
      Advanced protein glycation in psoriasis.
      ). Furthermore, specific post-translational modifications on glycoproteins expressed on the surface of T lymphocytes have been shown to target these cells to the inflamed skin (
      • Fuhlbrigge R.C.
      • Kieffer J.D.
      • Armerding D.
      • et al.
      Cutaneous lymphocyte antigen is a specialized form of PSGL-1 expressed on skin-homing T cells.
      ). Therefore, genetic variation in the post-translational protein modification pathway could perturb the glycosylation processes that are crucial to maintain the immune system tolerance.
      MGAT5 encodes for a key enzyme in the N-glycosylation pathway. This pathway has been directly implicated in T-cell activation and autoimmunity (
      • Demetriou M.
      • Granovsky M.
      • Quaggin S.
      • et al.
      Negative regulation of T-cell activation and autoimmunity by Mgat5 N-glycosylation.
      ). Recent research has found an association between MGAT5 glycosylation activity and multiple sclerosis etiology both in experimental models and in humans (
      • Grigorian A.
      • Demetriou M.
      Mgat5 deficiency in T cells and experimental autoimmune encephalomyelitis.
      ,
      • Mkhikian H.
      • Grigorian A.
      • Li C.F.
      • et al.
      Genetics and the environment converge to dysregulate N-glycosylation in multiple sclerosis.
      ). In this study, we have found that the MGAT5 is a key gene in the post-translational protein modification pathway associated with psoriasis. Subsequently, we found that genetic variation at MGAT5 is associated with the level of glycosylation of in vitro activated T cells. This result is consistent with previous findings showing that deficiency of MGAT5 glycosylation activity reduces the T-cell activation threshold and, consequently, promotes the triggering of autoimmune diseases (
      • Demetriou M.
      • Granovsky M.
      • Quaggin S.
      • et al.
      Negative regulation of T-cell activation and autoimmunity by Mgat5 N-glycosylation.
      ). Further studies evaluating the implication of the T-cell surface glycosylation in clinically relevant outcomes in psoriasis such as skin severity are warranted.
      The association of psoriasis risk with the inflammatory response and the natural killer T-cell pathways involves more than 10 immune-related genes, including IL12B. In a recent pathway analysis study using association results of a meta-analysis for psoriasis risk (
      • Tsoi L.C.
      • Elder J.T.
      • Abecasis G.R.
      Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model.
      ), these two pathways were also found to be associated. These findings, however, were not validated using an independent cohort. Our study, therefore, provides strong confirmation of the implication of these two genetic pathways in the risk of psoriasis. Also, the permutation-based approach used in our study allowed to control for the potential bias associated with the presence of strong linkage disequilibrium patterns within genes. Our results indicate that the association of these pathways is not only driven by IL12B gene, but it is the result of the joint contribution of other small-effect genes in these pathways. One of these genes is CXCR4, which encodes for a chemokine receptor from the natural killer T-cell pathway (
      • Colantonio L.
      • Recalde H.
      • Sinigaglia F.
      • et al.
      Modulation of chemokine receptor expression and chemotactic responsiveness during differentiation of human naive T cells into Th1 or Th2 cells.
      ). Although CXCR4 gene has not been previously associated with psoriasis risk in single-marker GWAS, CXCR4 chemokine has been shown to reduce keratinocyte proliferation and, consequently, the expansion of psoriatic plaques by regulating the proliferative cytokine signals that are activated in psoriatic lesions (
      • Takekoshi T.
      • Wu X.
      • Mitsui H.
      • et al.
      CXCR4 negatively regulates keratinocyte proliferation in IL-23-mediated psoriasiform dermatitis.
      ). In addition, the inflammatory angiogenesis of psoriatic skin that leads to vascular remodeling has been recently shown to be modulated by CXCR4 chemokine (
      • Zgraggen S.
      • Huggenberger R.
      • Kerl K.
      • et al.
      An important role of the SDF-1/CXCR4 axis in chronic skin inflammation.
      ). Using the pathway analysis, we can therefore identify small-effect genes like CXCR4 that cannot be detected by single-marker GWAS but that are biologically implicated in key processes of the disease pathophysiology.
      In this study, we have also found a significant association between the DNA repair genetic pathway and psoriasis risk. Together with the dysregulation of immune system processes, the epidermal hyperproliferation is another well-known biological process implicated in the psoriasis pathophysiology (
      • Wolf R.
      • Orion E.
      • Ruocco E.
      • et al.
      Abnormal epidermal barrier in the pathogenesis of psoriasis.
      ). The application of ultraviolet radiation in psoriasis skin lesions to induce apoptosis in aberrantly proliferating keratinocytes has proved to be a successful treatment for the clearance of plaque psoriasis in approximately 70% of patients (
      • Weatherhead S.C.
      • Farr P.M.
      • Jamieson D.
      • et al.
      Keratinocyte apoptosis in epidermal remodeling and clearance of psoriasis induced by UV radiation.
      ). The ultraviolet radiation induces DNA damage that promotes the transcription of the DNA repair pathway genes (
      • Roos W.P.
      • Kaina B.
      DNA damage-induced cell death by apoptosis.
      ). Consequently, the enzymatic machinery of the pathway repairs the DNA damage and also triggers the cell death by activating the p53 apoptotic signaling (
      • Lavin M.F.
      • Birrell G.
      • Chen P.
      • et al.
      ATM signaling and genomic stability in response to DNA damage.
      ). Therefore, these results suggest that genetic variation in the DNA repair pathway promotes an inefficient activation of the p53 apoptotic signaling that leads to an increased keratinocyte proliferation, as well as an inefficient response to ultraviolet therapy in patients with psoriasis.
      Although the pathway-based analysis is a powerful approach to identify small-effect genetic variants associated with disease risk, this methodology is not exempt of limitations. Intergenic SNPs across the whole genome that map physically far away from genes were not included in this study. These genetic variants could be known risk loci (e.g., rs12188300 is associated with psoriasis risk and is located at >20Kb from IL12B gene) or may regulate the expression of genes through cis- and trans-expression quantitative trait loci mechanisms (
      • Gilad Y.
      • Rifkin S.A.
      • Pritchard J.K.
      Revealing the architecture of gene regulation: the promise of eQTL studies.
      ). Also, some SNPs might not be functionally related to the closest genes. With the increasing regulatory information derived from expression quantitative trait loci and epigenomic data (
      • Bernstein B.E.
      • Stamatoyannopoulos J.A.
      • Costello J.F.
      • et al.
      The NIH Roadmap Epigenomics Mapping Consortium.
      ,
      • Martens J.H.
      • Stunnenberg H.G.
      BLUEPRINT: mapping human blood cell epigenomes.
      ,
      • Raney B.J.
      • Cline M.S.
      • Rosenbloom K.R.
      • et al.
      ENCODE whole-genome data in the UCSC genome browser (2011 update).
      ), intergenic SNPs could be integrated in the pathway-based analysis in the next few years.
      The complex linkage disequilibrium structure of the HLA region together with the strong association with the susceptibility to multiple common diseases has been shown to generate false positive results in pathway-based methods (
      • Wang K.
      • Li M.
      • Hakonarson H.
      Analysing biological pathways in genome-wide association studies.
      ). Following recent studies, in this study we removed the SNPs mapping to this locus to perform the present pathway analysis (
      • Chen D.
      • Enroth S.
      • Ivansson E.
      • et al.
      Pathway analysis of cervical cancer genome-wide association study highlights the MHC region and pathways involved in response to infection.
      ). As a result, known pathways associated with psoriasis risk that include genes from the HLA region, like the NFKB pathway, were not analyzed in this study. Importantly, however, in this study we have found and validated the association between genetic pathways related to IL12 signaling, an established genetic risk pathway for psoriasis and psoriasis risk. Also, within the associated pathways there are known risk genes for psoriasis (e.g., REV3L and IL4 within the DNA repair and inflammatory response pathways, respectively). Together, these results confirm the accuracy of the present pathway-based approach to identify relevant genetic variation associated with psoriasis risk.
      The present genome-wide pathway analysis has two important strengths. First, we used PLINK software (Boston, MA) to identify genetic pathways associated with psoriasis risk. This pathway analysis method uses genotype data in contrast to the methodologies that are only based on association statistics. An important limitation of these latter methodologies is that they do not account for the linkage disequilibrium between SNPs. This can result in highly biased results and a significant increase in false positive results (
      • Wang K.
      • Li M.
      • Hakonarson H.
      Analysing biological pathways in genome-wide association studies.
      ). Instead, the pathway analysis approach that we used, although can be computationally costly, efficiently overcomes these biases by maintaining the correct linkage disequilibrium patterns between SNPs. Finally, compared with previous pathway-based studies in other complex diseases, we have performed a two-stage pathway analysis in two large cohorts from different populations. Using an independent population, we have validated genetic pathways associated with psoriasis risk.
      In conclusion, using a genome-wide pathway analysis approach we have identified to our knowledge previously unreported genetic pathways associated with psoriasis risk. These biological pathways include retinol metabolism, transport of inorganic ions and amino acids, and post-translational protein modification. The results of this study represent an important contribution to the characterization of the genetic risk basis of psoriasis.

      Materials and Methods

      Study population

      A total of 1,263 patients with psoriasis and 1,558 controls were recruited for the discovery stage (Supplementary Table S5 online). An independent case-control cohort of 7,353 individuals from the UK was used to validate the significantly associated pathways in the discovery cohort. An independent cohort of 1,381 patients with psoriasis and 2,048 controls from Spain was used to replicate the association between MGAT5 gene and psoriasis risk (Supplementary Materials, Supplementary Table S6 online).
      All the procedures were followed in compliance with the principles of the Declaration of Helsinki and all patients provided written informed consent to participate in this study. The study and the consent procedure were approved by the local Institutional Review Board of each participating center.

      DNA extraction and genome-wide genotyping

      GWAS genotyping of the 2,821 individuals from the discovery cohort was performed using Illumina Quad610 Beadchips (Illumina, San Diego, CA) (Supplementary Materials). After the quality control analysis, a final data set of 541,926 SNPs from 1,172 patients with psoriasis was available for the pathway-based analysis. The genome-wide genotyping of the patients with psoriasis from the validation stage was performed using the Illumina Human660W-Quad (Illumina) and the healthy controls were genotyped using the Illumina custom Human1.2M-Duo (Illumina) as has been previously described (
      • Strange A.
      • Capon F.
      • Spencer C.C.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ). The final data set used for the replication study included 515,703 SNPs from 2,178 patients with psoriasis. The genotyping of the MGAT5 replication cohort was performed using the Taqman real-time PCR platform (Applied Biosystems, Foster City, CA) (Supplementary Materials).

      Pathway-based analysis

      Gene set definition

      Reference biological pathway annotation databases (www.biocarta.com), Kyoto Encyclopedia of Genes and Genomes (
      • Kanehisa M.
      • Goto S.
      KEGG: Kyoto Encyclopedia of Genes and Genomes.
      ), and Reactome (
      • Croft D.
      • Mundo A.F.
      • Haw R.
      • et al.
      The Reactome pathway knowledgebase.
      ) were used to determine the global pathways (Supplementary Materials, Supplementary Tables S7 and S8 online). The final gene set included in this study was composed of 215,948 SNPs mapping to 1,053 pathways.

      Gene-set association analysis

      The statistical association analysis was performed using the PLINK set-based test (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • et al.
      PLINK: a tool set for whole-genome association and population-based linkage analyses.
      ) (Supplementary Materials). To obtain the global statistical significance of each validated pathway, we combined the empirical P-values resulting from the discovery and replication stages using Fisher’s method (
      • Kugler K.G.
      • Mueller L.A.
      • Graber A.
      MADAM: an open source meta-analysis toolbox for R and Bioconductor.
      ). We tested the association of 1,053 pathways with psoriasis risk. The false discovery rate (FDR) method (
      • Hochberg Y.
      • Benjamini Y.
      More powerful procedures for multiple significance testing.
      ) was used to account for multiple testing.

      Sensitivity analysis by removing the HLA and IL12B loci

      In pathway-based analysis, the presence of a single marker with very strong effects can lead to false positive associations. In these cases, the joint contribution of the pathway genes to disease risk is masked and not adequately evaluated (
      • Wang K.
      • Li M.
      • Hakonarson H.
      Analysing biological pathways in genome-wide association studies.
      ). Similar to previous studies, to avoid this type of spurious associations, we removed all SNPs mapping to the HLA region (Megabases 25.6 to 33.3 in chromosome 6) (
      • Chen D.
      • Enroth S.
      • Ivansson E.
      • et al.
      Pathway analysis of cervical cancer genome-wide association study highlights the MHC region and pathways involved in response to infection.
      ). In the discovery stage, we found genetic pathways in which the IL12B gene was significantly associated with disease risk at a genome-wide scale. IL12B is a well-known psoriasis risk gene that shows a large effect on disease susceptibility and, like the HLA region, could generate false positive results (
      • Cargill M.
      • Schrodi S.J.
      • Chang M.
      • et al.
      A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes.
      ,
      • Nair R.P.
      • Ruether A.
      • Stuart P.E.
      • et al.
      Polymorphisms of the IL12B and IL23R genes are associated with psoriasis.
      ,
      • Zhu K.J.
      • Zhu C.Y.
      • Shi G.
      • et al.
      Meta-analysis of IL12B polymorphisms (rs3212227, rs6887695) with psoriasis and psoriatic arthritis.
      ). Accordingly, we removed this psoriasis susceptibility locus (from 158,741,791 to 158,757,481 base pairs in chromosome 5) from the significant pathways and we repeated the analysis. We excluded 73 and 58 SNPs from the discovery and replication studies, respectively.

      Characterization of the genetic pathways associated with psoriasis risk

      Genetic pathways involved in similar biological processes may share genes. To identify pathways representing different and independent biological processes, we computed the gene overlap between each pair of genetic pathways associated with psoriasis risk (Supplementary Materials).
      The statistical significance of the association between pathway genes and psoriasis risk was determined according to the most significant SNP mapping to each particular gene.

      Analysis of the functional-based networks associated with psoriasis risk

      The biological knowledge representing the functional association between gene pairs was used to build the functional-based network of each genetic pathway associated with psoriasis risk. To identify those genes that are more likely to play a central role in the genetic pathways associated with psoriasis risk, we analyzed the network statistical properties of each functional-based network (Supplementary Materials). Using the genes that were nominally associated with psoriasis risk in both discovery and replication stages, we identified the most influential gene according to the highest values of these network statistics.

      Functional analysis of MGAT5 variation

      Following the methodology previously described (
      • Chen H.L.
      • Li C.F.
      • Grigorian A.
      • et al.
      T cell receptor signaling co-regulates multiple Golgi genes to enhance N-glycan branching.
      ), we evaluated the association of MGAT5 psoriasis risk variant with the level of cell surface glycosylation of in vitro activated CD4+ and CD8+ T cells isolated from n = 27 patients with psoriasis (Supplementary Materials).

      Conflict of Interest

      The authors state no conflict of interest.

      Acknowledgments

      This study was funded by of the Spanish Ministry of Economy and Competitiveness, grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36.

      Supplementary Material

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