Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis

Hinks, Anne; Cobb, Joanna; Marion, Miranda C.; Prahalad, Sampath; Sudman, Marc; Bowes, John; Martin, Paul J.; Comeau, Mary E.; Sajuthi, Satria; Andrews, Robert; Brown, Milton R.; Chen, Weimin; Concannon, Patrick; Deloukas, Panos; Edkins, Sarah; Eyre, Steve; Gaffney, Patrick M.; Guthery, Stephen L.; Guthridge, Joel M.; Hunt, Sarah; James, Judith A.; Keddache, Mehdi; Moser, Kathy L.; Nigrović, Peter A.; Önengüt-Gümüşcü, Suna; Onslow, Mitchell; Rosé, Carlos D.; Rich, Stephen S.; Steel, Kathryn J A; Wakeland, Edward K.; Wallace, Carol A.; Wedderburn, Lucy; Woo, Patricia; Bohnsack, John F.; Haas, Johannes; Glass, David N.; Langefeld, Carl D.; Thomson, Wendy; Thompson, Susan D.

doi:10.1038/ng.2614

Cited by 350 publications

(385 citation statements)

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“…Of particular interest is the chromosome 3 p21.31 region, which is known to be associated with multiple autoimmune diseases such as celiac disease, type 1 diabetes mellitus, and Behçet's disease and is suggestively associated in juvenile idiopathic arthritis (JIA) 35, 36, 37, 38. The strongest association in this region was with rs112088397, which tags a large haplotype block where many additional SNPs reached a suggestive level of significance and is the same risk haplotype as that reported in JIA (r 2 = 0.87) 35, 39. The variant rs112088397 in our study is found at a higher frequency in controls (minor allele frequency [MAF] of 0.08 for patients versus MAF of 0.16 for controls) and is therefore protective against IBM (OR 0.42 [95% CI 0.29–0.60]).…”

Section: Discussionmentioning

confidence: 99%

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Rothwell

Cooper

Lundberg

et al. 2017

Arthritis & Rheumatology

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ObjectiveInclusion body myositis (IBM) is characterized by a combination of inflammatory and degenerative changes affecting muscle. While the primary cause of IBM is unknown, genetic factors may influence disease susceptibility. To determine genetic factors contributing to the etiology of IBM, we conducted the largest genetic association study of the disease to date, investigating immune‐related genes using the Immunochip.MethodsA total of 252 Caucasian patients with IBM were recruited from 11 countries through the Myositis Genetics Consortium and compared with 1,008 ethnically matched controls. Classic HLA alleles and amino acids were imputed using SNP2HLA.ResultsThe HLA region was confirmed as the most strongly associated region in IBM (P = 3.58 × 10−33). HLA imputation identified 3 independent associations (with HLA–DRB1*03:01, DRB1*01:01, and DRB1*13:01), although the strongest association was with amino acid positions 26 and 11 of the HLA–DRB1 molecule. No association with anti–cytosolic 5′‐nucleotidase 1A–positive status was found independent of HLA–DRB1*03:01. There was no association of HLA genotypes with age at onset of IBM. Three non‐HLA regions reached suggestive significance, including the chromosome 3 p21.31 region, an established risk locus for autoimmune disease, where a frameshift mutation in CCR5 is thought to be the causal variant.ConclusionThis is the largest, most comprehensive genetic association study to date in IBM. The data confirm that HLA is the most strongly associated region and identifies novel amino acid associations that may explain the risk in this locus. These amino acid associations differentiate IBM from polymyositis and dermatomyositis and may determine properties of the peptide‐binding groove, allowing it to preferentially bind autoantigenic peptides. A novel suggestive association within the chromosome 3 p21.31 region suggests a role for CCR5.

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Section: Discussionmentioning

confidence: 99%

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Rothwell

Cooper

Lundberg

et al. 2017

Arthritis & Rheumatology

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“…Two or more independent signals are commonly observed in fine mapped autoimmune disease regions using stepwise regression [7][8][9][10]. Despite long established doubts regarding the validity of stepwise regression [3], its use has continued to dominate in GWAS because of the number of SNPs measured simultaneously.…”

Section: Discussionmentioning

confidence: 99%

“…Bayesian methods that summarise evidence across SNPs in a region to assess either enrichment of signals in chromatin states [5] or colocalisation between association signals for different traits [6] make a similar single causal variant assumption. As stepwise approaches often obtain evidence for additional, independent association signals in a region [7][8][9][10], this assumption is unrealistic. One exception is BimBam [11] which can fit multi SNP models, but considers all possible models up to a specified maximum number of causal SNPs.…”

Section: Introductionmentioning

confidence: 99%

Dissection of a complex disease susceptibility region using a Bayesian stochastic search approach to fine mapping

Wallace

Cutler

Pontikos

et al. 2015

Preprint

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Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1Dassociated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r 2 ⋍ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4 + T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data. Author SummaryGenetic association studies have identified many DNA sequence variants that associate with disease risk. By exploiting the known correlation that exists between neighbouring variants in the genome, inference can be extended beyond those individual variants tested to identify sets within which a causal variant is likely to reside. However, this correlation, particularly in the presence of multiple disease causing variants in relative proximity, makes disentangling the specific causal variants difficult. Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach, beginning with the most associated variant in a region, then iteratively attempting to find additional associated variants. We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem. We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso. We applied our approach to understand the association of two immune-mediated dis...

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“…The Immunochip consortium designed a genotyping array, an Illumina Infinium array with ~196,000 single nucleotide polymorphisms (SNPs), from 186 loci previously associated with 12 autoimmune diseases identified from GWASs. The success of the Immunochip is evidenced by novel risk loci, not previously associated with a specific pheno-type, being identified in numerous autoimmune diseases (3)(4)(5)(6)(7)(8) including RA (9).…”

Section: Introductionmentioning

confidence: 99%

Immunochip Identifies Novel, and Replicates Known, Genetic Risk Loci for Rheumatoid Arthritis in Black South Africans

Govind

Choudhury

Hodkinson

et al. 2014

Mol Med

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The aim of this study was to identify genetic variants associated with rheumatoid arthritis (RA) risk in black South Africans. Black South African RA patients (n = 263) were compared with healthy controls (n = 374). Genotyping was performed using the Immunochip, and four-digit high-resolution human leukocyte antigen (HLA) typing was performed by DNA sequencing of exon 2. Standard quality control measures were implemented on the data. The strongest associations were in the intergenic region between the HLA-DRB1 and HLA-DQA1 loci. After conditioning on HLA-DRB1 alleles, the effect in the rest of the extended major histocompatibility (MHC) diminished. Non-HLA single nucleotide polymorphisms (SNPs) in the intergenic regions LOC389203|RBPJ, LOC100131131|IL1R1, KIAA1919|REV3L, LOC643749|TRAF3IP2, and SNPs in the intron and untranslated regions (UTR) of IRF1 and the intronic region of ICOS and KIAA1542 showed association with RA (p < 5 × 10 -5). Of the SNPs previously associated with RA in Caucasians, one SNP, rs874040, locating to the intergenic region LOC389203|RBPJ was replicated in this study. None of the variants in the PTPN22 gene was significantly associated. The seropositive subgroups showed similar results to the overall cohort. The effects observed across the HLA region are most likely due to HLA-DRB1, and secondary effects in the extended MHC cannot be detected. Seven non-HLA loci are associated with RA in black South Africans. Similar to Caucasians, the intergenic region between LOC38920 and RBPJ is associated with RA in this population. The strong association of the R620W variant of the PTPN22 gene with RA in Caucasians was not replicated since this variant was monomorphic in our study, but other SNP variants of the PTPN22 gene were also not associated with RA in black South Africans, suggesting that this locus does not play a major role in RA in this population.

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Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis

Cited by 350 publications

References 53 publications

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Dissection of a complex disease susceptibility region using a Bayesian stochastic search approach to fine mapping

Immunochip Identifies Novel, and Replicates Known, Genetic Risk Loci for Rheumatoid Arthritis in Black South Africans

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