Pervasive Sharing of Genetic Effects in Autoimmune Disease

Center For Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
PLoS Genetics (Impact Factor: 7.53). 08/2011; 7(8):e1002254. DOI: 10.1371/journal.pgen.1002254
Source: PubMed


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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Available from: Chris Cotsapas, Oct 02, 2015
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    • "Notably, the majority of these novel MS-associated genes played pivotal roles in the workings of the immune system and were also associated with other autoimmune diseases, supporting the hypothesis that the same processes occur in different autoimmune diseases (Baranzini, 2009; Cotsapas et al., 2011). "
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    ABSTRACT: Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system caused by a complex interaction between multiple genes and environmental factors. HLA region is the strongest susceptibility locus, but recent huge genome-wide association studies identified new susceptibility genes. Among these, BACH2, PTGER4, RGS1 and ZFP36L1 were highlighted. Here, a gene expression analysis revealed that three of them, namely BACH2, PTGER4 and ZFP36L1, are down-regulated in MS patients' blood cells compared to healthy subjects. Interestingly, all these genes are involved in the immune system regulation with predominant anti-inflammatory role and their reduction could predispose to MS development.
    Journal of Neuroimmunology 01/2015; 279. DOI:10.1016/j.jneuroim.2015.01.004 · 2.47 Impact Factor
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    • "The broader pursuit of examining multiple GWAS phenotypes has been explored in various ways, with some authors proposing combining traits before performing the GWAS as a way to increase power to detect pleiotropic genes [25]. Others have used multiple GWAS of the same disease to enhance biological pathway analysis [21], and statistics have been developed to identify pleiotropic SNPs from among several GWAS of related traits [7]. Our work builds on these ideas but explores a somewhat different question: that of using multiple GWAS of different phenotypes to enhance understanding of a single trait. "
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    ABSTRACT: We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.
    Genomics Data 12/2014; 2:202–211. DOI:10.1016/j.gdata.2014.04.004
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    • "Beyond transplantation, polymorphisms in the MHC region have been used as molecular markers for population genetics and studies of diseases and traits. In the past 30 years, no other region in the genome has provided more association signals with multifactorial traits, including autoimmune diseases [5]–[8], inflammatory and infectious diseases [9], cancer [10], adverse drug effects [11], [12], and behavioral traits such as mating [13], [14]. To assess HLA allelic diversity, these studies employed a broad range of methodologies from serology, restriction fragment length polymorphism, and microsatellites up to the latest generation of single nucleotide polymorphism (SNP) genotyping methods. "
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    ABSTRACT: The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation by sequencing at a level that should allow the genome-wide detection of most variants with frequencies as low as 1%. However, in the major histocompatibility complex (MHC), only the top 10 most frequent haplotypes are in the 1% frequency range whereas thousands of haplotypes are present at lower frequencies. Given the limitation of both the coverage and the read length of the sequences generated by the 1000 Genomes Project, the highly variable positions that define HLA alleles may be difficult to identify. We used classical Sanger sequencing techniques to type the HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1 genes in the available 1000 Genomes samples and combined the results with the 103,310 variants in the MHC region genotyped by the 1000 Genomes Project. Using pairwise identity-by-descent distances between individuals and principal component analysis, we established the relationship between ancestry and genetic diversity in the MHC region. As expected, both the MHC variants and the HLA phenotype can identify the major ancestry lineage, informed mainly by the most frequent HLA haplotypes. To some extent, regions of the genome with similar genetic or similar recombination rate have similar properties. An MHC-centric analysis underlines departures between the ancestral background of the MHC and the genome-wide picture. Our analysis of linkage disequilibrium (LD) decay in these samples suggests that overestimation of pairwise LD occurs due to a limited sampling of the MHC diversity. This collection of HLA-specific MHC variants, available on the dbMHC portal, is a valuable resource for future analyses of the role of MHC in population and disease studies.
    PLoS ONE 07/2014; 9(7):e97282. DOI:10.1371/journal.pone.0097282 · 3.23 Impact Factor
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