scholarly journals Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiangyu Ge ◽  
Mojca Frank-Bertoncelj ◽  
Kerstin Klein ◽  
Amanda McGovern ◽  
Tadeja Kuret ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Cellular Proliferation ◽  
Immune Cell ◽  
Association Studies ◽  
Synovial Fibroblasts ◽  
Cell Types ◽  
Joint Involvement ◽  
Genome Wide Association Studies ◽  
Causal Genes

Abstract Background Genome-wide association studies have reported more than 100 risk loci for rheumatoid arthritis (RA). These loci are shown to be enriched in immune cell-specific enhancers, but the analysis so far has excluded stromal cells, such as synovial fibroblasts (FLS), despite their crucial involvement in the pathogenesis of RA. Here we integrate DNA architecture, 3D chromatin interactions, DNA accessibility, and gene expression in FLS, B cells, and T cells with genetic fine mapping of RA loci. Results We identify putative causal variants, enhancers, genes, and cell types for 30–60% of RA loci and demonstrate that FLS account for up to 24% of RA heritability. TNF stimulation of FLS alters the organization of topologically associating domains, chromatin state, and the expression of putative causal genes such as TNFAIP3 and IFNAR1. Several putative causal genes constitute RA-relevant functional networks in FLS with roles in cellular proliferation and activation. Finally, we demonstrate that risk variants can have joint-specific effects on target gene expression in RA FLS, which may contribute to the development of the characteristic pattern of joint involvement in RA. Conclusion Overall, our research provides the first direct evidence for a causal role of FLS in the genetic susceptibility for RA accounting for up to a quarter of RA heritability.

scholarly journals Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability

2020 ◽  
Author(s):  
Xiangyu Ge ◽  
Mojca Frank-Bertoncelj ◽  
Kerstin Klein ◽  
Amanda Mcgovern ◽  
Tadeja Kuret ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Cellular Proliferation ◽  
Immune Cell ◽  
Association Studies ◽  
Synovial Fibroblasts ◽  
Cell Types ◽  
Joint Involvement ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have reported >100 risk loci for rheumatoid arthritis (RA). These loci have been shown to be enriched in immune cell-specific enhancers, but analysis so far has excluded stromal cells, such as synovial fibroblasts (FLS), despite their crucial involvement in the pathogenesis of RA. Here we integrated DNA architecture (ChIP-seq), 3D chromatin interactions (HiC, capture HiC), DNA accessibility (ATAC-seq) and gene expression (RNA-seq) in FLS, B cells and T cells with genetic fine mapping of RA loci. We identified putative causal variants, enhancers, genes, and cell types for 30 - 60% of RA loci and demonstrated that FLS account for up to 24% of RA heritability. TNF stimulation of FLS altered the organization of topologically associating domains (TADs), chromatin state and the expression of putative causal genes (e.g. TNFAIP3, IFNAR1). Several putative causal genes constituted RA-relevant functional networks in FLS with roles in cellular proliferation and activation. Finally, we demonstrated that risk variants can have joint-specific effects on target gene expression in RA FLS, which may contribute to the development of the characteristic pattern of joint involvement in RA. Overall, our research provides the first direct evidence for a causal role of FLS in the genetic susceptibility for RA accounting for up to a quarter of RA heritability.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals Multiomics landscape of synovial fibroblasts in rheumatoid arthritis

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Haruka Tsuchiya ◽  
Mineto Ota ◽  
Keishi Fujio
Keyword(s):  
Rheumatoid Arthritis ◽  
Genome Analysis ◽  
Association Studies ◽  
Therapeutic Targets ◽  
Synovial Cell ◽  
Synovial Fibroblasts ◽  
Mesenchymal Cells ◽  
Integrated Analysis ◽  
Main Body ◽  
Genome Wide Association Studies

Abstract Background Rheumatoid arthritis (RA) is an autoimmune disease characterized by tumor-like hyperplasia and inflammation of the synovium, which causes synovial cell invasion into the bone and cartilage. In RA pathogenesis, various molecules in effector cells (i.e., immune cells and mesenchymal cells) are dysregulated by genetic and environmental factors. Synovial fibroblasts (SFs), the most abundant resident mesenchymal cells in the synovium, are the major local effectors of the destructive joint inflammation and exert their effects through the pathogenic production of molecules such as interleukin-6. Main body To date, more than 100 RA susceptibility loci have been identified in genome-wide association studies (GWASs), and finding novel therapeutic targets utilizing genome analysis is considered a promising approach because some candidate causal genes identified by GWASs have previously been established as therapeutic targets. For further exploration of RA-responsible cells and cell type-specific therapeutic targets, integrated analysis (or functional genome analysis) of the genome and intermediate traits (e.g., transcriptome and epigenome) is crucial. Conclusion This review builds on the existing knowledge regarding the epigenomic abnormalities in RASFs and discusses the recent advances in single-cell analysis, highlighting the prospects of SFs as targets for safer and more effective therapies against RA.

scholarly journals Chronic lymphocytic leukemia (CLL) risk is mediated by multiple enhancer variants within CLL risk loci

2020 ◽  
Vol 29 (16) ◽  
pp. 2761-2774
Author(s):  
Huihuang Yan ◽  
Shulan Tian ◽  
Geffen Kleinstern ◽  
Zhiquan Wang ◽  
Jeong-Heon Lee ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Immune Cell ◽  
Association Studies ◽  
Cell Types ◽  
Lymphocytic Leukemia ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Gene Promoters ◽  
Functional Variants ◽  
Increased Risk

Abstract Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in Western countries. It has a strong genetic basis, showing a ~ 8-fold increased risk of CLL in first-degree relatives. Genome-wide association studies (GWAS) have identified 41 risk variants across 41 loci. However, for a majority of the loci, the functional variants and the mechanisms underlying their causal roles remain undefined. Here, we examined the genetic and epigenetic features associated with 12 index variants, along with any correlated (r2 ≥ 0.5) variants, at the CLL risk loci located outside of gene promoters. Based on publicly available ChIP-seq and chromatin accessibility data as well as our own ChIP-seq data from CLL patients, we identified six candidate functional variants at six loci and at least two candidate functional variants at each of the remaining six loci. The functional variants are predominantly located within enhancers or super-enhancers, including bi-directionally transcribed enhancers, which are often restricted to immune cell types. Furthermore, we found that, at 78% of the functional variants, the alternative alleles altered the transcription factor binding motifs or histone modifications, indicating the involvement of these variants in the change of local chromatin state. Finally, the enhancers carrying functional variants physically interacted with genes enriched in the type I interferon signaling pathway, apoptosis, or TP53 network that are known to play key roles in CLL. These results support the regulatory roles for inherited noncoding variants in the pathogenesis of CLL.

scholarly journals Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

2019 ◽  
Author(s):  
James Boocock ◽  
Megan Leask ◽  
Yukinori Okada ◽  
Hirotaka Matsuo ◽  
Yusuke Kawamura ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Organic Anion ◽  
Serum Urate ◽  
Regulatory Function ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Genes

AbstractSerum urate is the end-product of purine metabolism. Elevated serum urate is causal of gout and a predictor of renal disease, cardiovascular disease and other metabolic conditions. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control, however there has been little progress in understanding the molecular basis of the associated loci. Here we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify ten new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new loci. By cis- and trans-eQTL colocalization analysis we identified 24 and 20 genes respectively where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway; and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Trans-ancestral functional fine-mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1, HNF4G) with colocalized eQTL that contained putative causal SNPs (posterior probability of causality > 0.8). This systematic analysis of serum urate GWAS loci has identified candidate causal genes at 19 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.Author SummaryHigh serum urate is a prerequisite for gout and a risk factor for metabolic disease. Previous GWAS have identified numerous loci that are associated with serum urate control, however, only a small handful of these loci have known molecular consequences. The majority of loci are within the non-coding regions of the genome and therefore it is difficult to ascertain how these variants might influence serum urate levels without tangible links to gene expression and / or protein function. We have applied a novel bioinformatic pipeline where we combined population-specific GWAS data with gene expression and genome connectivity information to identify putative causal genes for serum urate associated loci. Overall, we identified 15 novel serum urate loci and show that these loci along with previously identified loci are linked to the expression of 44 genes. We show that some of the variants within these loci have strong predicted regulatory function which can be further tested in functional analyses. This study expands on previous GWAS by identifying further loci implicated in serum urate control and new causal mechanisms supported by gene expression changes.

scholarly journals Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

2020 ◽  
Author(s):  
Edward Mountjoy ◽  
Ellen M. Schmidt ◽  
Miguel Carmona ◽  
Gareth Peat ◽  
Alfredo Miranda ◽  
...  
Keyword(s):  
Complex Traits ◽  
Drug Targets ◽  
Association Studies ◽  
Single Gene ◽  
Cell Types ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Open Approach ◽  
Protein Coding ◽  
Causal Genes

AbstractGenome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

scholarly journals Genetics of rheumatoid arthritis: 2018 status

2018 ◽  
Vol 78 (4) ◽  
pp. 446-453 ◽  
Author(s):  
Yukinori Okada ◽  
Stephen Eyre ◽  
Akari Suzuki ◽  
Yuta Kochi ◽  
Kazuhiko Yamamoto
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Studies ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Genetic Components ◽  
Risk Variants ◽  
Causal Genes ◽  
Causal Variants ◽  
Resolution Mapping ◽  
Coding Variants

Study of the genetics of rheumatoid arthritis (RA) began about four decades ago with the discovery of HLA-DRB1. Since the beginning of this century, a number of non-HLA risk loci have been identified through genome-wide association studies (GWAS). We now know that over 100 loci are associated with RA risk. Because genetic information implies a clear causal relationship to the disease, research into the pathogenesis of RA should be promoted. However, only 20% of GWAS loci contain coding variants, with the remaining variants occurring in non-coding regions, and therefore, the majority of causal genes and causal variants remain to be identified. The use of epigenetic studies, high-resolution mapping of open chromatin, chromosomal conformation technologies and other approaches could identify many of the missing links between genetic risk variants and causal genetic components, thus expanding our understanding of RA genetics.

scholarly journals Schizophrenia Risk Alleles Often Affect The Expression of Many Genes and Each Gene May Have a Different Effect On The Risk; A Mediation Analysis

2020 ◽  
Author(s):  
Xi Peng ◽  
Joel S. Bader ◽  
Dimitrios Avramopoulos
Keyword(s):  
Gene Expression ◽  
Mediation Analysis ◽  
Target Genes ◽  
Risk Allele ◽  
Association Studies ◽  
Cell Types ◽  
Developmental Time ◽  
Genome Wide Association Studies ◽  
Expression Change ◽  
Risk Alleles

ABSTRACTVariants identified by genome-wide association studies (GWAS) are often expression quantitative trait loci (eQTLs), suggesting they are proxies or are themselves regulatory. Across many datasets analyses show that variants often affect multiple genes. Lacking data on many tissue types, developmental time points and homogeneous cell types, the extent of this one-to-many relationship is underestimated. This raises questions on whether a disease eQTL target gene explains the genetic association or is a by-stander and puts into question the direction of expression effect of on the risk, since the many variant - regulated genes may have opposing effects, imperfectly balancing each other. We used two brain gene expression datasets (CommonMind and BrainSeq) for mediation analysis of schizophrenia-associated variants. We confirm that eQTL target genes often mediate risk but the direction in which expression affects risk is often different from that in which the risk allele changes expression. Of 38 mediator genes significant in both datasets 33 showed consistent mediation direction (Chi2 test P=6*10−6). One might expect that the expression would correlate with the risk allele in the same direction it correlates with disease. For 15 of these 33 (45%), however, the expression change associated with the risk allele was protective, suggesting the likely presence of other target genes with overriding effects. Our results identify specific risk mediating genes and suggest caution in interpreting the biological consequences of targeted modifications of gene expression, as not all eQTL targets may be relevant to disease while those that are, might have different than expected directions.

scholarly journals Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

2020 ◽  
Author(s):  
Yanyu Liang ◽  
François Aguet ◽  
Alvaro Barbeira ◽  
Kristin Ardlie ◽  
Hae Kyung Im
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Average Power ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Causal Genes ◽  
Allele Specific

AbstractGenome-wide association studies (GWAS) have been highly successful in identifying genomic loci associated with complex traits. However, identification of the causal genes that mediate these associations remains challenging, and many approaches integrating transcriptomic data with GWAS have been proposed. However, there currently exist no computationally scalable methods that integrate total and allele-specific gene expression to maximize power to detect genetic effects on gene expression. Here, we describe a unified framework that is scalable to studies with thousands of samples. Using simulations and data from GTEx, we demonstrate an average power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and prediction.

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