New Research Building, 77 Avenue Louis Pasteur Boston, MA 02215 Contact
Haghighi A, Krier JB, Tóth-Petróczy A, Cassa CA, Frank NY, Carmichael N, Fieg E, Bjonnes AC, Mohanty AK, Briere LC, Lincoln SA, Lucia S, Gupta V, Söylemez O, Sutti S, Kooshesh K, Qiu H, Fay CJ, Perroni V, Valerius J, Hanna M, Frank A, Ouahed JD, Snapper SB, Pantazi A, Chopra SS, Leshchiner I, Stitziel NO, Feldweg AM, Mannstadt M, Loscalzo J, Sweetser DA, Liao E, Stoler JOM, Bearce nowak C, Sanchez-Lara PA, Klein OD, Perry H, Patsopoulos NA, Raychaudhuri S, Goessling W, Green RC, Seidman CE, MacRae CA, Sunyaev S, Maas RL, Vuzman D. An Integrated Clinical Program and Crowdsourcing Strategy for Genomic Sequencing and Mendelian Disease Gene Discovery. [Internet]. npj Genomic Medicine 2018;3:21. Publisher's VersionAbstract
Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs.
Current classification of primary inflammatory arthritis begins from the assumption that adults and children are different. No form of juvenile idiopathic arthritis bears the same name as an adult arthritis, a nomenclature gap with implications for both clinical care and research. Recent genetic data have raised questions regarding this adult/pediatric divide, revealing instead broad patterns that span the age spectrum. Combining these genetic patterns with demographic and clinical data, we propose that inflammatory arthritis can be segregated into 4 main clusters, largely irrespective of pediatric or adult onset: seropositive, seronegative (likely including a distinct group that usually begins in early childhood), spondyloarthritis, and systemic. Each of these broad clusters is internally heterogeneous, highlighting the need for further study to resolve etiologically discrete entities. Eliminating divisions based on arbitrary age cutoffs will enhance opportunities for collaboration between adult and pediatric rheumatologists, thereby helping to promote the understanding and treatment of arthritis.
Genome-wide association studies (GWAS) have identified many disease-associated noncoding variants, but cannot distinguish functional single-nucleotide polymorphisms (fSNPs) from others that reside incidentally within risk loci. To address this challenge, we developed an unbiased high-throughput screen that employs type IIS enzymatic restriction to identify fSNPs that allelically modulate the binding of regulatory proteins. We coupled this approach, termed SNP-seq, with flanking restriction enhanced pulldown (FREP) to identify regulation of CD40 by three disease-associated fSNPs via four regulatory proteins, RBPJ, RSRC2 and FUBP-1/TRAP150. Applying this approach across 27 loci associated with juvenile idiopathic arthritis, we identified 148 candidate fSNPs, including two that regulate STAT4 via the regulatory proteins SATB2 and H1.2. Together, these findings establish the utility of tandem SNP-seq/FREP to bridge the gap between GWAS and disease mechanism.
Donlin LT, Rao DA, Wei K, Slowikowski K, McGeachy MJ, Turner JD, Meednu N, Mizoguchi F, Gutierrez-Arcelus M, Lieb DJ, Keegan J, Muskat K, Hillman J, Rozo C, Ricker E, Eisenhaure TM, Li S, Browne EP, Chicoine A, Sutherby D, Noma A, Network AMPRA/SLE, Nusbaum C, Kelly S, Pernis AB, Ivashkiv LB, Goodman SM, Robinson WH, Utz PJ, Lederer JA, Gravallese EM, Boyce BF, Hacohen N, Pitzalis C, Gregersen PK, Firestein GS, Raychaudhuri S, Moreland LW, Holers VM, Bykerk V, Filer A, Boyle DL, Brenner MB, Anolik JH. Methods for high-dimensonal analysis of cells dissociated from cyropreserved synovial tissue [Internet]. Arthritis Research & Therapy 2018;20(1):139. Publisher's VersionAbstract
Detailed molecular analyses of cells from rheumatoid arthritis (RA) synovium hold promise in identifying cellular phenotypes that drive tissue pathology and joint damage. The Accelerating Medicines Partnership RA/SLE Network aims to deconstruct autoimmune pathology by examining cells within target tissues through multiple highdimensional assays. Robust standardized protocols need to be developed before cellular phenotypes at a single cell level can be effectively compared across patient samples. Methods: Multiple clinical sites collected cryopreserved synovial tissue fragments from arthroplasty and synovial biopsy in a 10% DMSO solution. Mechanical and enzymatic dissociation parameters were optimized for viable cell extraction and surface protein preservation for cell sorting and mass cytometry, as well as for reproducibility in RNA sequencing (RNA-seq). Cryopreserved synovial samples were collectively analyzed at a central processing site by a custom-designed and validated 35-marker mass cytometry panel. In parallel, each sample was flow sorted into fibroblast, T-cell, B-cell, and macrophage suspensions for bulk population RNA-seq and plate-based single-cell CEL-Seq2 RNA-seq. Results: Upon dissociation, cryopreserved synovial tissue fragments yielded a high frequency of viable cells, comparable to samples undergoing immediate processing. Optimization of synovial tissue dissociation across six clinical collection sites with ~ 30 arthroplasty and ~ 20 biopsy samples yielded a consensus digestion protocol using 100 μg/ml of LiberaseTM TL enzyme preparation. This protocol yielded immune and stromal cell lineages with preserved surface markers and minimized variability across replicate RNA-seq transcriptomes. Mass cytometry analysis of cells from cryopreserved synovium distinguished diverse fibroblast phenotypes, distinct populations of memory B cells and antibody-secreting cells, and multiple CD4 and CD8 T-cell activation states. Bulk RNA-seq of sorted cell populations demonstrated robust separation of synovial lymphocytes, fibroblasts, and macrophages. Single-cell RNA-seq produced transcriptomes of over 1000 genes/cell, including transcripts encoding characteristic lineage markers identified.
Genetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with independent chromatin data. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.
OBJECTIVES: Psoriatic arthritis (PsA) is an inflammatory arthritis associated with psoriasis. While many common risk alleles have been reported for association with PsA as well as psoriasis, few rare coding alleles have yet been identified. METHODS: To identify rare coding variation associated with PsA risk or protection, we genotyped 41 267 variants with the exome chip and investigated association within an initial cohort of 1980 PsA cases and 5913 controls. Genotype data for an independent cohort of 2234 PsA cases and 5708 controls was also made available, allowing for a meta-analysis to be performed with the discovery dataset. RESULTS: We identified an association with the rare variant rs35667974 (p=2.39x10(-6), OR=0.47), encoding an Ile923Val amino acid change in the IFIH1 gene protein product. The association was reproduced in our independent cohort, which reached a high level of significance on meta-analysis with the discovery and replication datasets (p=4.67x10(-10)). We identified a strong association with IFIH1 when performing multiple-variant analysis (p=6.77x10(-6)), and found evidence of independent effects between the rare allele and the common PsA variant at the same locus. CONCLUSION: For the first time, we report a rare coding allele in IFIH1 to be protective for PsA. This rare allele has also been identified to have the same direction of effect on type I diabetes and psoriasis. While this association further supports existing evidence for IFIH1 as a causal gene for PsA, mechanistic studies will need to be pursued to confirm that IFIH1 is indeed causal.
CD4(+) T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4(+) T cells within affected tissues may be identified by expression of markers of recent activation. Here we use mass cytometry to analyse activated T cells in joint tissue from patients with rheumatoid arthritis, a chronic immune-mediated arthritis that affects up to 1% of the population. This approach revealed a markedly expanded population of PD-1(hi)CXCR5(-)CD4(+) T cells in synovium of patients with rheumatoid arthritis. However, these cells are not exhausted, despite high PD-1 expression. Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a population of PD-1(hi)CXCR5(-) 'peripheral helper' T (TPH) cells that express factors enabling B-cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1(hi)CXCR5(+) T follicular helper cells, TPH cells induce plasma cell differentiation in vitro through IL-21 secretion and SLAMF5 interaction (refs 3, 4). However, global transcriptomics highlight differences between TPH cells and T follicular helper cells, including altered expression of BCL6 and BLIMP1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in TPH cells. TPH cells appear to be uniquely poised to promote B-cell responses and antibody production within pathologically inflamed non-lymphoid tissues.
OBJECTIVE: In many rheumatoid arthritis (RA) patients, disease is controlled with anti-tumor necrosis factor (anti-TNF) biologic therapies. However, in a significant number of patients, the disease fails to respond to anti-TNF therapy. We undertook the present study to examine the hypothesis that rare and low-frequency genetic variants might influence response to anti-TNF treatment. METHODS: We sequenced the coding region of 750 genes in 1,094 RA patients of European ancestry who were treated with anti-TNF. After quality control, 690 genes were included in the analysis. We applied single-variant association and gene-based association tests to identify variants associated with anti-TNF treatment response. In addition, given the key mechanistic role of TNF, we performed gene set analyses of 27 TNF pathway genes. RESULTS: We identified 14,420 functional variants, of which 6,934 were predicted as nonsynonymous 2,136 of which were further predicted to be "damaging." Despite the fact that the study was well powered, no single variant or gene showed study-wide significant association with change in the outcome measures disease activity or European League Against Rheumatism response. Intriguingly, we observed 3 genes, of 27 with nominal signals of association (P < 0.05), that were involved in the TNF signaling pathway. However, when we performed a rigorous gene set enrichment analysis based on association P value ranking, we observed no evidence of enrichment of association at genes involved in the TNF pathway (Penrichment = 0.15, based on phenotype permutations). CONCLUSION: Our findings suggest that rare and low-frequency protein-coding variants in TNF signaling pathway genes or other genes do not contribute substantially to anti-TNF treatment response in patients with RA.
CD4+ T cells have been long known to play an important role in the pathogenesis of rheumatoid arthritis (RA), but the specific cell populations and states that drive the disease have been challenging to identify with low dimensional single cell data and bulk assays. The advent of high dimensional single cell technologies-like single cell RNA-seq or mass cytometry-has offered promise to defining key populations, but brings new methodological and statistical challenges. Recent single cell profiling studies have revealed a broad diversity of cell types among CD4+ T cells, identifying novel populations that are expanded or altered in RA. Here, we will review recent findings on CD4+ T cell heterogeneity and RA that have come from single cell profiling studies and discuss the best practices for conducting these studies.
The pathogenetic mechanisms by which HLA-DRB1 alleles are associated with anticitrullinated peptide antibody (ACPA)-positive rheumatoid arthritis (RA) are incompletely understood. RA high-risk HLA-DRB1 alleles are known to share a common motif, the 'shared susceptibility epitope (SE)'. Here, the electropositive P4 pocket of HLA-DRB1 accommodates self-peptide residues containing citrulline but not arginine. HLA-DRB1 His/Phe13β stratifies with ACPA-positive RA, while His13βSer polymorphisms stratify with ACPA-negative RA and RA protection. Indigenous North American (INA) populations have high risk of early-onset ACPA-positive RA, whereby HLA-DRB1*04:04 and HLA-DRB1*14:02 are implicated as risk factors for RA in INA. However, HLA-DRB1*14:02 has a His13βSer polymorphism. Therefore, we aimed to verify this association and determine its molecular mechanism.
HLA genotype was compared in 344 INA patients with RA and 352 controls. Structures of HLA-DRB1*1402-class II loaded with vimentin-64Arg59-71, vimentin-64Cit59-71 and fibrinogen β-74Cit69-81 were solved using X-ray crystallography. Vimentin-64Cit59-71-specific and vimentin59-71-specific CD4+ T cells were characterised by flow cytometry using peptide-histocompatibility leukocyte antigen (pHLA) tetramers. After sorting of antigen-specific T cells, TCRα and β-chains were analysed using multiplex, nested PCR and sequencing.
ACPA+ RA in INA was independently associated with HLA-DRB1*14:02. Consequent to the His13βSer polymorphism and altered P4 pocket of HLA-DRB1*14:02, both citrulline and arginine were accommodated in opposite orientations. Oligoclonal autoreactive CD4+ effector T cells reactive with both citrulline and arginine forms of vimentin59-71 were observed in patients with HLA-DRB1*14:02+ RA and at-risk ACPA- first-degree relatives. HLA-DRB1*14:02-vimentin59-71-specific and HLA-DRB1*14:02-vimentin-64Cit59-71-specific CD4+ memory T cells were phenotypically distinct populations.
HLA-DRB1*14:02 broadens the capacity for citrullinated and native self-peptide presentation and T cell expansion, increasing risk of ACPA+ RA.
Juvenile idiopathic arthritis (JIA) is a heterogeneous group of diseases, comprising seven categories. Genetic data could potentially be used to help redefine JIA categories and improve the current classification system. The human leucocyte antigen (HLA) region is strongly associated with JIA. Fine-mapping of the region was performed to look for similarities and differences in HLA associations between the JIA categories and define correspondences with adult inflammatory arthritides.
Dense genotype data from the HLA region, from the Immunochip array for 5043 JIA cases and 14 390 controls, were used to impute single-nucleotide polymorphisms, HLA classical alleles and amino acids. Bivariate analysis was performed to investigate genetic correlation between the JIA categories. Conditional analysis was used to identify additional effects within the region. Comparison of the findings with those in adult inflammatory arthritic diseases was performed.
We identified category-specific associations and have demonstrated for the first time that rheumatoid factor (RF)-negative polyarticular JIA and oligoarticular JIA are genetically similar in their HLA associations. We also observe that each JIA category potentially has an adult counterpart. The RF-positive polyarthritis association at HLA-DRB1 amino acid at position 13 mirrors the association in adult seropositive rheumatoid arthritis (RA). Interestingly, the combined oligoarthritis and RF-negative polyarthritis dataset shares the same association with adult seronegative RA.
The findings suggest the value of using genetic data in helping to classify the categories of this heterogeneous disease. Mapping JIA categories to adult counterparts could enable shared knowledge of disease pathogenesis and aetiology and facilitate transition from paediatric to adult services.
HLA-DRB1 is the strongest susceptibility gene to rheumatoid arthritis (RA). HLA-DRB1 alleles showed significant non-additive and interactive effects on susceptibility to RA in the European population, but these effects on RA susceptibility should vary between populations due to the difference in allelic distribution. Furthermore, non-additive or interactive effects on the phenotypes of RA are not fully known. We evaluated the non-additive and interactive effects of HLA-DRB1 alleles on RA susceptibility and anticitrullinated protein/peptide antibody (ACPA) levels in Japanese patients.
A total of 5581 ACPA(+) RA and 19 170 controls were genotyped or imputed for HLA-DRB1 alleles. Logistic regression analysis was performed for both allelic non-additive effects and interactive effects of allelic combinations. The significant levels were set by Bonferroni's correction. A total of 4371 ACPA(+) RA were analysed for ACPA levels.
We obtained evidence of non-additive and interactive effects of HLA-DRB1 on ACPA(+) RA susceptibility (p=2.5×10-5 and 1.5×10-17, respectively). Multiple HLA-DRB1 alleles including HLA-DRB1*04:05, the most common susceptibility allele in the Japanese, showed significant non-additive effects (p≤0.0043). We identified multiple allelic combinations with significant interactive effects including a common combination with the European population as well as novel combinations. Additional variance of ACPA(+) RA susceptibility could be explained substantially by heterozygote dominance or interactive effects. We did not find evidence of non-additive and interactive effects on levels of ACPA.
HLA allelic non-additive and interactive effects on ACPA(+) RA susceptibility were observed in the Japanese population. The allelic non-additive and interactive effects depend on allelic distribution in populations.
Aung T, Ozaki M, Lee MC, Schlötzer-Schrehardt U, Thorleifsson G, Mizoguchi T, Igo RP, Haripriya A, Williams SE, Astakhov YS, Orr AC, Burdon KP, Nakano S, Mori K, Abu-Amero K, Hauser M, Li Z, Prakadeeswari G, Bailey JCN, Cherecheanu AP, Kang JH, Nelson S, Hayashi K, Manabe S-I, Kazama S, Zarnowski T, Inoue K, Irkec M, Coca-Prados M, Sugiyama K, Järvelä I, Schlottmann P, Lerner FS, Lamari H, Nilgün Y, Bikbov M, Park KH, Cha SC, Yamashiro K, Zenteno JC, Jonas JB, Kumar RS, Perera SA, Chan ASY, Kobakhidze N, George R, Vijaya L, Do T, Edward DP, de Juan Marcos L, Pakravan M, Moghimi S, Ideta R, Bach-Holm D, Kappelgaard P, Wirostko B, Thomas S, Gaston D, Bedard K, Greer WL, Yang Z, Chen X, Huang L, Sang J, Jia H, Jia L, Qiao C, Zhang H, Liu X, Zhao B, Wang Y-X, Xu L, Leruez S, Reynier P, Chichua G, Tabagari S, Uebe S, Zenkel M, Berner D, Mossböck G, Weisschuh N, Hoja U, Welge-Luessen U-C, Mardin C, Founti P, Chatzikyriakidou A, Pappas T, Anastasopoulos E, Lambropoulos A, Ghosh A, Shetty R, Porporato N, Saravanan V, Venkatesh R, Shivkumar C, Kalpana N, Sarangapani S, Kanavi MR, Beni AN, Yazdani S, Lashay A, Naderifar H, Khatibi N, Fea A, Lavia C, Dallorto L, Rolle T, Frezzotti P, Paoli D, Salvi E, Manunta P, Mori Y, Miyata K, Higashide T, Chihara E, Ishiko S, Yoshida A, Yanagi M, Kiuchi Y, Ohashi T, Sakurai T, Sugimoto T, Chuman H, Aihara M, Inatani M, Miyake M, Gotoh N, Matsuda F, Yoshimura N, Ikeda Y, Ueno M, Sotozono C, Jeoung JW, Sagong M, Park KH, Ahn J, Cruz-Aguilar M, Ezzouhairi SM, Rafei A, Chong YF, Ng XY, Goh SR, Chen Y, Yong VHK, Khan MI, Olawoye OO, Ashaye AO, Ugbede I, Onakoya A, Kizor-Akaraiwe N, Teekhasaenee C, Suwan Y, Supakontanasan W, Okeke S, Uche NJ, Asimadu I, Ayub H, Akhtar F, Kosior-Jarecka E, Lukasik U, Lischinsky I, Castro V, Grossmann RP, Megevand GS, Roy S, Dervan E, Silke E, Rao A, Sahay P, Fornero P, Cuello O, Sivori D, Zompa T, Mills RA, Souzeau E, Mitchell P, Wang JJ, Hewitt AW, Coote M, Crowston JG, Astakhov SY, Akopov EL, Emelyanov A, Vysochinskaya V, Kazakbaeva G, Fayzrakhmanov R, Al-Obeidan SA, Owaidhah O, Aljasim LA, Chowbay B, Foo JN, Soh RQ, Sim KS, Xie Z, Cheong AWO, Mok SQ, Soo HM, Chen XY, Peh SQ, Heng KK, Husain R, Ho S-L, Hillmer AM, Cheng C-Y, Escudero-Domínguez FA, González-Sarmiento R, Martinon-Torres F, Salas A, Pathanapitoon K, Hansapinyo L, Wanichwecharugruang B, Kitnarong N, Sakuntabhai A, Nguyn HX, Nguyn GTT, Nguyn TV, Zenz W, Binder A, Klobassa DS, Hibberd ML, Davila S, Herms S, Nöthen MM, Moebus S, Rautenbach RM, Ziskind A, Carmichael TR, Ramsay M, Álvarez L, García M, González-Iglesias H, Rodríguez-Calvo PP, Cueto LF-V, Oguz Ç, Tamcelik N, Atalay E, Batu B, Aktas D, Kasım B, Wilson RM, Coleman AL, Liu Y, Challa P, Herndon L, Kuchtey RW, Kuchtey J, Curtin K, Chaya CJ, Crandall A, Zangwill LM, Wong TY, Nakano M, Kinoshita S, den Hollander AI, Vesti E, Fingert JH, Lee RK, Sit AJ, Shingleton BJ, Wang N, Cusi D, Qamar R, Kraft P, Pericak-Vance MA, Raychaudhuri S, Heegaard S, Kivelä T, Reis A, Kruse FE, Weinreb RN, Pasquale LR, Haines JL, Thorsteinsdottir U, Jonasson F, Allingham RR, Milea D, Ritch R, Kubota T, Tashiro K, Vithana EN, Micheal S, Topouzis F, Craig JE, Dubina M, Sundaresan P, Stefansson K, Wiggs JL, Pasutto F, Khor CC. Genetic association study of exfoliation syndrome identifies a protective rare variant at LOXL1 and five new susceptibility loci [Internet]. Nat Genet 2017;49(7):993-1004. Publisher's VersionAbstract
Exfoliation syndrome (XFS) is the most common known risk factor for secondary glaucoma and a major cause of blindness worldwide. Variants in two genes, LOXL1 and CACNA1A, have previously been associated with XFS. To further elucidate the genetic basis of XFS, we collected a global sample of XFS cases to refine the association at LOXL1, which previously showed inconsistent results across populations, and to identify new variants associated with XFS. We identified a rare protective allele at LOXL1 (p.Phe407, odds ratio (OR) = 25, P = 2.9 × 10(-14)) through deep resequencing of XFS cases and controls from nine countries. A genome-wide association study (GWAS) of XFS cases and controls from 24 countries followed by replication in 18 countries identified seven genome-wide significant loci (P < 5 × 10(-8)). We identified association signals at 13q12 (POMP), 11q23.3 (TMEM136), 6p21 (AGPAT1), 3p24 (RBMS3) and 5q23 (near SEMA6A). These findings provide biological insights into the pathology of XFS and highlight a potential role for naturally occurring rare LOXL1 variants in disease biology.
BACKGROUND: The role of low dose methotrexate (LDM) in potential serious toxicities remains unclear despite its common use. Prior observational studies investigating LDM toxicity compared LDM to other active drugs. Prior placebo-controlled clinical trials of LDM in inflammatory conditions were not large enough to investigate toxicity. The Cardiovascular Inflammation Reduction Trial (CIRT) is an ongoing NIH-funded, randomized, double-blind, placebo-controlled trial of LDM in the secondary prevention of cardiovascular disease. We describe here the rationale and design of the CIRT-Adverse Events (CIRT-AE) ancillary study which aims to investigate adverse events within CIRT. DESIGN: CIRT will randomize up to 7000 participants with cardiovascular disease and no systemic rheumatic disease to either LDM (target dose: 15-20mg/week) or placebo for an average follow-up period of 3-5 years; subjects in both treatment arms receive folic acid 1mg daily for 6 days each week. The primary endpoints of CIRT include recurrent cardio vascular events, incident diabetes, and all-cause mortality, and the ancillary CIRT-AE study has been designed to adjudicate other clinically important adverse events including hepatic, gastrointestinal, respiratory, hematologic, infectious, mucocutaneous, oncologic, renal, neurologic, and musculoskeletal outcomes. Methotrexate polyglutamate levels and genome-wide single nucleotide polymorphisms will be examined for association with adverse events. SUMMARY: CIRT-AE will comprehensively evaluate potential LDM toxicities among subjects with cardiovascular disease within the context of a large, ongoing, double-blind, placebo-controlled trial. This information may lead to a personalized approach to monitoring LDM in clinical practice.
Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.
Terao C, Kawaguchi T, Dieude P, Varga J, Kuwana M, Hudson M, Kawaguchi Y, Matucci-Cerinic M, Ohmura K, Riemekasten G, Kawasaki A, Airo P, Horita T, Oka A, Hachulla E, Yoshifuji H, Caramaschi P, Hunzelmann N, Baron M, Atsumi T, Hassoun P, Torii T, Takahashi M, Tabara Y, Shimizu M, Tochimoto A, Ayuzawa N, Yanagida H, Furukawa H, Tohma S, Hasegawa M, Fujimoto M, Ishikawa O, Yamamoto T, Goto D, Asano Y, Jinnin M, Endo H, Takahashi H, Takehara K, Sato S, Ihn H, Raychaudhuri S, Liao K, Gregersen P, Tsuchiya N, Riccieri V, Melchers I, Valentini G, Cauvet A, Martinez M, Mimori T, Matsuda F, Allanore Y. Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis [Internet]. Ann Rheum Dis 2017;76(6):1150-1158. Publisher's VersionAbstract
OBJECTIVES: Systemic sclerosis (SSc) is an autoimmune disease characterised by skin and systemic fibrosis culminating in organ damage. Previous genetic studies including genome-wide association studies (GWAS) have identified 12 susceptibility loci satisfying genome-wide significance. Transethnic meta-analyses have successfully expanded the list of susceptibility genes and deepened biological insights for other autoimmune diseases. METHODS: We performed transethnic meta-analysis of GWAS in the Japanese and European populations, followed by a two-staged replication study comprising a total of 4436 cases and 14 751 controls. Associations between significant single nuclear polymorphisms (SNPs) and neighbouring genes were evaluated. Enrichment analysis of H3K4Me3, a representative histone mark for active promoter was conducted with an expanded list of SSc susceptibility genes. RESULTS: We identified two significant SNP in two loci, GSDMA and PRDM1, both of which are related to immune functions and associated with other autoimmune diseases (p=1.4×10(-10) and 6.6×10(-10), respectively). GSDMA also showed a significant association with limited cutaneous SSc. We also replicated the associations of previously reported loci including a non-GWAS locus, TNFAIP3. PRDM1 encodes BLIMP1, a transcription factor regulating T-cell proliferation and plasma cell differentiation. The top SNP in GSDMA was a missense variant and correlated with gene expression of neighbouring genes, and this could explain the association in this locus. We found different human leukocyte antigen (HLA) association patterns between the two populations. Enrichment analysis suggested the importance of CD4-naïve primary T cell. CONCLUSIONS: GSDMA and PRDM1 are associated with SSc. These findings provide enhanced insight into the genetic and biological basis of SSc.
Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.
Meta-analysis strategies have become critical to augment power of genome-wide association studies (GWAS). To reduce genotyping or sequencing cost, many studies today utilize shared controls, and these individuals can inadvertently overlap among multiple studies. If these overlapping individuals are not taken into account in meta-analysis, they can induce spurious associations. In this article, we propose a general framework for adjusting association statistics to account for overlapping subjects within a meta-analysis. The key idea of our method is to transform the covariance structure of the data, so it can be used in downstream analyses. As a result, the strategy is very flexible and allows a wide range of meta-analysis methods, such as the random effects model, to account for overlapping subjects. Using simulations and real datasets, we demonstrate that our method has utility in meta-analyses of GWAS, as well as in a multi-tissue mouse expression quantitative trait loci (eQTL) study where our method increases the number of discovered eQTL by up to 19% compared with existing methods.
There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10(-4)) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10(-3)). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10(-9)) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10(-4)) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).