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Item does not contain fulltext Systemic sclerosis (SSc) and systemic lupus erythematosus (SLE) are two archetypal systemic autoimmune diseases which have been shown to share multiple genetic susceptibility loci. In order to gain insight into the genetic basis of these diseases, we performed a pan-meta-analysis of two genome-wide association studies (GWASs) together with a replication stage including additional SSc and SLE cohorts. This increased the sample size to a total of 21 109 (6835 cases and 14 274 controls). We selected for replication 19 SNPs from the GWAS data. We were able to validate KIAA0319L (P = 3.31 x 10(-11), OR = 1.49) as novel susceptibility loci for SSc and SLE. Furthermore, we also determined that the previously described SLE susceptibility loci PXK (P = 3.27 x 10(-11), OR = 1.20) and JAZF1 (P = 1.11 x 10(-8), OR = 1.13) are shared with SSc. Supporting these new discoveries, we observed that KIAA0319L was overexpressed in peripheral blood cells of SSc and SLE patients compared with healthy controls. With these, we add three (KIAA0319L, PXK and JAZF1) and one (KIAA0319L) new susceptibility loci for SSc and SLE, respectively, increasing significantly the knowledge of the genetic basis of autoimmunity.

Systemic sclerosis (SSc) is complex autoimmune disease affecting the connective tissue; influenced by genetic and environmental components. Recently, we performed the first successful genome-wide association study (GWAS) of SSc. Here, we perform a large replication study to better dissect the genetic component of SSc. We selected 768 polymorphisms from the previous GWAS and genotyped them in seven replication cohorts from Europe. Overall significance was calculated for replicated significant SNPs by meta-analysis of the replication cohorts and replication-GWAS cohorts (3237 cases and 6097 controls). Six SNPs in regions not previously associated with SSc were selected for validation in another five independent cohorts, up to a total of 5270 SSc patients and 8326 controls. We found evidence for replication and overall genome-wide significance for one novel SSc genetic risk locus: CSK [P-value 5 5.04 3 10 -12, odds ratio (OR) 5 1.20]. Additionally, we found suggestive association in the loci PSD3 (P-value 5 3.18 3 10 -7, OR 5 1.36) and NFKB1 (P-value 5 1.03 3 10 -6, OR5 1.14). Additionally, we strengthened the evidence for previously confirmed associations. This study significantly increases the number of known putative genetic risk factors for SSc, including the genes CSK, PSD3 and NFKB1, and further confirms six previously described ones. © The Author 2012. Published by Oxford University Press. All rights reserved.

Severe natural or man-made disasters that occur in urban areas often require evacuation measures to protect or rescue the population. In crisis situations, different kinds of knowledge are necessary to get insight in the consequences of the proposed set of measures. In addition, there are typically many different parties involved in the decision making process. These decision makers can be supported by simulation models that assist in assessing and predicting the emergency situation and assist in the development of effective evacuation plans. There are currently many existing (simulation) models available for evacuation planning. However, the population data which is an important input parameter for the models, usually consists of generic or even fictive information. The mixed reality project “Reality Check”, which is part of the Dutch research program “Flood Control 2015”, aims to feed more reliable data into an evacuation model. This input data can be either static data, for example extracted from population registers, or dynamic real-time data. Both types of data improve the situational awareness of the decision makers dealing with a calamity and provide them with better simulation results to assess their options. The “Reality Check” tool combines several population databases, a road database and a scientific evacuation model. The tool has been realized using the MiReCol architecture (Mixed Reality for Collaboration). MiReCol enables the integration of scenarios, protocols, (scientific) models and data sources, in order to provide decision makers with real-time output from predicting models during crisis situations. In the MiReCol architecture, all components are linked through a network data bus, and web services are used to exchange information between data sources, evacuation models, and assessment or presentation tools. The “Reality Check” tool makes the knowledge and information from different sources and models available to all involved participants.

The aim of this study was to determine, through a genome-wide association study (GWAS), the genetic components contributing to different clinical sub-phenotypes of systemic sclerosis (SSc). We considered limited (lcSSc) and diffuse (dcSSc) cutaneous involvement, and the relationships with presence of the SSc-specific auto-antibodies, anti-centromere (ACA), and anti-topoisomerase I (ATA). Four GWAS cohorts, comprising 2,296 SSc patients and 5,171 healthy controls, were meta-analyzed looking for associations in the selected subgroups. Eighteen polymorphisms were further tested in nine independent cohorts comprising an additional 3,175 SSc patients and 4,971 controls. Conditional analysis for associated SNPs in the HLA region was performed to explore their independent association in antibody subgroups. Overall analysis showed that non-HLA polymorphism rs11642873 in IRF8 gene to be associated at GWAS level with lcSSc (P = 2.32×10−12, OR = 0.75). Also, rs12540874 in GRB10 gene (P = 1.27 × 10−6, OR = 1.15) and rs11047102 in SOX5 gene (P = 1.39×10−7, OR = 1.36) showed a suggestive association with lcSSc and ACA subgroups respectively. In the HLA region, we observed highly associated allelic combinations in the HLA-DQB1 locus with ACA (P = 1.79×10−61, OR = 2.48), in the HLA-DPA1/B1 loci with ATA (P = 4.57×10−76, OR = 8.84), and in NOTCH4 with ACA P = 8.84×10−21, OR = 0.55) and ATA (P = 1.14×10−8, OR = 0.54). We have identified three new non-HLA genes (IRF8, GRB10, and SOX5) associated with SSc clinical and auto-antibody subgroups. Within the HLA region, HLA-DQB1, HLA-DPA1/B1, and NOTCH4 associations with SSc are likely confined to specific auto-antibodies. These data emphasize the differential genetic components of subphenotypes of SSc., Author Summary Scleroderma or systemic sclerosis is a complex autoimmune disease affecting one individual of every 100,000 in Caucasian populations. Even though current genetic studies have led to better understanding of the pathogenesis of the disease, much remains unknown. Scleroderma is a heterogeneous disease, which can be subdivided according to different criteria, such as the involvement of organs and the presence of specific autoantibodies. Such subgroups present more homogeneous genetic groups, and some genetic associations with these manifestations have already been described. Through reanalysis of a genome-wide association study data, we identify three novel genes containing genetic variations which predispose to subphenotypes of the disease (IRF8, GRB10, and SOX5). Also, we better characterize the patterns of associated loci found in the HLA region. Together, our findings lead to a better understanding of the genetic component of scleroderma.

Contains fulltext : 97765.pdf (Publisher’s version ) (Closed access) 01 mei 2010

Systemic sclerosis (SSc) is an autoimmune disease characterized by fibrosis of the skin and internal organs that leads to profound disability and premature death. To identify new SSc susceptibility loci, we conducted the first genome-wide association study in a population of European ancestry including a total of 2,296 individuals with SSc and 5,171 controls. Analysis of 279,621 autosomal SNPs followed by replication testing in an independent case-control set of European ancestry (2,753 individuals with SSc (cases) and 4,569 controls) identified a new susceptibility locus for systemic sclerosis at CD247 (1q22-23, rs2056626, P = 2.09 x 10(-7) in the discovery samples, P = 3.39 x 10(-9) in the combined analysis). Additionally, we confirm and firmly establish the role of the MHC (P = 2.31 x 10(-18)), IRF5 (P = 1.86 x 10(-13)) and STAT4 (P = 3.37 x 10(-9)) gene regions as SSc genetic risk factors.