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Psoriasis is a complex disease of skin with a prevalence of about 2%. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for psoriasis to date, including data from eight different Caucasian cohorts, with a combined effective sample size >39,000 individuals. We identified 16 additional psoriasis susceptibility loci achieving genome-wide significance, increasing the number of identified loci to 63 for European-origin individuals. Functional analysis highlighted the roles of interferon signalling and the NFκB cascade, and we showed that the psoriasis signals are enriched in regulatory elements from different T cells (CD8 + T-cells and CD4 + T-cells including T H 0, T H 1 and T H 17). The identified loci explain ∼28% of the genetic heritability and generate a discriminatory genetic risk score (AUC=0.76 in our sample) that is significantly correlated with age at onset ( p= 2 × 10 −89 ). This study provides a comprehensive layout for the genetic architecture of common variants for psoriasis. Psoriasis is an immune-mediated skin disease with a complex genetic architecture. Here, Elder and colleagues identify 16 novel psoriasis susceptibility loci using GWAS meta-analysis with a combined effective sample size of over 39,000 individuals.

Various autoimmune diseases have sex-linked biases. Gudjonsson and colleagues find that the transcription factor VGLL3 is associated with a female-biased molecular signature linked to susceptibility to autoimmune disease. Autoimmune diseases affect 7.5% of the US population, and they are among the leading causes of death and disability. A notable feature of many autoimmune diseases is their greater prevalence in females than in males, but the underlying mechanisms of this have remained unclear. Through the use of high-resolution global transcriptome analyses, we demonstrated a female-biased molecular signature associated with susceptibility to autoimmune disease and linked this to extensive sex-dependent co-expression networks. This signature was independent of biological age and sex-hormone regulation and was regulated by the transcription factor VGLL3, which also had a strong female-biased expression. On a genome-wide level, VGLL3-regulated genes had a strong association with multiple autoimmune diseases, including lupus, scleroderma and Sjögren's syndrome, and had a prominent transcriptomic overlap with inflammatory processes in cutaneous lupus. These results identified a VGLL3-regulated network as a previously unknown inflammatory pathway that promotes female-biased autoimmunity. They demonstrate the importance of studying immunological processes in females and males separately and suggest new avenues for therapeutic development.

Although analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts. We used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes. Together, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.

Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment. Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.

Psoriasis is a chronic autoimmune disease with complex genetic architecture. Previous genome-wide association studies (GWAS) and a recent meta-analysis using Immunochip data have uncovered 36 susceptibility loci. Here, we extend our previous meta-analysis of European ancestry by refined genotype calling and imputation and by the addition of 5,033 cases and 5,707 controls. The combined analysis, consisting of over 15,000 cases and 27,000 controls, identifies five new psoriasis susceptibility loci at genome-wide significance ( P <5 × 10 −8 ). The newly identified signals include two that reside in intergenic regions (1q31.1 and 5p13.1) and three residing near PLCL2 (3p24.3), NFKBIZ (3q12.3) and CAMK2G (10q22.2). We further demonstrate that NFKBIZ is a TRAF3IP2 -dependent target of IL-17 signalling in human skin keratinocytes, thereby functionally linking two strong candidate genes. These results further integrate the genetics and immunology of psoriasis, suggesting new avenues for functional analysis and improved therapies. About 2% of the population are affected by psoriasis, a chronic skin disease with complex genetics. Here Tsoi et al. conduct a meta-analysis of several genome-wide association studies and identify five novel loci, helping to further our understanding of the biology behind this condition.

Chronic inflammation is a critical component of atherogenesis, however, reliable human translational models aimed at characterizing these mechanisms are lacking. Psoriasis, a chronic inflammatory skin disease associated with increased susceptibility to atherosclerosis, provides a clinical human model that can be utilized to investigate the links between chronic inflammation and atherosclerosis development. We sought to investigate key biological processes in psoriasis skin and human vascular tissue to identify biological components that may promote atherosclerosis in chronic inflammatory conditions. Using a bioinformatics approach of human skin and vascular tissue, we determined IFN-γ and TNF-α are the dominant pro-inflammatory signals linking atherosclerosis and psoriasis. We then stimulated primary aortic endothelial cells and ex-vivo atherosclerotic tissue with IFN-γ and TNF-α and found they synergistically increased monocyte and T-cell chemoattractants, expression of adhesion molecules on the endothelial cell surface, and decreased endothelial barrier integrity in vitro , therefore increasing permeability. Our data provide strong evidence of synergism between IFN-γ and TNF- α in inflammatory atherogenesis and provide rationale for dual cytokine antagonism in future studies.

Psoriasis is a common and debilitating disease of the skin, nails, and joints, with an acknowledged but complex genetic basis. Early genome-wide linkage studies of psoriasis focused on segregation of microsatellite markers in families; however, the only locus consistently identified resided in the major histocompatibility complex. Subsequently, several groups mapped this locus to the vicinity of HLA-C, and two groups have reported HLA-Cw6 itself to be the major susceptibility allele. More recently, the development of millions of single-nucleotide polymorphisms, coupled with the development of high-throughput genotyping platforms and a comprehensive map of human haplotypes, has made possible a genome-wide association approach using cases and controls rather than families. Taking advantage of these developments, we participated in a collaborative genome-wide association study of psoriasis involving thousands of cases and controls. Initial analysis of these data revealed and/or confirmed association between psoriasis and seven genetic loci—HLA-C, IL12B, IL23R, IL23A, IL4/IL13, TNFAIP3, and TNIP1—and ongoing studies are revealing additional loci. Here, we review the epidemiology, immunopathology, and genetics of psoriasis, and present a disease model integrating its genetics and immunology.

Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis.

James Elder and colleagues report meta-analyses of two psoriasis genome-wide association studies with replication in additional cohorts. They make use of imputation using both the HapMap and initial 1000 Genomes Project datasets and identify three new psoriasis susceptibility loci. We carried out a meta-analysis of two recent psoriasis genome-wide association studies 1 , 2 with a combined discovery sample of 1,831 affected individuals (cases) and 2,546 controls. One hundred and two loci selected based on P value rankings were followed up in a three-stage replication study including 4,064 cases and 4,685 controls from Michigan, Toronto, Newfoundland and Germany. In the combined meta-analysis, we identified three new susceptibility loci, including one at NOS2 (rs4795067, combined P = 4 × 10 −11 ), one at FBXL19 (rs10782001, combined P = 9 × 10 −10 ) and one near PSMA6 - NFKBIA (rs12586317, combined P = 2 × 10 −8 ). All three loci were also associated with psoriatic arthritis (rs4795067, combined P = 1 × 10 −5 ; rs10782001, combined P = 4 × 10 −8 ; and rs12586317, combined P = 6 × 10 −5 ) and purely cutaneous psoriasis (rs4795067, combined P = 1 × 10 −8 ; rs10782001, combined P = 2 × 10 −6 ; and rs12586317, combined P = 1 × 10 −6 ). We also replicated a recently identified 3 association signal near RNF114 (rs495337, combined P = 2 × 10 −7 ).

Background A recent systematic review identified four candidate serum-soluble bone-turnover biomarkers (dickkopf-1, Dkk-1; macrophage-colony stimulating factor, M-CSF; matrix metalloproteinase-3, MMP-3; osteoprotegerin, OPG) showing possible association with psoriatic arthritis (PsA). We aimed to: (i) confirm and determine if these four biomarkers are associated with PsA; (ii) differentiate psoriasis cases with and without arthritis; and (iii) differentiate PsA cases with and without axial arthritis. Methods A prospective cross-sectional comparative two-centre study recruited 200 patients with psoriasis without arthritis (PsC), 127 with PsA without axial arthritis (pPsA), 117 with PsA with axial arthritis (psoriatic spondyloarthritis, PsSpA), 157 with ankylosing spondylitis (AS) without psoriasis, and 50 matched healthy controls (HC). Serum biomarker concentrations were measured using ELISA. Multivariable regression and receiver operating characteristic analyses were performed. Results MMP-3 concentrations were significantly higher and M-CSF significantly lower in each arthritis disease group compared with HC ( p  ≤ 0.02). MMP-3 concentrations were significantly higher (adjusted odds ratio, OR adj 1.02 per ng/ml increase in concentration; p  = 0.0004) and M-CSF significantly lower (OR adj 0.44 per ng/ml increase; p  = 0.01) in PsA (pPsA and PsSpA combined) compared with PsC. Dkk-1 concentrations were significantly higher (OR adj 1.22 per ng/mL increase; p  = 0.01), and OPG concentrations significantly lower (OR adj 0.20 per ng/mL increase; p  = 0.02) in patients with axial arthritis (PsSpA and AS combined) than in those without (pPsA). Furthermore, Dkk-1 concentrations were significantly higher along a spectrum of increasing axial arthritis; Dkk-1 concentrations were higher in AS compared with PsSpA (OR adj 1.18 per ng/mL increase; p  = 0.02). Receiver operating characteristic analysis showed MMP-3 to be the best single biomarker for differentiating PsA from PsC (AUC 0.70 for a cut-off of 14.51 ng/mL; sensitivity 0.76, specificity 0.60). Conclusions MMP-3 and M-CSF are biomarkers for the presence of arthritis in psoriatic disease, and could therefore be used to screen for PsA in psoriasis cohorts. Dkk-1 and OPG are biomarkers of axial arthritis; they could therefore be used to screen for the presence of axial disease in PsA cases, and help differentiate PsSpA from AS. High concentrations of Dkk-1 in AS and PsSpA compared with HC, support previous reports that Dkk-1 is dysfunctional in the spondyloarthritides.