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 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 High-resolution regulatory maps connect cardiovascular risk variants to disease related pathways

2018 ◽  
Author(s):  
Örjan Åkerborg ◽  
Rapolas Spalinskas ◽  
Sailendra Pradhananga ◽  
Anandashankar Anil ◽  
Pontus Höjer ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Resolution ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Cvd Risk ◽  
Expression Levels ◽  
Functional Studies ◽  
Multiple Promoters ◽  
Risk Variants ◽  
Coding Variants

AbstractGenetic variant landscape of cardiovascular disease (CVD) is dominated by non-coding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct gene both to gain insights into perturbed functions and better assess the risk of disease. In this study, we generated high-resolution genomic interaction maps (~750 bases) in aortic endothelial, smooth muscle and THP-1 macrophages using Hi-C coupled with sequence capture targeting 25,429 features including variants associated with CVD. We detected interactions for 761 CVD risk variants obtained by genome-wide association studies (GWAS) and identified novel as well as established functions associated with CVD. We were able to fine-map 331 GWAS variants using interaction networks, thereby identifying additional genes and functions. We also discovered a subset of risk variants interacting with multiple promoters and the expression levels of such genes were correlated. The presented resource enables functional studies of cardiovascular disease providing novel approaches for its diagnosis and treatment.

scholarly journals CRISPR screens identify gene targets and drug repositioning opportunities at breast cancer risk loci

2021 ◽  
Author(s):  
Natasha K Tuano ◽  
Jonathan Beesley ◽  
Murray Manning ◽  
Wei Shi ◽  
Luis Malver-Ortega ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Risk ◽  
Drug Repositioning ◽  
Association Studies ◽  
Drug Repurposing ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Causal Variants ◽  
Cancer Phenotypes

Genome-wide association studies (GWAS) have identified >200 loci associated with breast cancer (BC) risk. The majority of candidate causal variants (CCVs) are in non-coding regions and are likely to modulate cancer risk by regulating gene expression. We recently developed a scoring system, INQUISIT, to predict candidate risk genes at BC-risk loci. Here, we used pooled CRISPR activation and suppression screens to validate INQUISIT predictions, and to define the cancer phenotypes they mediate. We measured proliferation in 2D, 3D, and in immune-deficient mice, as well as the effect on the DNA damage response. We performed 60 CRISPR screens and identified 21 high-confidence INQUISIT predictions that mediate a cancer phenotype. We validated the direct regulation of a subset of genes by BC-risk variants using HiCHIP and CRISPRqtl. Furthermore, we show the utility of expression profiling for drug repurposing against these targets. We provide a platform for identifying gene targets of risk variants, and lay a blueprint of interventions for BC risk reduction and treatment.

scholarly journals Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis

PLoS Genetics ◽  
2020 ◽  
Vol 16 (12) ◽  
pp. e1009060
Author(s):  
Corbin Quick ◽  
Xiaoquan Wen ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
Hyun Min Kang
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Early Gene ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Regulatory Variants ◽  
Causal Genes ◽  
And Performance ◽  
The Uk ◽  
Coding Variants

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

Identification of rare coding variants in TYK2 protective for rheumatoid arthritis in the Japanese population and their effects on cytokine signalling

2019 ◽  
Vol 78 (8) ◽  
pp. 1062-1069 ◽  
Author(s):  
Tomoki Motegi ◽  
Yuta Kochi ◽  
Koichi Matsuda ◽  
Michiaki Kubo ◽  
Kazuhiko Yamamoto ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Japanese Population ◽  
Association Studies ◽  
Kinase Domain ◽  
Genome Wide Association Studies ◽  
Risk Variant ◽  
Coding Regions ◽  
Genome Wide ◽  
Reporter Assays ◽  
Coding Variants

ObjectiveAlthough genome-wide association studies (GWAS) have identified approximately 100 loci for rheumatoid arthritis (RA), the disease mechanisms are not completely understood. We evaluated the pathogenesis of RA by focusing on rare coding variants.MethodsThe coding regions of 98 candidate genes identified by GWAS were sequenced in 2294 patients with RA and 4461 controls in Japan. An association analysis was performed using cases and controls for variants, genes and domains of TYK2. Cytokine responses for two associated variants (R231W, rs201917359; and R703W, rs55882956) in TYK2 as well as a previously reported risk variant (P1004A, rs34536443) for multiple autoimmune diseases were evaluated by reporter assays.ResultsA variant in TYK2 (R703W) showed a suggestive association (p=5.47×10−8, OR=0.48). We observed more accumulation of rare coding variants in controls in TYK2 (p=3.94×10−12, OR=0.56). The four-point-one, ezrin, radixin, moesin (FERM; 2.14×10−3, OR=0.66) and pseudokinase domains (1.63×10−8, OR=0.52) of TYK2 also showed enrichment of variants in controls. R231W in FERM domain especially reduced interleukin (IL)-6 and interferon (IFN)-γ signalling, whereas P1104A in kinase domain reduced IL-12, IL-23 and IFN-α signalling. R703W in pseudokinase domain reduced cytokine signals similarly to P1104A, but the effects were weaker than those of P1104A.ConclusionsThe FERM and pseudokinase domains in TYK2 were associated with the risk of RA in the Japanese population. Variants in TYK2 had different effects on cytokine signalling, suggesting that the regulation of selective cytokine signalling is a target for RA treatment.

scholarly journals find-tfbs: a tool to identify functional non-coding variants associated with complex human traits using open chromatin maps and phased whole-genome sequences

2020 ◽  
Author(s):  
Sébastian Méric de Bellefon ◽  
Florian Thibord ◽  
Paul L. Auer ◽  
John Blangero ◽  
Zeynep H Coban-Akdemir ◽  
...  
Keyword(s):  
Blood Cell ◽  
Association Studies ◽  
Supplementary Information ◽  
Open Chromatin ◽  
Regulatory Sequences ◽  
Large Sample Size ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Link Type ◽  
Coding Variants

AbstractMotivationWhole-genome DNA sequencing (WGS) enables the discovery of non-coding variants, but tools are lacking to prioritize the subset that functionally impacts human phenotypes. DNA sequence variants that disrupt or create transcription factor binding sites (TFBS) can modulate gene expression. find-tfbs efficiently scans phased WGS in large cohorts to identify and count TFBSs in regulatory sequences. This information can then be used in association testing to find putatively functional non-coding variants associated with complex human diseases or traits.ResultsWe applied find-tfbs to discover functional non-coding variants associated with hematological traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) WGS dataset (Nmax=44,709). We identified >2000 associations at P<1×10−9, implicating specific blood cell-types, transcription factors and causal genes. The vast majority of these associations are captured by variants identified in large genome-wide association studies (GWAS) for blood-cell traits. find-tfbs is computationally efficient and robust, allowing for the rapid identification of non-coding variants associated with multiple human phenotypes in very large sample size.Availabilityhttps://github.com/Helkafen/find-tfbs and https://github.com/Helkafen/[email protected] and [email protected] informationSupplementary data are available.

scholarly journals Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

2021 ◽  
Vol 12 ◽  
Author(s):  
Haojie Lu ◽  
Jinhui Zhang ◽  
Zhou Jiang ◽  
Meng Zhang ◽  
Ting Wang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Systemic Lupus Erythematosus ◽  
Genetic Correlation ◽  
Lupus Erythematosus ◽  
Association Studies ◽  
Integrative Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Systemic Lupus ◽  
Genetic Components

BackgroundClinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.MethodsOur analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.ResultsWe found there existed a significant positive genetic correlation (rg = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.ConclusionThis study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

scholarly journals Integrating Comprehensive Functional Annotations to Boost Power and Accuracy in Gene-Based Association Analysis

2019 ◽  
Author(s):  
Corbin Quick ◽  
Xiaoquan Wen ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
Hyun Min Kang
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Early Gene ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Regulatory Variants ◽  
Causal Genes ◽  
And Performance ◽  
The Uk ◽  
Coding Variants

AbstractGene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

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 Insights from complex trait fine-mapping across diverse populations

2021 ◽  
Author(s):  
Masahiro Kanai ◽  
Jacob C Ulirsch ◽  
Juha Karjalainen ◽  
Mitja Kurki ◽  
Konrad J Karczewski ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
High Confidence ◽  
Causal Variants ◽  
Coding Variants

AbstractDespite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified1–3. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan4,5, FinnGen6, and UK Biobank7,8; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.

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