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      Pervasive Sharing of Genetic Effects in Autoimmune Disease

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          Abstract

          Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases—as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple—but not all—immune-mediated diseases (SNP-wise P CPMA<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.

          Author Summary

          Over the last five years we have found over 100 genetic variants predisposing to common diseases affecting the immune system. In this study we analyze 107 such variants across seven diseases and find that almost half are shared across diseases. We also find that the patterns of sharing across diseases cluster these variants into groups; proteins encoded near variants in the same group tend to interact. This suggests that genetic variation may influence entire pathways to create risk to multiple diseases.

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          Most cited references15

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          A human phenome-interactome network of protein complexes implicated in genetic disorders.

          We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.
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            A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC.

            The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and are essential in self versus non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune diseases. Yet identification of causal variants is problematic owing to linkage disequilibrium that extends across multiple HLA and non-HLA genes in the MHC. We therefore set out to characterize the linkage disequilibrium patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common SNPs and deletion-insertion polymorphisms across four population samples. The analysis provides informative tag SNPs that capture much of the common variation in the MHC region and that could be used in disease association studies, and it provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.
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              STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus.

              Rheumatoid arthritis is a chronic inflammatory disease with a substantial genetic component. Susceptibility to disease has been linked with a region on chromosome 2q. We tested single-nucleotide polymorphisms (SNPs) in and around 13 candidate genes within the previously linked chromosome 2q region for association with rheumatoid arthritis. We then performed fine mapping of the STAT1-STAT4 region in a total of 1620 case patients with established rheumatoid arthritis and 2635 controls, all from North America. Implicated SNPs were further tested in an independent case-control series of 1529 patients with early rheumatoid arthritis and 881 controls, all from Sweden, and in a total of 1039 case patients and 1248 controls from three series of patients with systemic lupus erythematosus. A SNP haplotype in the third intron of STAT4 was associated with susceptibility to both rheumatoid arthritis and systemic lupus erythematosus. The minor alleles of the haplotype-defining SNPs were present in 27% of chromosomes of patients with established rheumatoid arthritis, as compared with 22% of those of controls (for the SNP rs7574865, P=2.81x10(-7); odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.32). The association was replicated in Swedish patients with recent-onset rheumatoid arthritis (P=0.02) and matched controls. The haplotype marked by rs7574865 was strongly associated with lupus, being present on 31% of chromosomes of case patients and 22% of those of controls (P=1.87x10(-9); odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.55). Homozygosity of the risk allele, as compared with absence of the allele, was associated with a more than doubled risk for lupus and a 60% increased risk for rheumatoid arthritis. A haplotype of STAT4 is associated with increased risk for both rheumatoid arthritis and systemic lupus erythematosus, suggesting a shared pathway for these illnesses. Copyright 2007 Massachusetts Medical Society.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                August 2011
                August 2011
                10 August 2011
                : 7
                : 8
                : e1002254
                Affiliations
                [1 ]Center For Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [2 ]Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                [3 ]Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
                [4 ]Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [5 ]Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [6 ]Health Science and Technology MD Program, Harvard University and Massachusetts Institute of Technology, Boston, Massachusetts, United States of America
                [7 ]Harvard Biological and Biomedical Sciences Program, Harvard University, Boston, Massachusetts, United States of America
                [8 ]Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [9 ]Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
                [10 ]Analytical and Translational Genetics Unity, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [11 ]Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
                [12 ]Center for Statistical Genetics, University of Michigan, Ann Arbor, Massachusetts, United States of America
                [13 ]Human Genetics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
                [14 ]Genentech, South San Francisco, California, United States of America
                [15 ]Departments of Medicine and Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [16 ]Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
                [17 ]Department of Dermatology, University of Michigan, Ann Arbor, Michigan, United States of America
                [18 ]Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
                [19 ]Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
                [20 ]Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Canada
                [21 ]Blizard Institute, The London School of Medicine and Dentistry, London, United Kingdom
                [22 ]Department of Genetics, University Medical Center Groningen and Groningen University, Groningen, The Netherlands
                [23 ]Arthritis Research UK Epidemiology Unit, School of Translational Medicine, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, United Kingdom
                [24 ]Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
                University of Geneva Medical School, Switzerland
                Author notes

                Conceived and designed the experiments: C Cotsapas, BF Voight, DA Hafler, SS Rich, MJ Daly. Performed the experiments: C Cotsapas, BF Voight, E Rossin, BM Neale, MJ Daly. Analyzed the data: C Cotsapas, BF Voight, E Rossin, K Lage, MJ Daly. Contributed reagents/materials/analysis tools: BF Voight, K Lage, BM Neale, C Wallace, GR Abecasis, JC Barrett, T Behrens, J Cho, PL De Jager, JT Elder, RR Graham, P Gregersen, L Klareskog, KA Siminovitch, DA van Heel, C Wijmenga, J Worthington, JA Todd, DA Hafler, SS Rich, MJ Daly. Wrote the paper: C Cotsapas, BF Voight, JA Todd, DA Hafler, SS Rich, MJ Daly.

                Article
                PGENETICS-D-11-00126
                10.1371/journal.pgen.1002254
                3154137
                21852963
                7f20ec29-a3b8-4437-bd41-8bd4caca87a4
                Cotsapas et al. 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.
                History
                : 19 January 2011
                : 1 July 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Genetics
                Human Genetics
                Genetic Association Studies
                Genome-Wide Association Studies
                Gene Networks
                Genetics of Disease
                Genome-Wide Association Studies
                Genomics
                Genome Analysis Tools
                Genetic Networks
                Genome Scans
                Genome-Wide Association Studies

                Genetics
                Genetics

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