4.6 Article

Pervasive Sharing of Genetic Effects in Autoimmune Disease

Journal

PLOS GENETICS
Volume 7, Issue 8, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pgen.1002254

Keywords

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Funding

  1. MRC [G1001158] Funding Source: UKRI
  2. Medical Research Council [G1001158] Funding Source: researchfish
  3. National Institute for Health Research [NF-SI-0508-10275] Funding Source: researchfish
  4. Arthritis Research UK [17552] Funding Source: Medline
  5. Medical Research Council [G1001158] Funding Source: Medline
  6. NIAMS NIH HHS [R01 AR050511, R01 AR054966, R01 AR042742] Funding Source: Medline
  7. NIDDK NIH HHS [P30 DK043351] Funding Source: Medline
  8. NIGMS NIH HHS [T32 GM007753] Funding Source: Medline
  9. Wellcome Trust [089989, 091157] Funding Source: Medline

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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.

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