Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis

Systemic lupus erythematosus genetics: insights into pathogenesis and implications for therapy

Systemic lupus erythematosus (SLE) is a prime example of how the interplay between genetic and environmental factors can trigger systemic autoimmunity, particularly in young women. Although clinical disease can take years to manifest, risk is established by the unique genetic makeup of an individual. Genome-wide association studies have identified almost 200 SLE-associated risk loci, yet unravelling the functional effect of these loci remains a challenge. New analytic tools have enabled researchers to delve deeper, leveraging DNA sequencing and cell-specific and immune pathway analysis to elucidate the immunopathogenic mechanisms. Both common genetic variants and rare non-synonymous mutations can interact to increase SLE risk. Notably, variants strongly associated with SLE are often located in genome super-enhancers that regulate MHC class II gene expression. Additionally, the 3D conformations of DNA and RNA contribute to genome regulation and innate immune system activation. Improved therapies for SLE are urgently needed and current and future knowledge from genetic and genomic research should provide new tools to facilitate patient diagnosis, enhance the identification of therapeutic targets and optimize testing of agents.

A review of the advances in understanding the genetic basis of spondylarthritis and emerging clinical benefit

Spondyloarthropathies (SpA), including ankylosing spondylitis (AS) and psoriatic arthritis (PsA), have been shown to have a substantial genetic predisposition based on heritability estimates derived from family studies and genome-wide association studies (GWAS). GWAS have uncovered numerous genetic loci associated with susceptibility to SpA, with significant associations to human leukocyte antigen (HLA) genes, which are major genetic risk factors for both AS and PsA. Specific loci differentiating PsA from cutaneous-only psoriasis have been identified, though these remain limited. Further research with larger sample sizes is necessary to identify more PsA-specific genetic markers. Current research focuses on translating these genetic insights into clinical applications. For example, polygenic risk scores are showing promise for the classification of disease risk and diagnosis and future research should focus on refining these risk assessment tools to improve clinical outcomes for individuals with SpA. Addressing these challenges will help integrate genetic testing into patients care and impact clinical practice.

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.

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.

Using Polygenic Risk Scores to Aid Diagnosis of Patients With Early Inflammatory Arthritis: Results From the Norfolk Arthritis Register

OBJECTIVE

There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting. Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis.

METHODS

Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0%-100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or "other diseases." Performance of the G-probabilities compared with clinician diagnosis was assessed.

RESULTS

We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84-0.86). G-probabilities <5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest >2 unlikely diseases for 94% of patients and >3 for 53.7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis.

CONCLUSION

G-PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Distinguishing different psychiatric disorders using DDx-PRS

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

Genetic Studies Investigating Susceptibility to Psoriatic Arthritis: A Narrative Review

PURPOSE

Approximately 30% of patients with psoriasis will develop psoriatic arthritis (PsA), leading to a decreased quality of life for the patient caused by increasing disability and additional health complications. The identification of risk factors for the development of PsA would facilitate the development of risk prediction models in which patients with psoriasis at high risk of developing PsA could be targeted in a stratified medicine approach, enabling early intervention and treatment. PsA is known to have a genetic contribution to susceptibility, and the identification of genetic risk factors that differentiate PsA from cutaneous-only psoriasis is a key area of research. This narrative review summarizes the discovery of genetic risk factors and, with the aid of a primer on risk prediction models, discusses their potential role for the classification of PsA risk and diagnosis.

METHODS

All relevant research articles were identified through searches of the PubMed database for literature published up until December 2022. Search terms included psoriatic arthritis, genetic susceptibility, genetic association, genome-wide association study, GWAS, prediction, and polygenic risk score.

FINDINGS

The current literature reveals considerable overlap between the genetic susceptibility loci for PsA and psoriasis. Several PsA-specific genetic risk factors have been reported, and most notably these implicate the HLA-B and IL23R genes. Efforts to include genetic risk factors in prediction models for the development of PsA have reported good discrimination.

IMPLICATIONS

Key messages emerging from this narrative are as follows: the limited number of PsA-specific susceptibility loci reported to date suggest larger studies are required, facilitated by international collaboration, to achieve the power to detect further genetic factors; the early promising results for genetic-based risk prediction require further validation in independent datasets; and risk prediction models combining clinical and genetic risk factors have yet to be explored.

A review of genetic risk in systemic lupus erythematosus

INTRODUCTION

Systemic Lupus Erythematosus (SLE) is a complex multisystem autoimmune disease with a wide range of signs and symptoms in affected individuals. The utilization of genome-wide association study (GWAS) technology has led to an explosion in the number of genetic risk factors mapped for autoimmune diseases, including SLE.

AREAS COVERED

In this review, we summarize the more recent genetic risk loci mapped in SLE, which bring the total number of loci mapped to approximately 200. We review prioritization analyses of the associated variants and experimental validation of the putative causal variants. This includes the implementation of new bioinformatic techniques to align genomic and functional data and the use of transcriptomics with single-cell RNA-sequencing, CRISPR genome editing, and Massive Parallel Reporter Assays to analyze non-coding regulatory genetics.

EXPERT OPINION

Despite progress in identifying more genetic risk loci and variant-gene pairs for SLE, understanding its pathogenesis and applying findings clinically remains challenging. The polygenic risk score (PRS) has been used as an application of SLE genetics, but with limited performance in non-EUR populations. In the next few years, advancements in proteomics, post-translational modification estimation, and whole-genome sequencing will enhance disease understanding.

The Role of Genetics in Clinically Suspect Arthralgia and Rheumatoid Arthritis Development: A Large Cross-Sectional Study

OBJECTIVE

To investigate whether established genetic predictors for rheumatoid arthritis (RA) differentiate healthy controls, patients with clinically suspect arthralgia (CSA), and RA patients.

METHODS

Using analyses of variance, chi-square tests, and mean risk difference analyses, we investigated the association of an RA polygenic risk score (PRS) and HLA shared epitope (HLA-SE) with all participant groups, both unstratified and stratified for anti-citrullinated protein antibody (ACPA) status. We used 3 separate data sets sampled from the same Dutch population (1,015 healthy controls, 479 CSA patients, and 1,146 early classified RA patients). CSA patients were assessed for conversion to inflammatory arthritis over a period of 2 years, after which they were classified as either CSA converters (n = 84) or CSA nonconverters (n = 395).

RESULTS

The PRS was increased in RA patients (mean ± SD PRS 1.31 ± 0.96) compared to the complete CSA group (1.07 ± 0.94) and compared to CSA converters (1.12 ± 0.94). In ACPA- strata, PRS distributions differed strongly when comparing the complete CSA group (mean ± SD PRS 1.05 ± 0.94) and CSA converters (0.97 ± 0.87) to RA patients (1.20 ± 0.94), while in the ACPA+ strata, the complete CSA group (1.25 ± 0.99) differed clearly from healthy controls (1.05 ± 0.94) and RA patients (1.41 ± 0.96). HLA-SE was more prevalent in the RA group (prevalence 0.64) than the complete CSA group (0.45), with small differences between RA patients and CSA converters (0.64 versus 0.60) and larger differences between CSA converters and CSA nonconverters (0.60 versus 0.42). HLA-SE prevalence differed more strongly within the ACPA+ strata as follows: healthy controls (prevalence 0.43), CSA nonconverters (0.48), complete CSA group (0.59), CSA converters (0.66), and RA patients (0.79).

CONCLUSION

We observed that genetic predisposition increased across pre-RA participant groups. The RA PRS differed in early classified RA and inflammatory pre-disease stages, regardless of ACPA stratification. HLA-SE prevalence differed between arthritis patients, particularly ACPA+ patients, and healthy controls. Genetics seem to fulfill different etiologic roles.

Risk prediction models for incident systemic lupus erythematosus among women in the Nurses' health study cohorts using genetics, family history, and lifestyle and environmental factors

OBJECTIVE

Systemic lupus erythematosus (SLE) is a severe multisystem autoimmune disease that predominantly affects women. Its etiology is complex and multifactorial, with several known genetic and environmental risk factors, but accurate risk prediction models are still lacking. We developed SLE risk prediction models, incorporating known genetic, lifestyle and environmental risk factors, and family history.

METHODS

We performed a nested case-control study within the Nurses' Health Study cohorts (NHS). NHS began in 1976 and enrolled 121,700 registered female nurses ages 30-55 from 11 U.S. states; NHSII began in 1989 and enrolled 116,430 registered female nurses ages 25-42 from 14 U.S. states. Participants were asked about lifestyle, reproductive and environmental exposures, as well as medical information, on biennial questionnaires. Incident SLE cases were self-reported and validated by medical record review (Updated 1997 American College of Rheumatology classification criteria). Those with banked blood samples for genotyping (∼25% of each cohort), were selected and matched by age (± 4 years) and race/ethnicity to women who had donated a blood sample but did not develop SLE. Lifestyle and reproductive variables, including smoking, alcohol use, body mass index, sleep, socioeconomic status, U.S. region, menarche age, oral contraceptive use, menopausal status/postmenopausal hormone use, and family history of SLE or rheumatoid arthritis (RA) were assessed through the questionnaire prior to SLE diagnosis questionnaire cycle (or matched index date). Genome-wide genotyping results were used to calculate a SLE weighted genetic risk score (wGRS) using 86 published single nucleotide polymorphisms (SNPs) and 10 classical HLA alleles associated with SLE. We compared four sequential multivariable logistic regression models of SLE risk prediction, each calculating the area under the receiver operating characteristic curve (AUC): 1) SLE wGRS, 2) SLE/RA family history, 3) lifestyle, environmental and reproductive factors and 4) combining model 1-3 factors. Models were internally validated using a bootstrapped estimate of optimism of the AUC. We also examined similar sequential models to predict anti-dsDNA positive SLE risk.

RESULTS

We identified and matched 138 women who developed incident SLE to 1136 women who did not. Models 1-4 yielded AUCs 0.63 (95%CI 0.58-0.68), 0.64 (95%CI 0.59-0.68), 0.71(95% CI 0.66-0.75), and 0.76 (95% CI 0.72-0.81). Model 4 based on genetics, family history and eight lifestyle and environmental factors had best discrimination, with an optimism-corrected AUC 0.75. AUCs for similar models predicting anti-dsDNA positive SLE risk, were 0.60, 0.63, 0.81 and 0.82, with optimism corrected AUC of 0.79 for model 4.

CONCLUSION

A final model including SLE weighted genetic risk score, family history and eight lifestyle and environmental SLE risk factors accurately classified future SLE risk with optimism corrected AUC of 0.75. To our knowledge, this is the first SLE prediction model based on known risk factors. It might be feasibly employed in at-risk populations as genetic data are increasingly available and the risk factors easily assessed. The NHS cohorts include few non-White women and mean age at incident SLE was early 50s, calling for further research in younger and more diverse cohorts.

Age/autoimmunity-associated B cells in inflammatory arthritis: An emerging therapeutic target

Age/autoimmunity-associated B cells (ABCs) are a novel B cell subpopulation with a unique transcriptional signature and cell surface phenotype. They are not sensitive to BCR but rely on TLR7 or TLR9 in the context of T cell-derived cytokines for the differentiation. It has been established that aberrant expansion of ABCs is linked to the pathogenesis of systemic autoimmune diseases such as systemic lupus erythematosus. Recently, we and other groups have shown that increased ABCs is associated with rheumatoid arthritis (RA) disease activity and have demonstrated their pathogenic role in RA, indicating that targeting specific B cell subsets is a promising strategy for the treatment of inflammatory arthritis. In this review, we summarize the current knowledge of ABCs, focusing on their emerging role in the pathogenesis of inflammatory arthritis. A deep understanding of the biology of ABCs in the context of inflammatory settings in vivo will ultimately contribute to the development of novel targeted therapies for the treatment of inflammatory arthritis.

Associations of the MUC5B promoter variant with timing of interstitial lung disease and rheumatoid arthritis onset

OBJECTIVES

To investigate the associations of the common MUC5B promoter variant with timing of RA-associated interstitial lung disease (RA-ILD) and RA onset.

METHODS

We identified patients with RA meeting 2010 ACR/EULAR criteria and available genotype information in the Mass General Brigham Biobank, a multihospital biospecimen and clinical data collection research study. We determined RA-ILD presence by reviewing all RA patients who had CT imaging, lung biopsy or autopsy results. We determined the dates of RA and RA-ILD diagnoses by manual records review. We examined the associations of the MUC5B promoter variant (G>T at rs35705950) with RA-ILD, RA-ILD occurring before or within 2 years of RA diagnosis and RA diagnosis at age >55 years. We used multivariable logistic regression to estimate odds ratios (ORs) for each outcome by MUC5B promoter variant status, adjusting for potential confounders including genetic ancestry and smoking.

RESULTS

We identified 1005 RA patients with available genotype data for rs35705950 (mean age 45 years, 79% female, 81% European ancestry). The MUC5B promoter variant was present in 155 (15.4%) and was associated with RA-ILD [multivariable OR 3.34 (95% CI 1.97, 5.60)], RA-ILD before or within 2 years of RA diagnosis [OR 4.01 (95% CI 1.78, 8.80)] and RA onset after age 55 years [OR 1.52 (95% CI 1.08, 2.12)].

CONCLUSIONS

The common MUC5B promoter variant was associated with RA-ILD onset earlier in the RA disease course and older age of RA onset. These findings suggest that the MUC5B promoter variant may impact RA-ILD risk early in the RA disease course, particularly in patients with older-onset RA.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

The Association of Angiopoietin-2 1064 C/T Rs3020221 Gene Polymorphism with Knee Osteoarthritis

Osteoarthritis (OA) is a common type of arthritis, affecting millions of people around the world. Angiopoietin-2 (Angpt-2) has a role in the development of chronic inflammatory diseases. We aimed to assess the serum Angpt-2 levels in knee OA patients and to investigate the association of Angpt-2 gene polymorphism(rs3020221 C/T) with knee OA susceptibility and severity. Angiopoietin-2(rs3020221C/T) gene polymorphism was identified in 254 knee OA patients and 227 healthy controls using real-time polymerase chain reaction. Serum Angpt-2 was measured using ELISA. The Arabic version of the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) Index and Kellgren-Lawrence (KL) grading score were used to assess the clinical and radiological severity of OA and their relationship with Angpt-2(rs3020221C/T) gene polymorphism was investigated. Serum Angpt-2 levels were significantly higher in knee OA patients than in the controls (P = .001). OA patients with C/T genotype had a four times greater risk of developing OA than other genotypes (OR = 4.39, 95% CI = 2.85-6.76). Additionally, the T allele presented more in OA patients 224/508 (44%) with two times risk of developing OA (OR = 1.86, 95% CI = 1.43-2.43, p = .001). Angpt-2 SNP (rs3020221C/T) genotype C/T was significantly associated with elevated serum Angpt-2 levels (14.15 ± 5.62 ng/ml). The serum Angpt-2 levels are significantly elevated in OA patients and Angpt-2 gene polymorphism (rs3020221 C/T) may be a risk factor for OA development and both are associated with the severity of knee OA. Carriers of the C/T genotype have a significantly higher serum Angpt-2 levels and a greater risk of developing OA.

Systemic lupus erythematosus as a genetic disease

Systemic lupus erythematosus is the prototypical systemic autoimmune disease, as it is characterized both by protean multi-organ system manifestations and by the uniform presence of pathogenic autoantibodies directed against components of the nucleus. Prior to the modern genetic era, the diverse clinical manifestations of SLE suggested to many that SLE patients were unlikely to share a common genetic risk basis. However, modern genetic studies have revealed that SLE usually arises when an environmental exposure occurs in an individual with a collection of genetic risk variants passing a liability threshold. Here, we summarize the current state of the field aimed at: (1) understanding the genetic architecture of this complex disease, (2) synthesizing how this genetic risk architecture impacts cellular and molecular disease pathophysiology, (3) providing illustrative examples that highlight the rich complexity of the pathobiology of this prototypical autoimmune disease and (4) communicating this complex etiopathogenesis to patients.

Advances of Genomic Medicine in Psoriatic Arthritis

Psoriatic arthritis (PsA) is a common type of inflammatory arthritis found in up to 40% of patients with psoriasis. Although early diagnosis is important for reducing the risk of irreversible structural damage, there are no adequate screening tools for this purpose, and there are no clear markers of predisposition to the disease. Much evidence indicates that PsA disorder is complex and heterogeneous, where genetic and environmental factors converge to trigger inflammatory events and the development of the disease. Nevertheless, the etiologic events that underlie PsA are complex and not completely understood. In this review, we describe the existing data in PsA in order to highlight the need for further research in this disease to progress in the knowledge of its pathobiology and to obtain early diagnosis tools for these patients.

Multi-omics approach to precision medicine for immune-mediated diseases

Recent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.

Advancing drug discovery using the power of the human genome

Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating the discovery of therapies, present examples of how human genetics evidence supports successful drug targets, and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct-to-consumer platforms in the era of fast-paced big data biotechnology, and how diverse genetic and health data can benefit society. © 2021 23andMe, Inc. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

The vastly diverse nature of systemic lupus erythematosus (SLE) poses great challenges to clinicians and patients, as well as to research and drug development efforts. Precise management of lupus patients would be advanced by the ability to identify specific abnormalities operative in individual patients at the time of encounter with the clinician. Advances in new technologies and bioinformatics have greatly improved the understanding of the pathophysiology of SLE. Recent research has focused on the discovery and classification of sensitive and specific markers that could aid early accurate diagnosis, better monitoring of disease and identification of appropriate therapy choices based on specific dysregulated molecular pathways. Here, we summarize some of the advances and discuss the challenges in moving toward precise patient-centric management modalities in SLE.

An insight into osteoarthritis susceptibility: Integration of immunological and genetic background

Osteoarthritis (OA) is a progressive degenerative disease that affects all synovial joints, causing the disability of the main locomotor diarthrodial joints. OA pathogenesis is caused by a complex interplay between a number of genetic and environmental risk factors, involved in the early onset and progression of this chronic inflammatory joint disease. Uncovering the underlying immunological and genetic mechanisms will enable an insight into OA pathophysiology and lead to novel and integrative approaches in the treatment of OA patients, together with a reduction of the disease risk, or a delay of its onset in susceptible patients.

Etiologies of Rheumatoid Arthritis: Update on Mucosal, Genetic, and Cellular Pathogenesis

Purpose of Review

Over the last few years, the scientific community has made significant progress in understanding the etiology of rheumatoid arthritis (RA). In this review, we summarize those key findings and trends.

Recent Findings

New data strongly implicates respiratory exposures, obesity, diet and microbiome, genetics, and their interactions in the etiology of RA. Furthermore, anti-posttranslationally modified protein antibodies (AMPAs) and abnormal glycosylation may be additional biomarkers for RA. Finally, functional genomics techniques implicate loss of certain macrophage populations and proliferation of synovial fibroblasts in RA.

Summary

These findings support the notion that RA originates at mucosal sites, augmented by genetic predisposition, and mediated by certain cell types including macrophages and fibroblasts. Weight loss, physical activity, and diet are additional modifiable factors beyond smoking cessation that can reduce risk of RA. Future epidemiologic and translational studies leveraging multi-omics approaches will help map the precise sequence of events in RA pathogenesis.

Serum Urate Polygenic Risk Score Can Improve Gout Risk Prediction: A Large-Scale Cohort Study

Gout is a painful inflammatory arthritis affecting more than 8 million Americans. Identifying high-risk patients in early life could potentially encourage people to adopt lifestyle changes to prevent gout. Polygenic risk score (PRS) provides an overall estimate of an individual's genetic liability to develop a disease and can be used for early identification of high-risk individuals. In this study, we validated a previously reported PRS in an independent cohort. The urate-PRS was constructed from 110 significant urate-associated variants identified in Europeans. Phenome-wide and PRS-wide association study showed the urate-PRS is highly specifically associated with gout (phecode: 274.10; beta = 1.495 [1.372, 1.619], p = 4.37e-124). Urate-PRS alone did not performed in the gout prediction (area under the receiver operating characteristic curve, AUROC = 0.640); however, the addition of PRS upon demographics significantly improved the model performance, yielding an AUROC of 0.804 from 0.777 (DeLong test p = 3.66e−9). Trans-ethnic PRS and European-specific PRS showed similar prediction performance. We observed increasing gout prevalence and odds ratio (OR) across the PRS quintiles. Our study showed 8.2% of the cohort had more than 2.5 odds for gout than remainders, indicating that urate-PRS may be a better marker than age and sex to stratify patient risk. With the rapid growth of large biorepositories, such as All of Us, urate-PRS can be applied quickly and widely in population to estimate individual's risk, providing a powerful tool for gout preventive purpose in population health.

Approaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics

Immune-mediated diseases (IMDs) are complex pathologies that are strongly influenced by environmental and genetic factors. Associations between genetic loci and susceptibility to these diseases have been widely studied, and hundreds of risk variants have emerged during the last two decades, with researchers observing a shared genetic pattern among them. Nevertheless, the pathological mechanism behind these associations remains a challenge that has just started to be understood thanks to functional genomic approaches. Transcriptomics, regulatory elements, chromatin interactome, as well as the experimental characterization of genomic findings, constitute key elements in the emerging understandings of how genetics affects the etiopathogenesis of IMDs. In this review, we will focus on the latest advances in the field of functional genomics, centering our attention on systemic rheumatic IMDs.

Latin American Genes: The Great Forgotten in Rheumatoid Arthritis

The successful implementation of personalized medicine will rely on the integration of information obtained at the level of populations with the specific biological, genetic, and clinical characteristics of an individual. However, because genome-wide association studies tend to focus on populations of European descent, there is a wide gap to bridge between Caucasian and non-Caucasian populations before personalized medicine can be fully implemented, and rheumatoid arthritis (RA) is not an exception. In this review, we discuss advances in our understanding of genetic determinants of RA risk among global populations, with a focus on the Latin American population. Geographically restricted genetic diversity may have important implications for health and disease that will remain unknown until genetic association studies have been extended to include Latin American and other currently under-represented ancestries. The next few years will witness many breakthroughs in personalized medicine, including applications for common diseases and risk stratification instruments for targeted prevention/intervention strategies. Not all of these applications may be extrapolated from the Caucasian experience to Latin American or other under-represented populations.

The Genetics of Spondyloarthritis

The term spondyloarthritis (SpA) encompasses a group of chronic inflammatory diseases with common features in terms of clinical presentation and genetic predisposition. SpA is characterized by inflammation of the spine and peripheral joints, and is also be associated with extra-articular inflammatory manifestations such as psoriasis, uveitis, or inflammatory bowel disease (IBD). The etiology of SpA is not completely understood, but it is known to have a strong genetic component dominated by the human leukocyte antigen (HLA)-B27. In the last few years, our understanding of genetic susceptibility to SpA, particularly ankylosing spondylitis (AS), has greatly improved thanks to the findings derived from powered genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) arrays. These studies have identified many candidate genes, therefore providing new potential directions in the exploration of disease mechanisms, especially with regard to the key role of the immune system in the pathogenesis of SpA. SpA is a complex disease where genetic variability, environmental factors, and random events interact to trigger pathological pathways. The aim of this review is to summarize current findings on the genetics of SpA, some of which might help to study new treatment approaches.

Quarterly picks from the editors

The Science Translational Medicine editors highlight interesting translational ties across select articles published recently in the Science family of journals.