Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis

Targeting TNF/TNFR superfamilies in immune-mediated inflammatory diseases

Dysregulated signaling from TNF and TNFR proteins is implicated in several immune-mediated inflammatory diseases (IMIDs). This review centers around seven IMIDs (rheumatoid arthritis, systemic lupus erythematosus, Crohn's disease, ulcerative colitis, psoriasis, atopic dermatitis, and asthma) with substantial unmet medical needs and sheds light on the signaling mechanisms, disease relevance, and evolving drug development activities for five TNF/TNFR signaling axes that garner substantial drug development interest in these focus conditions. The review also explores the current landscape of therapeutics, emphasizing the limitations of the approved biologics, and the opportunities presented by small-molecule inhibitors and combination antagonists of TNF/TNFR signaling.

Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease

The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation has been challenging due to small effects, but experimental perturbations offer a complementary approach. Using CRISPR, we knocked out 84 genes in primary CD4+ T cells, targeting inborn error of immunity (IEI) disease transcription factors (TFs) and TFs without immune disease association. We developed a novel gene network inference method called linear latent causal Bayes (LLCB) to estimate the network from perturbation data and observed 211 regulatory connections between genes. We characterized programs affected by the TFs, which we associated with immune genome-wide association study (GWAS) genes, finding that JAK-STAT family members are regulated by KMT2A, an epigenetic regulator. These analyses reveal the trans-regulatory cascades linking GWAS genes to signaling pathways.

Evolving understanding of autoimmune mechanisms and new therapeutic strategies of autoimmune disorders

Autoimmune disorders are characterized by aberrant T cell and B cell reactivity to the body's own components, resulting in tissue destruction and organ dysfunction. Autoimmune diseases affect a wide range of people in many parts of the world and have become one of the major concerns in public health. In recent years, there have been substantial progress in our understanding of the epidemiology, risk factors, pathogenesis and mechanisms of autoimmune diseases. Current approved therapeutic interventions for autoimmune diseases are mainly non-specific immunomodulators and may cause broad immunosuppression that leads to serious adverse effects. To overcome the limitations of immunosuppressive drugs in treating autoimmune diseases, precise and target-specific strategies are urgently needed. To date, significant advances have been made in our understanding of the mechanisms of immune tolerance, offering a new avenue for developing antigen-specific immunotherapies for autoimmune diseases. These antigen-specific approaches have shown great potential in various preclinical animal models and recently been evaluated in clinical trials. This review describes the common epidemiology, clinical manifestation and mechanisms of autoimmune diseases, with a focus on typical autoimmune diseases including multiple sclerosis, type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus, and sjögren's syndrome. We discuss the current therapeutics developed in this field, highlight the recent advances in the use of nanomaterials and mRNA vaccine techniques to induce antigen-specific immune tolerance.

A genome-wide association study identified PTPN2 as a population-specific susceptibility gene locus for primary biliary cholangitis

BACKGROUND AND AIMS

Previous genome-wide association studies (GWAS) have indicated the involvement of shared (population-nonspecific) and nonshared (population-specific) susceptibility genes in the pathogenesis of primary biliary cholangitis (PBC) among European and East-Asian populations. Although a meta-analysis of these distinct populations has recently identified more than 20 novel PBC susceptibility loci, analyses of population-specific genetic architecture are still needed for a more comprehensive search for genetic factors in PBC.

APPROACH AND RESULTS

Protein tyrosine phosphatase nonreceptor type 2 ( PTPN2) was identified as a novel PBC susceptibility gene locus through GWAS and subsequent genome-wide meta-analysis involving 2181 cases and 2699 controls from the Japanese population (GWAS-lead variant: rs8098858, p = 2.6 × 10 -8 ). In silico and in vitro functional analyses indicated that the risk allele of rs2292758, which is a primary functional variant, decreases PTPN2 expression by disrupting Sp1 binding to the PTPN2 promoter in T follicular helper cells and plasmacytoid dendritic cells. Infiltration of PTPN2-positive T-cells and plasmacytoid dendritic cells was confirmed in the portal area of the PBC liver by immunohistochemistry. Furthermore, transcriptomic analysis of PBC-liver samples indicated the presence of a compromised negative feedback loop in vivo between PTPN2 and IFNG in patients carrying the risk allele of rs2292758.

CONCLUSIONS

PTPN2 , a novel susceptibility gene for PBC in the Japanese population, may be involved in the pathogenesis of PBC through an insufficient negative feedback loop caused by the risk allele of rs2292758 in IFN-γ signaling. This suggests that PTPN2 could be a potential molecular target for PBC treatment.

Distinct mucosal endotypes as initiators and drivers of rheumatoid arthritis

Rheumatoid arthritis (RA) is a potentially devastating autoimmune disease. The great majority of patients with RA are seropositive for anti-citrullinated protein antibodies (ACPAs), rheumatoid factors, or other autoantibodies. The onset of clinically apparent inflammatory arthritis meeting classification criteria (clinical RA) is preceded by ACPA seropositivity for an average of 3-5 years, a period that is designated as 'at-risk' of RA for ACPA-positive individuals who do not display signs of arthritis, or 'pre-RA' for individuals who are known to have progressed to developing clinical RA. Prior studies of individuals at-risk of RA have associated pulmonary mucosal inflammation with local production of ACPAs and rheumatoid factors, leading to development of the 'mucosal origins hypothesis'. Recent work now suggests the presence of multiple distinct mucosal site-specific mechanisms that drive RA evolution. Indicatively, subsets of individuals at-risk of RA and patients with RA harbour a faecal bacterial strain that has exhibited arthritogenic activity in animal models and that favours T helper 17 (TH17) cell responses in patients. Periodontal inflammation and oral microbiota have also been suggested to promote the development of arthritis through breaches in the mucosal barrier. Herein, we argue that mucosal sites and their associated microbial strains can contribute to RA evolution via distinct pathogenic mechanisms, which can be considered causal mucosal endotypes. Future therapies instituted for prevention in the at-risk period, or, perhaps, during clinical RA as therapeutics for active arthritis, will possibly have to address these individual mechanisms as part of precision medicine approaches.

Integrating Genome-Wide Polygenic Risk Scores With Nongenetic Models to Predict Surgical Site Infection After Total Knee Arthroplasty Using United Kingdom Biobank Data

BACKGROUND

Prediction of the risk of developing surgical site infection (SSI) in patients following total knee arthroplasty (TKA) is of clinical importance. Genetic susceptibility is involved in developing TKA-related SSI. Previously reported models for predicting SSI were constructed using nongenetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database.

METHODS

Adult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Nongenetic covariates, including demographic data and preoperative comorbidities, were recorded. Genetic variants associated with SSI were identified by GWAS and included to obtain standardized polygenic risk scores (zPRS, an estimate of genetic risk). Prediction models were established through analyses of multivariable logistic regression and the receiver operating characteristic curve.

RESULTS

There were 4 variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed 7 significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the receiver operating characteristic curve were 0.628 and 0.708 when zPRS (model 1) and nongenetic covariates (model 2) were used as predictors, respectively. The areas under the receiver operating characteristic curve increased to 0.76 when both zPRS and nongenetic covariates (model 3) were used as predictors. A risk-prediction nomogram was constructed based on model 3 to visualize the relative effect of statistically significant covariates on the risk of SSI and predict the probability of developing SSI. Age and zPRS were the top 2 covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks.

CONCLUSIONS

Our GWAS identified 4 novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with nongenetic risk factors improved the performance of the model in predicting SSI.

Explainable biology for improved therapies in precision medicine: AI is not enough

Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we formulate and test biological hypotheses, and how we treat patients. Most complex diseases arise on a background of genetics, lifestyle and environment factors, and manifest themselves as a spectrum of symptoms. To fathom intricate biological processes and their changes from healthy to disease states, we need to systematically integrate and analyze multi-omics datasets, ontologies, and diverse annotations. Without proper management of such complex biological and clinical data, artificial intelligence (AI) algorithms alone cannot be effectively trained, validated, and successfully applied to provide trustworthy and patient-centric diagnosis, prognosis and treatment. Precision medicine requires to use multi-omics approaches effectively, and offers many opportunities for using AI, "big data" analytics, and integrative computational biology workflows. Advances in optical and biochemical assay technologies including sequencing, mass spectrometry and imaging modalities have transformed research by empowering us to simultaneously view all genes expressed, identify proteome-wide changes, and assess interacting partners of each individual protein within a dynamically changing biological system, at an individual cell level. While such views are already having an impact on our understanding of healthy and disease conditions, it remains challenging to extract useful information comprehensively and systematically from individual studies, ensure that signal is separated from noise, develop models, and provide hypotheses for further research. Data remain incomplete and are often poorly connected using fragmented biological networks. In addition, statistical and machine learning models are developed at a cohort level and often not validated at the individual patient level. Combining integrative computational biology and AI has the potential to improve understanding and treatment of diseases by identifying biomarkers and building explainable models characterizing individual patients. From systematic data analysis to more specific diagnostic, prognostic and predictive biomarkers, drug mechanism of action, and patient selection, such analyses influence multiple steps from prevention to disease characterization, and from prognosis to drug discovery. Data mining, machine learning, graph theory and advanced visualization may help identify diagnostic, prognostic and predictive biomarkers, and create causal models of disease. Intertwining computational prediction and modeling with biological experiments leads to faster, more biologically and clinically relevant discoveries. However, computational analysis results and models are going to be only as accurate and useful as correct and comprehensive are the networks, ontologies and datasets used to build them. High quality, curated data portals provide the necessary foundation for translational research. They help to identify better biomarkers, new drugs, precision treatments, and should lead to improved patient outcomes and their quality of life. Intertwining computational prediction and modeling with biological experiments, efficiently and effectively leads to more useful findings faster.

Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

Updating on pregnancy in rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA), the most prevalent autoimmune disease in reproductive years, exhibits a higher incidence in females, suggesting involvement of estrogens, genetics and environmental factors in disease onset. Literature shows smaller families in RA patients, driving increased interest in Assisted Reproductive Techniques.

AREAS COVERED

This review elucidates how immunotolerance mechanisms contribute to favorable pregnancy outcomes in RA, emphasizing the need for a careful pregnancy planning to mitigate fetal complications and postnatal flares, which surpass those in the general population. A thorough medication evaluation, orchestrated by a multidisciplinary team, is imperative during pregnancy, weighing potential teratogenic effects against safer alternatives to balance medication safety with disease control. A systematic literature search on PubMed and MEDLINE, using specific terms, covered relevant academic journals up to the latest date.

EXPERT OPINION

This narrative review comprehensively addresses pregnancy-related considerations in RA patients, prioritizing meticulous disease management with pregnancy and breastfeeding-compatible drugs in line with the latest recommendations and registry data. The focus remains on evaluating glucocorticoids, conventional, and biological disease-modifying drugs for compatibility during pregnancy and breastfeeding. Additionally, the evolving landscape of targeted synthetic drugs during pregnancy is explored, providing insights into the latest developments in rheumatological care.

Epidemiologic and genetic associations between primary biliary cholangitis and extrahepatic rheumatic diseases

Patients with primary biliary cholangitis (PBC) commonly experience extrahepatic rheumatic diseases. However, the epidemiologic and genetic associations as well as causal relationship between PBC and these extrahepatic conditions remain undetermined. In this study, we first conducted systematic review and meta-analyses by analyzing 73 studies comprising 334,963 participants across 17 countries and found strong phenotypic associations between PBC and rheumatic diseases. Next, we utilized large-scale genome-wide association study summary data to define the shared genetic architecture between PBC and rheumatic diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and Sjögren's syndrome (SS). We observed significant genetic correlations between PBC and each of the four rheumatic diseases. Pleiotropy and heritability enrichment analysis suggested the involvement of humoral immunity and interferon-associated processes for the comorbidity. Of note, we identified four variants shared between PBC and RA (rs80200208), SLE (rs9843053), and SSc (rs27524, rs3873182) using cross-trait meta-analysis. Additionally, several pleotropic loci for PBC and rheumatic diseases were found to share causal variants with gut microbes possessing immunoregulatory functions. Finally, Mendelian randomization revealed consistent evidence for a causal effect of PBC on RA, SLE, SSc, and SS, but no or inconsistent evidence for a causal effect of extrahepatic rheumatic diseases on PBC. Our study reveals a profound genetic overlap and causal relationships between PBC and extrahepatic rheumatic diseases, thus providing insights into shared biological mechanisms and novel therapeutic interventions.

Global impact of unproductive splicing on human gene expression

Alternative splicing (AS) in human genes is widely viewed as a mechanism for enhancing proteomic diversity. AS can also impact gene expression levels without increasing protein diversity by producing 'unproductive' transcripts that are targeted for rapid degradation by nonsense-mediated decay (NMD). However, the relative importance of this regulatory mechanism remains underexplored. To better understand the impact of AS-NMD relative to other regulatory mechanisms, we analyzed population-scale genomic data across eight molecular assays, covering various stages from transcription to cytoplasmic decay. We report threefold more unproductive splicing compared with prior estimates using steady-state RNA. This unproductive splicing compounds across multi-intronic genes, resulting in 15% of transcript molecules from protein-coding genes being unproductive. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are as often associated with NMD-induced expression level differences as with differences in protein isoform usage. Our findings suggest that much of the impact of AS is mediated by NMD-induced changes in gene expression rather than diversification of the proteome.

Genetic regulation of B cell receptor signaling pathway: Insights from expression quantitative trait locus analysis using a mixed model

The B cell receptor (BCR) signaling pathway regulates non-immune cellular response through various pathways like MAPK, NF-kB, and PI3K-Akt. This study aimed to identify expression quantitative trait loci (eQTL) and their regulatory functions on BCR signaling pathway genes. A mixed model was employed to analyze eQTL using RNA expression levels in lymphoblastoid from 376 Europeans in the GEUVADIS dataset. In total, 266 SNPs, including 115 cis-acting SNPs, were found for association with transcription of 13 genes (P < 5 × 10-8), revealing 19 independent signals for five genes through linkage disequilibrium analysis. Functional analysis, aligning them with DNase sensitive sites, transcription factor binding sites, histone modification, promoters/enhancers, CpG islands, and ChIA-PET, identified regulatory variants targeting SYK, VAV2, and PLCG2. Notably, rs2562397 was validated as a SYK promoter variant, and rs694505, rs636667, and rs4889409 were confirmed as enhancer variants for VAV2 and PLCG2. Their allelic differences in gene expression were also confirmed using ENCODE ChIP-seq and Sei neural network prediction. Persistent differential expression of these genes by alleles might impact the adaptive immune system, vascular development, and/or relevant diseases that have been previously associated with other variants of the genes. Comprehensive genetic architecture studies of the BCR signaling pathway, along with experiments demonstrating related mechanisms, will greatly contribute to understanding the underlying mechanisms of relevant disease development and implementing precision medicine.

A20 haploinsufficiency disturbs immune homeostasis and drives the transformation of lymphocytes with permissive antigen receptors

Genetic TNFAIP3 (A20) inactivation is a classical somatic lymphoma lesion and the genomic trait in haploinsufficiency of A20 (HA20). In a cohort of 34 patients with HA20, we show that heterozygous TNFAIP3 loss skews immune repertoires toward lymphocytes with classical self-reactive antigen receptors typically found in B and T cell lymphomas. This skewing was mediated by a feed-forward tumor necrosis factor (TNF)/A20/nuclear factor κB (NF-κB) loop that shaped pre-lymphoma transcriptome signatures in clonally expanded B (CD81, BACH2, and NEAT1) or T (GATA3, TOX, and PDCD1) cells. The skewing was reversed by anti-TNF treatment but could also progress to overt lymphoma. Analysis of conditional TNFAIP3 knock-out mice reproduced the wiring of the TNF/A20/NF-κB signaling axis with permissive antigen receptors and suggested a distinct regulation in B and T cells. Together, patients with the genetic disorder HA20 provide an exceptional window into A20/TNF/NF-κB-mediated control of immune homeostasis and early steps of lymphomagenesis that remain clinically unrecognized.

Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

Role and mechanism of IRF9 in promoting the progression of rheumatoid arthritis by regulating macrophage polarization via PSMA5

Aim

To explore the mechanisms of IRF9 in the progression of rheumatoid arthritis(RA), and the effects of IRF9 on M1/M2 polarization.

Methods

RA dataset (GSE55457) was downloaded from GEO. Correlation analysis between IRF9 and its downstream target protein PSMA5 was performed using bioinformatics analysis. The M1/M2 cell ratio of peripheral blood mononuclear cells which from 20 healthy specimen and 40 RA patients was determined. The expression of IRF9 and PSMA5 was detected using qPCR and Western blot. Then, knockdown IRF9 in RAW264.7 cell line (sh-IRF9 RAW264.7) was constructed. The effect of sh-IRF9 RAW264.7 on RA was explored by constructing a CIA mouse model.

Results

IRF9 is upregulated in RA and is of good early screening effect. The results of pathway analysis showed that IRF9 targets and regulates the PSMA5 signaling pathway. IRF9 and PSMA5 were significantly elevated in RA patients, M1/M2 ratio was also increased. The effects of IRF9 on RAW264.7 macrophages were deeply explored in vitro, revealing that knockdown of IRF9 suppressed PSMA5, M1/M2 ratio and the secretion of pro-inflammatory factor in RAW264.7. In mouse in vivo experiments, sh-IRF9 RAW264.7 cells were found to modulate RA by downregulating PSMA5, modulating the M1/M2 ratio through enhancing the anti-inflammatory factor, and suppressing the pro-inflammatory factor.

Conclusion

IRF9 promoted the progression of RA via regulating macrophage polarization through PSMA5.

Reduced function of the adaptor SH2B3 promotes T1D via altered gc cytokine-regulated, T cell intrinsic immune tolerance

Genome-wide association studies have identified SH2B3 as an important non-MHC gene for islet autoimmunity and type 1 diabetes (T1D). In this study, we found a single SH2B3 haplotype significantly associated with increased risk for human T1D, and this haplotype carries the single nucleotide variant rs3184504*T in SH2B3. To better characterize the role of SH2B3 in T1D, we used mouse modeling and found a T cell-intrinsic role for SH2B3 regulating peripheral tolerance. SH2B3 deficiency had minimal effect on TCR signaling or proliferation across antigen doses, yet enhanced cell survival and cytokine signaling including common gamma chain-dependent and interferon-gamma receptor signaling. SH2B3 deficient CD8+T cells showed augmented STAT5-MYC and effector-related gene expression partially reversed with blocking autocrine IL-2 in culture. Using the RIP-mOVA model, we found CD8+ T cells lacking SH2B3 promoted early islet destruction and diabetes without requiring CD4+ T cell help. SH2B3-deficient cells demonstrated increased survival post-transfer compared to control cells despite a similar proliferation profile in the same host. Next, we created a spontaneous NOD .Sh2b3 -/- mouse model and found markedly increased incidence and accelerated T1D across sexes. Collectively, these studies identify SH2B3 as a critical mediator of peripheral T cell tolerance limiting the T cell response to self-antigens.

Article Highlights

The rs3184504 polymorphism, encoding a hypomorphic variant of the negative regulator SH2B3, strongly associates with T1D.SH2B3 deficiency results in hypersensitivity to cytokines, including IL-2, in murine CD4+ and CD8+ T cells.SH2B3 deficient CD8+ T cells exhibit a comparable transcriptome to wild-type CD8+ T cells at baseline, but upon antigen stimulation SH2B3 deficient cells upregulate genes characteristic of enhanced JAK/STAT signaling and effector functions.We found a T-cell intrinsic role of SH2B3 leading to severe islet destruction in an adoptive transfer murine T1D model, while global SH2B3 deficiency accelerated spontaneous NOD diabetes across sexes.

Gut microbiota as a sensor of autoimmune response and treatment for rheumatoid arthritis

Rheumatoid arthritis (RA) is considered a multifactorial condition where interaction between the genetic and environmental factors lead to immune dysregulation causing autoreactivity. While among the various genetic factors, HLA-DR4 and DQ8, have been reported to be the strongest risk factors, the role of various environmental factors has been unclear. Though events initiating autoreactivity remain unknown, a mucosal origin of RA has gained attention based on the recent observations with the gut dysbiosis in patients. However, causality of gut dysbiosis has been difficult to prove in humans. Mouse models, especially mice expressing RA-susceptible and -resistant HLA class II genes have helped unravel the complex interactions between genetic factors and gut microbiome. This review describes the interactions between HLA genes and gut dysbiosis in sex-biased preclinical autoreactivity and discusses the potential use of endogenous commensals as indicators of treatment efficacy as well as therapeutic tool to suppress pro-inflammatory response in rheumatoid arthritis.

Gastroesophageal reflux disease increases the risk of rheumatoid arthritis: a bidirectional two-sample Mendelian randomization study

Rheumatoid arthritis (RA) is a common autoimmune disease, and some observational studies have indicated an association between Gastroesophageal Reflux Disease (GERD) and RA. However, the causal relationship between the two remains uncertain. We used Mendelian randomization (MR) to assess the causal relationship between GERD and RA. Two-sample Mendelian randomization analysis was performed using pooled data from large-scale genome-wide association studies. In addition, we performed multivariate MR analyses to exclude confounding factors between GERD and RA, including smoking quantity, drinking frequency, BMI, depression, and education attainment. The MR results for GERD on RA suggested a causal effect of the genetic susceptibility of GERD on RA (discovery dataset, IVW, odds ratio [OR] = 1.41, 95% confidence interval [CI] 1.22-1.63, p = 2.81 × 10-6; validation dataset, IVW, OR = 1.38, 95% CI 1.23-1.55, P = 1.76 × 10-8). Multivariate MR analysis also supports this result. But the results of the reverse MR analysis did not reveal compelling evidence that RA can increase the risk of developing GERD. Our bidirectional Two-Sample Mendelian randomization analysis and multivariate MR analysis provide support for the causal effect of GERD on RA. This discovery could offer new insights for the prevention and treatment of RA.

Investigating the Bidirectional Association of Rheumatoid Arthritis and Thyroid Function: A Methodologic Assessment of Mendelian Randomization

OBJECTIVE

Rheumatoid arthritis (RA) and thyroid dysfunction are frequently observed in the same patient. However, whether they co-occur or exhibit a causal relationship remains uncertain. We aimed to systematically investigate the causal relationship between RA and thyroid function using a large sample and advanced methods.

METHODS

Bidirectional two-sample Mendelian randomization (MR) analysis was performed based on RA and six thyroid function trait data sets from the European population. The robustness of the results was demonstrated using multiple MR methods and a series of sensitivity analyses. Multivariable MR using Bayesian model averaging (MR-BMA) was performed to adjust for possible competing risk factors. A sensitivity data set, which included data from patients with seropositive RA and controls, was used to repeat the analyses. Furthermore, enrichment analysis was employed to discover the underlying mechanism between RA and thyroid functions.

RESULTS

A significantly positive causal effect was identified for RA on autoimmune thyroid disease (AITD) as well as for AITD on RA (P < 0.001). Further sensitivity analyses showed consistent causal estimates from a variety of MR methods. After removing the outliers, MR-BMA results showed that RA and AITD were independent risk factors in their bidirectional causality, even in the presence of other competing risk factors (adjusted P < 0.05). Enrichment analysis showed immune cell activation and immune response play crucial roles in them.

CONCLUSION

Our results illustrate the significant bidirectional causal effect of RA and AITD, which holds even in multiple competing risk factors. Clinical screening for thyroid dysfunction in patients with RA deserves further attention, and vice versa.

Patterns of Immune Dysregulation in Bipolar Disorder

Background

Bipolar disorder is a debilitating mood disorder associated with a high risk of suicide and characterized by immune dysregulation. In this study, we used a multi-faceted approach to better distinguish the pattern of dysregulation of immune profiles in individuals with BD.

Methods

We analyzed peripheral blood mononuclear cells (bipolar disorder N=39, control N=30), serum cytokines (bipolar disorder N=86, control N=58), whole blood RNA (bipolar disorder N=25, control N=25), and whole blood DNA (bipolar disorder N=104, control N=66) to identify immune-related differences in participants diagnosed with bipolar disorder compared to controls.

Results

Flow cytometry revealed a higher proportion of monocytes in participants with bipolar disorder together with a lower proportion of T helper cells. Additionally, the levels of 18 cytokines were significantly elevated, while two were reduced in participants with bipolar disorder. Most of the cytokines altered in individuals with bipolar disorder were proinflammatory. Forty-nine genes were differentially expressed in our bipolar disorder cohort and further analyses uncovered several immune-related pathways altered in these individuals. Genetic analysis indicated variants associated with inflammatory bowel disease also influences bipolar disorder risk.

Discussion

Our findings indicate a significant immune component to bipolar disorder pathophysiology and genetic overlap with inflammatory bowel disease. This comprehensive study supports existing literature, whilst also highlighting novel immune targets altered in individuals with bipolar disorder. Specifically, multiple lines of evidence indicate differences in the peripheral representation of monocytes and T cells are hallmarks of bipolar disorder.

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

BACKGROUND

Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes.

RESULTS

We comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia.

CONCLUSIONS

Our study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.

Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

Cross-Phenotype GWAS Supports Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis

Objective

An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype GWAS meta-analysis.

Methods

We performed cross-phenotype GWAS meta-analysis and colocalization analysis for SSc and PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, colocalization analyses with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies (PheWAS). Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci.

Results

We detected a strong genetic correlation between SSc and PBC (rg = 0.84, p = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 non-HLA loci that reached genome-wide significance (p < 5 × 10-8). Evidence of shared causal variants between SSc and PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function.

Conclusion

Our study supports a strong shared genetic susceptibility between SSc and PBC. Through cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Genetics of rheumatoid arthritis

In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.

Influence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia

BACKGROUND

Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH.

METHODS

We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease.

RESULTS

Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease was higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing genetic variant.

CONCLUSIONS

Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.

Casual relationships between circulating metabolites and rheumatoid arthritis: A mendelian randomization study

Background

Blood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support.

Method

In pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications.

Results

In our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis.

Conclusions

By employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition

Background

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.

Methods

We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.

Results

Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.

Conclusions

The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

Renshen Yangrong decoction for secondary malaise and fatigue: network pharmacology and Mendelian randomization study

Background

Renshen Yangrong decoction (RSYRD) has been shown therapeutic effects on secondary malaise and fatigue (SMF). However, to date, its bioactive ingredients and potential targets remain unclear.

Purpose

The purpose of this study is to assess the potential ingredients and targets of RSYRD on SMF through a comprehensive strategy integrating network pharmacology, Mendelian randomization as well as molecular docking verification.

Methods

Search for potential active ingredients and corresponding protein targets of RSYRD on TCMSP and BATMAN-TCM for network pharmacology analysis. Mendelian randomization (MR) was performed to find therapeutic targets for SMF. The eQTLGen Consortium (sample sizes: 31,684) provided data on cis-expression quantitative trait loci (cis-eQTL, exposure). The summary data on SMF (outcome) from genome-wide association studies (GWAS) were gathered from the MRC-IEU Consortium (sample sizes: 463,010). We built a target interaction network between the probable active ingredient targets of RSYRD and the therapeutic targets of SMF. We next used drug prediction and molecular docking to confirm the therapeutic value of the therapeutic targets.

Results

In RSYRD, network pharmacology investigations revealed 193 possible active compounds and 234 associated protein targets. The genetically predicted amounts of 176 proteins were related to SMF risk in the MR analysis. Thirty-seven overlapping targets for RSYRD in treating SMF, among which six (NOS3, GAA, IMPA1, P4HTM, RB1, and SLC16A1) were prioritized with the most convincing evidence. Finally, the 14 active ingredients of RSYRD were identified as potential drug molecules. The strong affinity between active components and putative protein targets was established by molecular docking.

Conclusion

This study revealed several active components and possible RSYRD protein targets for the therapy of SMF and provided novel insights into the feasibility of using Mendelian randomization for causal inference between Chinese medical formula and disease.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis

Objective

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy.

Methods

scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features.

Results

By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients.

Conclusion

Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Conditional frequency spectra as a tool for studying selection on complex traits in biobanks

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. To account for GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insight into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

Unveiling the Significance of HLA and KIR Diversity in Underrepresented Populations

Human leukocyte antigen (HLA) molecules and their relationships with natural killer (NK) cells, specifically through their interaction with killer-cell immunoglobulin-like receptors (KIRs), exhibit robust associations with the outcomes of diverse diseases. Moreover, genetic variations in HLA and KIR immune system genes offer limitless depths of complexity. In recent years, a surge of high-powered genome-wide association studies (GWASs) utilizing single nucleotide polymorphism (SNP) arrays has occurred, significantly advancing our understanding of disease pathogenesis. Additionally, advances in HLA reference panels have enabled higher resolution and more reliable imputation, allowing for finer-grained evaluation of the association between sequence variations and disease risk. However, it is essential to note that the majority of these GWASs have focused primarily on populations of Caucasian and Asian origins, neglecting underrepresented populations in Latin America and Africa. This omission not only leads to disparities in health care access but also restricts our knowledge of novel genetic variants involved in disease pathogenesis within these overlooked populations. Since the KIR and HLA haplotypes prevalent in each population are clearly modelled by the specific environment, the aim of this review is to encourage studies investigating HLA/KIR involvement in infection and autoimmune diseases, reproduction, and transplantation in underrepresented populations.

Genetic Polymorphisms in Exon 5 and Intron 5 and 7 of AIRE Are Associated with Rheumatoid Arthritis Risk in a Hungarian Population

BACKGROUND

Rheumatoid arthritis (RA) is chronically persistent synovitis and systemic inflammation. Although multiple contributors are detected, only one is pivotal in the neonatal period: the negative selection of autoimmune naïve T-cells by the autoimmune regulator (AIRE) transcriptional factor.

METHODS

Single-nucleotide polymorphisms (SNPs) in the DNA-binding site of AIRE may determine its function and expression. We intended to analyse site-specific allelic polymorphisms in two exon (rs878081 and rs1055311) and three intron (rs1003853, rs2075876, and rs1003854) loci with an RA risk. Our analytical case-control study analysed 270 RA patients and 322 control subjects in five different genetic models using quantitative real-time PCR (qPCR) with TaqMan® assays.

RESULTS

Statistically significant differences were found between the odds of allelic polymorphisms in the loci of rs878081, rs1003854, and rs1003853 among the controls and RA patients, and the disease activity seemed to be significantly associated with the genotypic subgroups of rs878081 and rs1055311. Our in silico analysis supported this, suggesting that allele-specific alterations in the binding affinity of transcriptional factor families might determine RA activity.

CONCLUSION

Our findings highlight the involvement of neonatal self-tolerance in RA pathogenesis, providing novel insights into disease development and paving the way for an analysis of further site-specific genetic polymorphisms in AIRE to expand the intervention time for RA.

Genetics of child aggression, a systematic review

Excessive and persistent aggressiveness is the most common behavioral problem that leads to psychiatric referrals among children. While half of the variance in childhood aggression is attributed to genetic factors, the biological mechanism and the interplay between genes and environment that results in aggression remains elusive. The purpose of this systematic review is to provide an overview of studies examining the genetics of childhood aggression irrespective of psychiatric diagnosis. PubMed, PsycINFO, and MEDLINE databases were searched using predefined search terms for aggression, genes and the specific age group. From the 652 initially yielded studies, eighty-seven studies were systematically extracted for full-text review and for further quality assessment analyses. Findings show that (i) investigation of candidate genes, especially of MAOA (17 studies), DRD4 (13 studies), and COMT (12 studies) continue to dominate the field, although studies using other research designs and methods including genome-wide association and epigenetic studies are increasing, (ii) the published articles tend to be moderate in sizes, with variable methods of assessing aggressive behavior and inconsistent categorizations of tandem repeat variants, resulting in inconclusive findings of genetic main effects, gene-gene, and gene-environment interactions, (iii) the majority of studies are conducted on European, male-only or male-female mixed, participants. To our knowledge, this is the first study to systematically review the effects of genes on youth aggression. To understand the genetic underpinnings of childhood aggression, more research is required with larger, more diverse sample sets, consistent and reliable assessments and standardized definition of the aggression phenotypes. The search for the biological mechanisms underlying child aggression will also benefit from more varied research methods, including epigenetic studies, transcriptomic studies, gene system and genome-wide studies, longitudinal studies that track changes in risk/ameliorating factors and aggression-related outcomes, and studies examining causal mechanisms.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

Assessment of bidirectional relationships between autoimmune diseases and primary ovarian insufficiency: insights from a bidirectional two-sample Mendelian randomization analysis

PURPOSE

The simultaneous occurrence of primary ovarian insufficiency (POI) and autoimmune diseases has been noted and debated in some epidemiological research. This bidirectional two-sample Mendelian randomization (MR) study aimed to investigate the causal relationships between autoimmune diseases and POI.

METHODS

We obtained summary-level data for ten autoimmune diseases and POI from published large-scale genome-wide association studies and the FinnGen consortium of European ancestry. A series of filtering steps was performed to discern independent genetic variants. Causal estimates were mainly calculated by the inverse variance weighting method and verified through multiple sensitivity analyses.

RESULTS

Of the ten autoimmune diseases, genetically predicted Addison's disease (odds ratio [OR] = 1.26, 95% confidence interval [CI]: 1.09-1.47, P = 0.003) and systemic lupus erythematosus (OR = 1.12, 95% CI 1.02-1.24, P = 0.021) were associated with an increased risk of POI, and sensitivity analyses confirmed the robustness of the results. In addition, there were weak associations between liability to POI and elevated risks of type 1 diabetes (OR = 1.05, 95% CI 1.00-1.10, P = 0.046) and autoimmune thyroid disease (OR = 1.03, 95% CI 1.01-1.05, P = 0.015).

CONCLUSION

This study revealed that Addison's disease and systemic lupus erythematosus are potential risk factors for POI, underscoring the necessity to consider the impact of autoimmune factors in the diagnosis and treatment of POI.

Polymorphic Analysis of Genes PADI4 (rs2240340, rs1748033) and HLA-DRB1 (rs2395175) in Arthritis Patients in Pakistani Population

Genes are an important factor for the initiation of any disease. Many genes are associated with rheumatoid arthritis (RA) other than environmental factors. The main objective of the study was to evaluate the association of genes PADI4 (peptidylarginine deiminases 14) (rs2240340, rs1748033) and Human leukocyte antigen class II histocompatibility, D-related beta chain (HLA-DRB1) (rs2395175) polymorphisms in RA patients from Punjab, Pakistan. Blood samples of RA patients were collected from different hospitals of Sargodha. DNA was extracted, followed by PCR. Polymorphic analysis was performed in 300 rheumatoid arthritis patients and 300 healthy controls on PADI4 (rs2240340, rs1748033) and HLA-DRB1 (rs2395175). In PADI4 gene, both homozygous mutant genotype (TT) and heterozygous (CT) of SNP rs2240340 showed significant association by increasing the risk of RA up to two fold (OR 2.55; 95% CI 1.57-4.15; p = 0.0002). In case of rs1748033 polymorphism, homozygous mutant genotype (TT) showed significant association with RA by increasing the risk of disease up to three fold (OR 3.46; 95% CI 1.97-6.07; p = 0.0001), while heterozygous genotype (CT) of the same SNP showed significant association with RA by playing a protective role (OR 0.57; 95% CI 0.36-0.91; p = 0.0197). In HLA-DRB1 gene, homozygous mutant genotype (GG) of SNP rs2395175 showed no significant association with RA, while heterozygous genotype (AG) of the same SNP showed significant association with RA by playing a protective role (OR 0.44; 95% CI 0.27-0.71; p = 0.0009). Highly significance association of genes PADI4 (rs2240340, rs1748033) and HLA-DRB1 (rs2395175) polymorphisms with RA was observed in Pakistani population.

The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis

Synovial tissue inflammation is a hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. Here, we examine genome-wide open chromatin at single-cell resolution in 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identify 24 chromatin classes and predict their associated transcription factors, including a CD8 + GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating with an RA tissue transcriptional atlas, we propose that these chromatin classes represent 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrate the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance, as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

An isoform quantitative trait locus in SBNO2 links genetic susceptibility to Crohn's disease with defective antimicrobial activity

Despite major advances in linking single genetic variants to single causal genes, the significance of genetic variation on transcript-level regulation of expression, transcript-specific functions, and relevance to human disease has been poorly investigated. Strawberry notch homolog 2 (SBNO2) is a candidate gene in a susceptibility locus with different variants associated with Crohn's disease and bone mineral density. The SBNO2 locus is also differentially methylated in Crohn's disease but the functional mechanisms are unknown. Here we show that the isoforms of SBNO2 are differentially regulated by lipopolysaccharide and IL-10. We identify Crohn's disease associated isoform quantitative trait loci that negatively regulate the expression of the noncanonical isoform 2 corresponding with the methylation signals at the isoform 2 promoter in IBD and CD. The two isoforms of SBNO2 drive differential gene networks with isoform 2 dominantly impacting antimicrobial activity in macrophages. Our data highlight the role of isoform quantitative trait loci to understand disease susceptibility and resolve underlying mechanisms of disease.

Gut microbial metabolite targets HDAC3-FOXK1-interferon axis in fibroblast-like synoviocytes to ameliorate rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease. Early studies hold an opinion that gut microbiota is environmentally acquired and associated with RA susceptibility. However, accumulating evidence demonstrates that genetics also shape the gut microbiota. It is known that some strains of inbred laboratory mice are highly susceptible to collagen-induced arthritis (CIA), while the others are resistant to CIA. Here, we show that transplantation of fecal microbiota of CIA-resistant C57BL/6J mice to CIA-susceptible DBA/1J mice confer CIA resistance in DBA/1J mice. C57BL/6J mice and healthy human individuals have enriched B. fragilis than DBA/1J mice and RA patients. Transplantation of B. fragilis prevents CIA in DBA/1J mice. We identify that B. fragilis mainly produces propionate and C57BL/6J mice and healthy human individuals have higher level of propionate. Fibroblast-like synoviocytes (FLSs) in RA are activated to undergo tumor-like transformation. Propionate disrupts HDAC3-FOXK1 interaction to increase acetylation of FOXK1, resulting in reduced FOXK1 stability, blocked interferon signaling and deactivation of RA-FLSs. We treat CIA mice with propionate and show that propionate attenuates CIA. Moreover, a combination of propionate with anti-TNF etanercept synergistically relieves CIA. These results suggest that B. fragilis or propionate could be an alternative or complementary approach to the current therapies.

Genetically predicted major depression causally increases the risk of temporomandibular joint disorders

Objective

Observational studies have reported that mental disorders are comorbid with temporomandibular joint disorder (TMD). However, the causal relationship remains uncertain. To clarify the causal relationship between three common mental illnesses and TMD, we conduct this Mendelian Randomization (MR) study.

Methods

The large-scale genome-wide association studies data of major depression, bipolar disorder and schizophrenia were retrieved from the Psychiatric Genomics Consortium. The summary data of TMD was obtained from the Finn-Gen consortium, including 211,023 subjects of European descent (5,668 cases and 205,355 controls). The main approach utilized was inverse variance weighting (IVW) to evaluate the causal association between the three mental disorders and TMD. Five sensitivity analyses including MR-Egger, Maximum Likelihood, Weighted median, MR. RAPS and MR-PRESSO were used as supplements. We conducted heterogeneity tests and pleiotropic tests to ensure the robustness.

Results

As shown by the IVW method, genetically determined major depression was associated with a 1.65-fold risk of TMD (95% CI = 1.10-2.47, p < 0.05). The direction and effect size remained consistent with sensitivity analyses. The odds ratios (ORs) were 1.51 (95% CI = 0.24-9.41, p > 0.05) for MR-Egger, 1.60 (95% CI = 0.98-2.61, p > 0.05) for Weighted median, 1.68 (95% CI = 1.19-2.38, p < 0.05) for Maximum likelihood, 1.56 (95% CI = 1.05-2.33, p < 0.05) for MR. RAPS, and 1.65 (95% CI = 1.10-2.47, p < 0.05) for MR-PRESSO, respectively. No pleiotropy was observed (both P for MR-Egger intercept and Global test >0.05). In addition, the IVW method identified no significant correlation between bipolar disorder, schizophrenia and TMD.

Conclusion

Genetic evidence supports a causal relationship between major depression and TMD, instead of bipolar disorder and schizophrenia. These findings emphasize the importance of assessing a patient's depressive status in clinical settings.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

Efficient and accurate mixed model association tool for single-cell eQTL analysis

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Shared genetic architecture between autoimmune disorders and B-cell acute lymphoblastic leukemia: insights from large-scale genome-wide cross-trait analysis

BACKGROUND

To study the shared genetic structure between autoimmune diseases and B-cell acute lymphoblastic leukemia (B-ALL) and identify the shared risk loci and genes and genetic mechanisms involved.

METHODS

Based on large-scale genome-wide association study (GWAS) summary-level data sets, we observed genetic overlaps between autoimmune diseases and B-ALL, and cross-trait pleiotropic analysis was performed to detect shared pleiotropic loci and genes. A series of functional annotation and tissue-specific analysis were performed to determine the influence of pleiotropic genes. The heritability enrichment analysis was used to detect crucial immune cells and tissues. Finally, bidirectional Mendelian randomization (MR) methods were utilized to investigate the casual associations.

RESULTS

Our research highlighted shared genetic mechanisms between seven autoimmune disorders and B-ALL. A total of 73 pleiotropic loci were identified at the genome-wide significance level (P < 5 × 10-8), 16 of which had strong evidence of colocalization. We demonstrated that several loci have been previously reported (e.g., 17q21) and discovered some novel loci (e.g., 10p12, 5p13). Further gene-level identified 194 unique pleiotropic genes, for example IKZF1, GATA3, IKZF3, GSDMB, and ORMDL3. Pathway analysis determined the key role of cellular response to cytokine stimulus, B cell activation, and JAK-STAT signaling pathways. SNP-level and gene-level tissue enrichment suggested that crucial role pleiotropic mechanisms involved in the spleen, whole blood, and EBV-transformed lymphocytes. Also, hyprcoloc and stratified LD score regression analyses revealed that B cells at different developmental stages may be involved in mechanisms shared between two different diseases. Finally, two-sample MR analysis determined causal effects of asthma and rheumatoid arthritis on B-ALL.

CONCLUSIONS

Our research proved shared genetic architecture between autoimmune disorders and B-ALL and shed light on the potential mechanism that might involve in.