A Meta-Analysis of Genome-Wide Association Scans Identifies IL18RAP, PTPN2, TAGAP, and PUS10 As Shared Risk Loci for Crohn's Disease and Celiac Disease

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Complex nature of SNP genotype effects on gene expression in primary human leucocytes.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Genome wide association studies have been hugely successful in identifying disease risk variants, yet most variants do not lead to coding changes and how variants influence biological function is usually unknown. METHODS: We correlated gene expression and genetic variation in untouched primary leucocytes (n = 110) from individuals with celiac disease - a common condition with multiple risk variants identified. We compared our observations with an EBV-transformed HapMap B cell line dataset (n = 90), and performed a meta-analysis to increase power to detect non-tissue specific effects. RESULTS: In celiac peripheral blood, 2,315 SNP variants influenced gene expression at 765 different transcripts (< 250 kb from SNP, at FDR = 0.05, cis expression quantitative trait loci, eQTLs). 135 of the detected SNP-probe effects (reflecting 51 unique probes) were also detected in a HapMap B cell line published dataset, all with effects in the same allelic direction. Overall gene expression differences within the two datasets predominantly explain the limited overlap in observed cis-eQTLs. Celiac associated risk variants from two regions, containing genes IL18RAP and CCR3, showed significant cis genotype-expression correlations in the peripheral blood but not in the B cell line datasets. We identified 14 genes where a SNP affected the expression of different probes within the same gene, but in opposite allelic directions. By incorporating genetic variation in co-expression analyses, functional relationships between genes can be more significantly detected. CONCLUSION: In conclusion, the complex nature of genotypic effects in human populations makes the use of a relevant tissue, large datasets, and analysis of different exons essential to enable the identification of the function for many genetic risk variants in common diseases.Coeliac UKNetherlands Organization for Scientific ResearchCeliac Disease Consortium (an innovative cluster approved by the Netherlands Genomics Initiative and partly funded by the Dutch government)Netherlands Genomics InitiativeWellcome Trus

Trans-eQTLs Reveal That Independent Genetic Variants Associated with a Complex Phenotype Converge on Intermediate Genes, with a Major Role for the HLA

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Рідке мило на основі соапстоків після нейтралізації олій та жирів в нейтралізуючому розчині, що містить етанол

Development and application of statistical models for medical scientific researc

Coeliac disease-associated risk variants in TNFAIP3 and REL implicate altered NF-kappaB signalling

Objective: Our previous coeliac disease genome-wide association study (GWAS) implicated risk variants in the human leucocyte antigen (HLA) region and eight novel risk regions. To identify more coeliac disease loci, we selected 458 single nucleotide polymorphisms (SNPs) that showed more modest association in the GWAS for genotyping and analysis in four independent cohorts. Design: 458 SNPs were assayed in 1682 cases and 3258 controls from three populations (UK, Irish and Dutch). We combined the results with the original GWAS cohort (767 UK cases and 1422 controls); six SNPs showed association with p Results: We identified two novel coeliac disease risk regions: 6q23.3 (OLIG3-TNFAIP3) and 2p16.1 (REL), both of which reached genome-wide significance in the combined analysis of all 2987 cases and 5273 controls (rs2327832 p= 1.3x10(-08), and rs842647 p= 5.26x10(-07)). We investigated the expression of these genes in the RNA isolated from biopsies and from whole blood RNA. We did not observe any changes in gene expression, nor in the correlation of genotype with gene expression. Conclusions: Both TNFAIP3 (A20, at the protein level) and REL are key mediators in the nuclear factor kappa B (NF-kappa B) inflammatory signalling pathway. For the first time, a role for primary heritable variation in this important biological pathway predisposing to coeliac disease has been identified. Currently, the HLA risk factors and the 10 established non-HLA risk factors explain similar to 40% of the heritability of coeliac disease

Common polygenic variation in coeliac disease and confirmation of ZNF335 and NIFA as disease susceptibility loci

Coeliac disease (CD) is a chronic immune-mediated disease triggered by the ingestion of gluten. It has an estimated prevalence of approximately 1% in European populations. Specific HLA-DQA1 and HLA-DQB1 alleles are established coeliac susceptibility genes and are required for the presentation of gliadin to the immune system resulting in damage to the intestinal mucosa. In the largest association analysis of CD to date, 39 non-HLA risk loci were identified, 13 of which were new, in a sample of 12 014 individuals with CD and 12 228 controls using the Immunochip genotyping platform. Including the HLA, this brings the total number of known CD loci to 40. We have replicated this study in an independent Irish CD case–control population of 425 CD and 453 controls using the Immunochip platform. Using a binomial sign test, we show that the direction of the effects of previously described risk alleles were highly correlated with those reported in the Irish population, (P=2.2 × 10−16). Using the Polygene Risk Score (PRS) approach, we estimated that up to 35% of the genetic variance could be explained by loci present on the Immunochip (P=9 × 10−75). When this is limited to non-HLA loci, we explain a maximum of 4.5% of the genetic variance (P=3.6 × 10−18). Finally, we performed a meta-analysis of our data with the previous reports, identifying two further loci harbouring the ZNF335 and NIFA genes which now exceed genome-wide significance, taking the total number of CD susceptibility loci to 42

Genomic profiling of T-cell activation suggests increased sensitivity of memory T cells to CD28 costimulation

T-cell activation is a critical driver of immune responses. The CD28 costimulation is an essential regulator of CD4 T-cell responses, however, its relative importance in naive and memory T cells is not fully understood. Using different model systems, we observe that human memory T cells are more sensitive to CD28 costimulation than naive T cells. To deconvolute how the T-cell receptor (TCR) and CD28 orchestrate activation of human T cells, we stimulate cells using varying intensities of TCR and CD28 and profiled gene expression. We show that genes involved in cell cycle progression and division are CD28-driven in memory cells, but under TCR control in naive cells. We further demonstrate that T-helper differentiation and cytokine expression are controlled by CD28. Using chromatin accessibility profiling, we observe that AP1 transcriptional regulation is enriched when both TCR and CD28 are engaged, whereas open chromatin near CD28- sensitive genes is enriched for NF-kB motifs. Lastly, we show that CD28-sensitive genes are enriched in GWAS regions associated with immune diseases, implicating a role for CD28 in disease development. Our study provides important insights into the differential role of costimulation in naive and memory T-cell responses and disease susceptibility

Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk.

Variation in the human leukocyte antigen (HLA) genes accounts for one-half of the genetic risk in type 1 diabetes (T1D). Amino acid changes in the HLA-DR and HLA-DQ molecules mediate most of the risk, but extensive linkage disequilibrium complicates the localization of independent effects. Using 18,832 case-control samples, we localized the signal to 3 amino acid positions in HLA-DQ and HLA-DR. HLA-DQβ1 position 57 (previously known; P = 1 × 10(-1,355)) by itself explained 15.2% of the total phenotypic variance. Independent effects at HLA-DRβ1 positions 13 (P = 1 × 10(-721)) and 71 (P = 1 × 10(-95)) increased the proportion of variance explained to 26.9%. The three positions together explained 90% of the phenotypic variance in the HLA-DRB1-HLA-DQA1-HLA-DQB1 locus. Additionally, we observed significant interactions for 11 of 21 pairs of common HLA-DRB1-HLA-DQA1-HLA-DQB1 haplotypes (P = 1.6 × 10(-64)). HLA-DRβ1 positions 13 and 71 implicate the P4 pocket in the antigen-binding groove, thus pointing to another critical protein structure for T1D risk, in addition to the HLA-DQ P9 pocket.This research utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. This work is supported in part by funding from the National Institutes of Health (5R01AR062886-02 (PIdB), 1R01AR063759 (SR), 5U01GM092691-05 (SR), 1UH2AR067677-01 (SR), R01AR065183 (PIWdB)), a Doris Duke Clinical Scientist Development Award (SR), the Wellcome Trust (JAT) and the National Institute for Health Research (JAT and JMMH), and a Vernieuwingsimpuls VIDI Award (016.126.354) from the Netherlands Organization for Scientific Research (PIWdB). TLL was supported by the German Research Foundation (LE 2593/1-1 and LE 2593/2-1).This is the accepted manuscript. The final version is available at http://www.nature.com/ng/journal/v47/n8/full/ng.3353.html

Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls.

Determining whether potential causal variants for related diseases are shared can identify overlapping etiologies of multifactorial disorders. Colocalization methods disentangle shared and distinct causal variants. However, existing approaches require independent data sets. Here we extend two colocalization methods to allow for the shared-control design commonly used in comparison of genome-wide association study results across diseases. Our analysis of four autoimmune diseases--type 1 diabetes (T1D), rheumatoid arthritis, celiac disease and multiple sclerosis--identified 90 regions that were associated with at least one disease, 33 (37%) of which were associated with 2 or more disorders. Nevertheless, for 14 of these 33 shared regions, there was evidence that the causal variants differed. We identified new disease associations in 11 regions previously associated with one or more of the other 3 disorders. Four of eight T1D-specific regions contained known type 2 diabetes (T2D) candidate genes (COBL, GLIS3, RNLS and BCAR1), suggesting a shared cellular etiology.MF is funded by the Wellcome Trust (099772). CW and HG are funded by the Wellcome Trust (089989). This work was funded by the JDRF (9–2011–253), the Wellcome Trust (091157) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). ImmunoBase.org is supported by Eli Lilly and Company. We thank the UK Medical Research Council and Wellcome Trust for funding the collection of DNA for the British 1958 Birth Cohort (MRC grant G0000934, WT grant 068545/Z/02). DNA control samples were prepared and provided by S. Ring, R. Jones, M. Pembrey, W. McArdle, D. Strachan and P. Burton. Biotec Cluster M4, the Fidelity Biosciences Research Initiative, Research Foundation Flanders, Research Fund KU Leuven, the Belgian Charcot Foundation, Gemeinntzige Hertie Stiftung, University Zurich, the Danish MS Society, the Danish Council for Strategic Research, the Academy of Finland, the Sigrid Juselius Foundation, Helsinki University, the Italian MS Foundation, Fondazione Cariplo, the Italian Ministry of University and Research, the Torino Savings Bank Foundation, the Italian Ministry of Health, the Italian Institute of Experimental Neurology, the MS Association of Oslo, the Norwegian Research Council, the South–Eastern Norwegian Health Authorities, the Australian National Health and Medical Research Council, the Dutch MS Foundation and Kaiser Permanente. Marina Evangelou is thanked for motivating the investigation of the FASLG association.This is the author accepted manuscript. The final version is available at http://www.nature.com/ng/journal/v47/n7/full/ng.3330.html

Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al