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Genome-wide association studies of the related chronic inflammatory bowel diseases (IBD) known as Crohn's disease and ulcerative colitis have shown strong evidence of association to the major histocompatibility complex (MHC). This region encodes a large number of immunological candidates, including the antigen-presenting classical human leukocyte antigen (HLA) molecules. Studies in IBD have indicated that multiple independent associations exist at HLA and non-HLA genes, but they have lacked the statistical power to define the architecture of association and causal alleles. To address this, we performed high-density SNP typing of the MHC in >32,000 individuals with IBD, implicating multiple HLA alleles, with a primary role for HLA-DRB1*01:03 in both Crohn's disease and ulcerative colitis. Noteworthy differences were observed between these diseases, including a predominant role for class II HLA variants and heterozygous advantage observed in ulcerative colitis, suggesting an important role of the adaptive immune response in the colonic environment in the pathogenesis of IBD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 TH0, TH1 and TH17). 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. [ABSTRACT FROM AUTHOR]

Chronic venous disease (CVD) is a multifactorial condition representing one of the most common disorders among populations of Western countries. The heritability of about 17% suggests genetic risk factors in CVD etiology. However, so far the genetic causes are unknown. We undertook the hitherto first genome-wide association study (GWAS) for CVD, analyzing more than 1.93 M SNPs in 4,942 German individuals, followed by replication in two independent German data sets. The combined analysis of discovery and replication stages (2,269 cases and 7,765 controls) yielded robust associations within the two genes EFEMP1 and KCNH8 (rs17278665, rs727139 with P < 5 × 10−8), and suggestive association within gene SKAP2 (rs2030136 with P < 5 × 10−7). Association signals of rs17278665 and rs727139 reside in regions of low linkage disequilibrium containing no other genes. Data from the ENCODE and Roadmap Epigenomics projects show that tissue specific marks overlap with the variants. SNPs rs17278665 and rs2030136 are known eQTLs. Our study demonstrates that GWAS are a valuable tool to study the genetic component of CVD. With our approach, we identified two novel genome-wide significant susceptibility loci for this common disease. Particularly, the extracellular matrix glycoprotein EFEMP1 is promising for future functional studies due to its antagonistic role in vessel development and angiogenesis. [ABSTRACT FROM AUTHOR]