Genetic risk score predicting risk of rheumatoid arthritis phenotypes and age of symptom onset

Clinical prediction models of rheumatoid arthritis and its complications: focus on cardiovascular disease and interstitial lung disease

Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease of unknown etiology with erosive, symmetric polyarthritis as the main clinical manifestations. Its basic pathological changes are the formation of synovitis, and patients gradually develop destruction of articular cartilage destruction and bone erosion, which eventually leads to joint deformity, disability, and various extra-articular manifestations. Clinical prediction models (CPMs), also known as risk prediction models or risk scores, are mathematical formulas used to estimate the probability that a given individual will have a disease or an outcome in the future. The models are mainly divided into two categories: diagnostic models and prognostic models, which can be used to provide information on disease diagnosis or prognosis to help make better medical decisions. Currently, there is no cure for RA, but effective early diagnosis and treatment are crucial for limiting the severity of the disease and preventing the occurrence and development of complications. This paper reviews the CPMs associated with RA and its related complications, including cardiovascular disease (CVD) and interstitial lung disease (ILD), in order to provide reference and evidence for the early diagnosis and treatment of these diseases and personalized medicine for patients. In addition, the possible pathogenesis and risk factors of these comorbidities are summarized, and possible directions for future related research are prospected.

Added value of multiple autoantibody testing for predicting progression to inflammatory arthritis in at-risk individuals

BACKGROUND

Predicting progression to clinical arthritis in individuals at-risk of developing rheumatoid arthritis is a prerequisite to developing stratification groups for prevention strategies. Selecting accurate predictive criteria is the critical step to define the population at-risk. While positivity for anti-citrullinated protein antibodies (ACPA) remains the main recruitment biomarker, positivity for other autoantibodies (AutoAbs) identified before the onset of symptoms, may provide additional predictive accuracy for stratification.

OBJECTIVE

To perform a multiple AutoAbs analysis for both the prediction and the time of progression to inflammatory arthritis (IA).

METHODS

392 individuals were recruited based on a new musculoskeletal complaint and positivity for ACPA or rheumatoid factor (RF). ELISAs were performed for ACPA, RF, anti-nuclear Ab, anti-carbamylated protein (anti-CarP) and anti-collagen AutoAbs. Logistic and COX regression were used for analysis.

RESULTS

Progression to IA was observed in 125/392 (32%) of cases, of which 78 progressed within 12 months. The AutoAbs ACPA, RF, anti-CarP were individually associated with progression (p<0.0001) and improved prediction when combined with demographic/clinical data (Accuracy >77%; area under the curve (AUC) >0.789), compared with prediction using only demographic/clinical data (72.9%, AUC=0.760). Multiple AutoAbs testing provided added value, with +6.4% accuracy for number of positive AutoAbs (AUC=0.852); +5.4% accuracy for AutoAbs levels (ACPA/anti-CarP, AUC=0.832); and +6.2% accuracy for risk-groups based on high/low levels (ACPA/RF/anti-CarP, AUC=0.837). Time to imminent progression was best predicted using ACPA/anti-CarP levels (AUC=0.779), while the number of positive AutoAbs was/status/risk were as good (AUC=0.778).

CONCLUSION

We confirm added value of multiple AutoAbs testing for identifying progressors to clinical disease, allowing more specific stratification for intervention studies.

Precision medicine: the precision gap in rheumatic disease

For many oncological conditions, the application of timely and patient-tailored targeted therapies, or precision medicine, is a major therapeutic development that has provided considerable clinical benefit. However, despite the application of increasingly sophisticated technologies, alongside advanced bioinformatic and machine-learning algorithms, this success is yet to be replicated for the rheumatic diseases. In rheumatoid arthritis, for example, despite an array of targeted biologic and conventional therapeutics, treatment choice remains largely based on trial and error. The concept of the 'precision gap' for rheumatic disease can help us to identify factors that underpin the slow progress towards the discovery and adoption of precision-medicine approaches for rheumatic disease. In a rheumatic disease such as rheumatoid arthritis, it is possible to identify four themes that have slowed progress, solutions to which should help to close the precision gap. These themes relate to our fundamental understanding of disease pathogenesis, how we determine treatment response, confounders of treatment outcomes and trial design.

No evidence that genetic predictors of susceptibility predict changes in core outcomes in JIA

OBJECTIVES

The clinical progression of JIA is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures.

METHODS

A total of 713 JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow-up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value < 5×10-08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (ILAR) category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups.

RESULTS

Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow-up or baseline.

CONCLUSION

For the first time we have used all known JIA genetic susceptibility loci (P=<5×10-08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.

Recent advances in elucidating the genetic basis of systemic sclerosis

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care.

RECENT FINDINGS

This review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease.

SUMMARY

Early diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.

A Summary on the Genetics of Systemic Lupus Erythematosus, Rheumatoid Arthritis, Systemic Sclerosis, and Sjögren's Syndrome

Systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, and Sjögren's syndrome are four major autoimmune rheumatic diseases characterized by the presence of autoantibodies, caused by a dysregulation of the immune system that leads to a wide variety of clinical manifestations. These conditions present complex etiologies strongly influenced by multiple environmental and genetic factors. The human leukocyte antigen (HLA) region was the first locus identified to be associated and still represents the strongest susceptibility factor for each of these conditions, particularly the HLA class II genes, including DQA1, DQB1, and DRB1, but class I genes have also been associated. Over the last two decades, the genetic component of these disorders has been extensively investigated and hundreds of non-HLA risk genetic variants have been uncovered. Furthermore, it is widely accepted that autoimmune rheumatic diseases share molecular disease pathways, such as the interferon (IFN) type I pathways, which are reflected in a common genetic background. Some examples of well-known pleiotropic loci for autoimmune rheumatic diseases are the HLA region, DNASEL13, TNIP1, and IRF5, among others. The identification of the causal molecular mechanisms behind the genetic associations is still a challenge. However, recent advances have been achieved through mouse models and functional studies of the loci. Here, we provide an updated overview of the genetic architecture underlying these four autoimmune rheumatic diseases, with a special focus on the HLA region.

Assessing improved risk prediction of rheumatoid arthritis by environmental, genetic, and metabolomic factors

OBJECTIVE

We sought to improve seropositive rheumatoid arthritis (RA) risk prediction using a novel weighted genetic risk score (wGRS) and preclinical plasma metabolites associated with RA risk. Predictive performance was compared to previously validated models including RA-associated environmental factors.

METHODS

This nested case-control study matched incident seropositive RA cases (meeting ACR 1987 or EULAR/ACR 2010 criteria) in the Nurses' Health Studies (NHS) to two controls on age, blood collection features, and post-menopausal hormone use at pre-RA blood draw. Environmental variables were measured at the questionnaire cycle preceding blood draw. Four models were generated and internally validated using a bootstrapped optimism estimate: (a) base with environmental factors (E), (b) environmental, genetic and gene-environment interaction factors (E + G + GEI), c) environmental and metabolic factors (E + M), and d) all factors (E + G + GEI + M). A fifth model including all factors and interaction terms was fit using ridge regression and cross-validation. Models were compared using area under the receiver operating characteristic curve (AUC).

RESULTS

150 pre-RA cases and 455 matched controls were included. The E model yielded an optimism-corrected AUC of 0.622. The E + M model did not show improvement over the E model (corrected AUC 0.620). Including genetic factors increased prediction, producing corrected AUCs of 0.677 in the E + G + GEI model and 0.674 in the E + G + GEI + M model. Similarly, the performance of the cross-validated ridge regression model yielded an AUC of 0.657.

CONCLUSION

Addition of wGRS and gene-environment interaction improved seropositive RA risk prediction models. Preclinical metabolite levels did not significantly contribute to prediction.

Can Studying Genetically Predisposed Individuals Inform Prevention Strategies for RA?

Rheumatoid arthritis (RA) is a prevalent autoimmune disorder in which complex genetic predisposition interacts with multiple environmental factors to precipitate chronic and progressive immune-mediated joint inflammation. Currently, in most affected individuals, ongoing suppression of the inflammation is required to prevent irreversible damage and functional loss. The delineation of a protracted preclinical period in which autoimmunity is initially established and then evolves to become pathogenic provides unprecedented opportunities for interventions that have the potential to prevent the onset of this lifelong disease. Clinical trials aimed at assessing the impact of specific prevention strategies require the identification of individuals who are at high risk of future RA development. Currently, these risk factors include a strong family history of RA, and the detection of circulating RA-associated autoantibodies, particularly anti-citrullinated protein antibodies (ACPA). Yet, even in such individuals, there remains considerable uncertainty about the likelihood and the timeframe for future disease development. Thus, individuals who are approached to participate in such clinical trials are left weighing the risks and benefits of the prevention measures, while having large gaps in our current understanding. To address this challenge, we have undertaken longitudinal studies of the family members of Indigenous North American RA patients, this population being known to have a high prevalence of RA, early age of onset, and familial clustering of cases. Our studies have indicated that the concepts of "risk" and "prevention" need to be communicated in a culturally relevant manner, and proposed prevention interventions need to have an appropriate balance of effectiveness, safety, convenience, and cultural acceptability. We have focused our proposed prevention studies on immunomodulatory/anti-inflammatory nutritional supplements that appear to strike such a complex balance.

A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that commonly manifests as destructive joint inflammation but also affects multiple other organ systems. The pathogenesis of RA is complex where a variety of factors including comorbidities, demographic, and socioeconomic variables are known to associate with RA and influence the progress of the disease. In this work, we used a Bayesian logistic regression model to quantitatively assess how these factors influence the risk of RA, individually and through their interactions. Using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), a set of 11 well-known RA risk factors such as age, gender, ethnicity, body mass index (BMI), and depression were selected to predict RA. We considered up to third-order interactions between the risk factors and implemented factor analysis of mixed data (FAMD) to account for both the continuous and categorical natures of these variables. The model was further optimized over the area under the receiver operating characteristic curve (AUC) using a genetic algorithm (GA) with the optimal predictive model having a smoothed AUC of 0.826 (95% CI: 0.801–0.850) on a validation dataset and 0.805 (95% CI: 0.781–0.829) on a holdout test dataset. Apart from corroborating the influence of individual risk factors on RA, our model identified a strong association of RA with multiple second- and third-order interactions, many of which involve age or BMI as one of the factors. This observation suggests a potential role of risk-factor interactions in RA disease mechanism. Furthermore, our findings on the contribution of RA risk factors and their interactions to disease prediction could be useful in developing strategies for early diagnosis of RA.

Rheumatoid Arthritis Pathogenesis, Prediction, and Prevention: An Emerging Paradigm Shift

Rheumatoid arthritis (RA) is currently diagnosed and treated when an individual presents with signs and symptoms of inflammatory arthritis (IA) as well as other features, such as autoantibodies and/or imaging findings, that provide sufficient confidence that the individual has RA-like IA (e.g., meeting established classification criteria) that warrants therapeutic intervention. However, it is now known that there is a stage of seropositive RA during which circulating biomarkers and other factors (e.g., joint symptoms) can be used to predict if and when an individual who does not currently have IA may develop future clinically apparent IA and classifiable RA. Indeed, the discovery of the "pre-RA" stage of seropositive disease has led to the development of several clinical trials in which individuals are studied to identify ways to delay or prevent the onset of clinically apparent IA/RA. This review focuses on several issues pertinent to understanding the prevention of RA. These include discussion of the pathogenesis of pre-RA development, prediction of the likelihood and timing of future classifiable RA, and a review of completed and ongoing clinical trials in RA prevention. Furthermore, this review discusses challenges and opportunities to be addressed to effect a paradigm shift in RA, where in the near future, proactive risk assessment focused on prevention of RA will become a public health strategy in much the same manner as cardiovascular disease is managed today.

Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis

It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician's initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% (P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.

[Family history of rheumatic diseases in patients with rheumatoid arthritis: a large scale cross-sectional study]

OBJECTIVE

To determine the associations between the family history of rheumatic diseases and clinical features in patients with rheumatoid arthritis (RA).

METHODS

In total, eight hundred and ninety patients with RA were enrolled. The demographic and clinical data were collected, including gender, age, height, body weight, age of disease onset, history of smoking and drinking, family history of rheumatic diseases, clinical and laboratory features, pain and global visual analogue scale (VAS), and multi-dimensional health assessment questionnaire (MDHAQ). Finally, 803 patients were completed the dataset and were included in the study.

RESULTS

In this cohort, the male/female ratio was 1:3.5, and the age of onset was (45.09±14.50) years. A total of 123 (15.32%) patients were accompanied with family history of rheumatic diseases, including RA, spondyloarthritis, Sjögren's syndrome, systemic lupus erythematosus and systemic sclerosis. The percentages of first degree, second degree and both first and second degree relatives were 91 (73.98%), 22 (17.89%), and 10 (8.13%) respectively. The most common disease was RA (70.73%), followed by other rheumatic diseases (21.95%), and RA combined with other rheumatic diseases (7.32%). The clinical and laboratory characteristics were compared between the patients with and without family history. The onset-age of the subjects was significantly different between those with and without family history of rheumatic diseases (39.97 ±13.68 vs. 46.01±14.46; P<0.01), which meant that the onset-age in patients with family history was 6.04 years earlier than that in patients without family history. The patients with family history had higher positive rate of rheumatoid factor (RF) compared with those without family history (78.48% vs. 66.67%, P<0.05). By adjusting with gender, body mass index (BMI), smoking and alcohol drinking, anti-cyclic citrullinated peptide (CCP) antibody and RF level, the age at disease onset in the patients with family history was 4.54 years earlier than that in the patients without family history (β=-4.54; 95%CI:-8.70, -0.38; P<0.05). Further hierarchical regression analysis showed that, the age at onset of the RA patients with family history was 10.02 years earlier than that without family history among the smoking patients (β= -10.02; 95%CI:-17.60, -2.43; P=0.01), while the age at onset of the RA patients with family history was 3.27 years earlier than that without family history among the never smoking patients (β=-3.27; 95%CI:-8.37, 1.82; P=0.21).

CONCLUSION

The family history of rheumatic diseases is a risk factor for early onset of RA, and may interact with smoking.

Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments.

OBJECTIVE

To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2).

DATA SOURCES

Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts.

STUDY SELECTION

Review 1 - primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 - primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients.

RESULTS

Review 1 - 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 - 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant.

LIMITATIONS

The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment.

SUGGESTED RESEARCH PRIORITIES

Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice.

CONCLUSIONS

Review 1 - uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 - in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016042402.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real use-cases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case. This work suggests it is possible to link de-identified research databases stripped of any personal health identifiers using only diagnosis codes, provided sufficient information is shared between the data sources.

Positive predictive value of first-time rheumatoid arthritis diagnoses and their serological subtypes in the Danish National Patient Registry

Purposes

To assess whether the positive predictive value (PPV) of first-time rheumatoid arthritis (RA) diagnosis registration in the Danish National Patient Registry increases when data are linked to the RA treatment codes and to assess the PPV of first-time RA diagnoses according to RA serological subtypes.

Methods

Participants from the Danish Diet, Cancer, and Health cohort with at least one RA diagnosis, registered at one of the Central Denmark Region hospitals in the Danish National Patient Registry during the period 1977–2016, were identified. Register-based RA diagnoses were verified by scrutinizing medical records against RA classification criteria or clinical case RA. PPVs for overall RA, seropositive RA, and other RA were calculated for two models: first-time RA diagnosis registration ever in the Danish National Patient Registry and first-time RA diagnosis registration ever where subsequently a prescription had been redeemed for a synthetic disease-modifying antirheumatic drug.

Results

Overall, 205 of 311 first-time register-based RA diagnoses were verified (PPV: 61.9%; 95% CI: 56.9–67.0). Regarding RA serological subtypes, 93 of 150 register-based seropositive RA (PPV: 62.0; 95% CI: 53.9–69.5) and 36 of 144 other RA (PPV: 25.0; 95% CI: 18.5–32.8) were confirmed. When register-based RA diagnosis codes were linked to RA treatment codes, the PPVs increased substantially: the PPV for overall RA was 87.7% (95% CI: 82.5–91.5), the PPV for seropositive RA was 80.2% (95% CI: 71.6–86.7), and the PPV for other RA was 41.1% (95% CI: 30.2–52.9).

Conclusion

The first-time RA diagnoses in the Danish National Patient Registry should be used with caution in epidemiology research. However, linking registry-based RA diagnoses to the subsequent RA treatment codes increases the probability of identifying true RA diagnoses, especially overall RA and seropositive RA.

Preventive Treatments for Rheumatoid Arthritis: Issues Regarding Patient Preferences

The detection of biomarkers in the preclinical phase of rheumatoid arthritis (RA) and recent therapeutic advances suggest that it may be possible to identify and treat persons at high risk and to prevent the development of RA. Several trials are ongoing to test the efficacy of a therapeutic intervention in primary prevention. This paper reviews potential populations that might be considered for preventative medication. Further, we review the medications that are being explored to treat individuals considered at high risk of developing RA. Finally, in a group of asymptomatic individuals at high risk of developing RA, we assessed which factors mattered most when considering a preventive therapeutic intervention and what type of preventive treatment would be most acceptable to them. Understanding subjects' perceptions of risks and benefits and willingness to undergo preventive therapy will be important in designing and implementing screening and preventive strategies.

Genetic Characterization and Influence on Inflammatory Bowel Disease Expression in a Diverse Hispanic South Florida Cohort

OBJECTIVES

Hispanics represent an understudied inflammatory bowel disease (IBD) population. Prior studies examining genetic predisposition to IBD in Hispanics are limited. In this study, we examined whether European-derived IBD variants confer risk in Hispanics and their influence on IBD phenotype in Hispanics compared to non-Hispanic whites (NHW).

METHODS

Self-identified Hispanics and NHWs with IBD were included. Hispanic controls were included for our genetic analyses. We performed single-variant testing at previously identified Crohn's disease (CD) and ulcerative colitis (UC) IBD variants in Hispanic cases and controls. These risk variants were used to compute individual genetic risk scores. Genetic risk scores and phenotype associations were compared between Hispanic and NHW.

RESULTS

A total of 1,115 participants were included: 698 controls and 417 IBD patients (230 Hispanics). We found evidence of association within our Hispanic cohort at 22 IBD risk loci, with ~76% of the risk loci demonstrating over-representation of the European risk allele; these included loci corresponding to IL23R and NOD2 genes. CD genetic risk score for Hispanics (199.67) was similar to the score for NHW (200.33), P=0.51; the same was true in UC. Genetic risk scores did not predict IBD phenotype or complications in Hispanics or NHW except for a younger age of CD onset in Hispanics (P=0.04).

CONCLUSIONS

This study highlights the fundamental importance of these loci in IBD pathogenesis including in our diverse Hispanic population. Future studies looking at non-genetic mechanisms of disease are needed to explain differences in age of presentation and phenotype between Hispanics and NHW.

Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn's disease

For most immune-mediated diseases, the main determinant of patient well-being is not the diagnosis itself but instead the course that the disease takes over time (prognosis). Prognosis may vary substantially between patients for reasons that are poorly understood. Familial studies support a genetic contribution to prognosis, but little evidence has been found for a proposed association between prognosis and the burden of susceptibility variants. To better characterize how genetic variation influences disease prognosis, we performed a within-cases genome-wide association study in two cohorts of patients with Crohn's disease. We identified four genome-wide significant loci, none of which showed any association with disease susceptibility. Conversely, the aggregated effect of all 170 disease susceptibility loci was not associated with disease prognosis. Together, these data suggest that the genetic contribution to prognosis in Crohn's disease is largely independent of the contribution to disease susceptibility and point to a biology of prognosis that could provide new therapeutic opportunities.

A Combination of CD28 (rs1980422) and IRF5 (rs10488631) Polymorphisms Is Associated with Seropositivity in Rheumatoid Arthritis: A Case Control Study

Introduction

The aim of the study was to analyse genetic architecture of RA by utilizing multiparametric statistical methods such as linear discriminant analysis (LDA) and redundancy analysis (RDA).

Methods

A total of 1393 volunteers, 499 patients with RA and 894 healthy controls were included in the study. The presence of shared epitope (SE) in HLA-DRB1 and 11 SNPs (PTPN22 C/T (rs2476601), STAT4 G/T (rs7574865), CTLA4 A/G (rs3087243), TRAF1/C5 A/G (rs3761847), IRF5 T/C (rs10488631), TNFAIP3 C/T (rs5029937), AFF3 A/T (rs11676922), PADI4 C/T (rs2240340), CD28 T/C (rs1980422), CSK G/A (rs34933034) and FCGR3A A/C (rs396991), rheumatoid factor (RF), anti–citrullinated protein antibodies (ACPA) and clinical status was analysed using the LDA and RDA.

Results

HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls in LDA. The correlation between RA diagnosis and the explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form. The correlation between serologic status and genetic determinants on the 1st ordinal axis was 0.468, and 0.145 on the 2nd one (Trace = 0.179; F = 6.135; P = 0.001). The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA.

Conclusions

The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA. Our study demonstrates that multiparametric analyses represent a powerful tool for investigation of mutual relationships of potential risk factors in complex diseases such as RA.

Post-Traumatic Stress Disorder and Risk for Incident Rheumatoid Arthritis

OBJECTIVE

To examine the association between symptoms of post-traumatic stress disorder (PTSD) and rheumatoid arthritis (RA) risk in a prospective cohort and to characterize the role of smoking in this relationship.

METHODS

A subset (n = 54,224) of the Nurses' Health Study II, a prospective cohort of female nurses, completed the Brief Trauma Questionnaire and a screen for PTSD symptoms. Participants were categorized based on trauma exposure and number of PTSD symptoms. Incident RA cases (n = 239) from 1989 to 2011 were identified. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs) between PTSD symptoms and incident RA. To identify the impact of smoking, secondary and subgroup analyses were performed. In all analyses, PTSD and smoking were lagged 2 years before the development of RA.

RESULTS

Compared to no history of trauma/PTSD symptoms, the HR for ≥4 PTSD symptoms and incident RA was 1.76 (95% CI 1.16-2.67) in models adjusted for age, race, and socioeconomic status. The risk for RA increased with an increasing number of PTSD symptoms (P = 0.01). When smoking was added to the model, the HR for RA remained elevated (HR 1.60 [95% CI 1.05-2.43]). In a subgroup analysis, excluding women who smoked before PTSD onset, results were unchanged (HR 1.68 [95% CI 1.04-2.70]).

CONCLUSION

This study suggests that women with high PTSD symptomatology have an elevated risk for RA, independent of smoking, adding to emerging evidence that stress is an important determinant of physical health.

A comparison of genomic profiles of complex diseases under different models

Background

Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task.

Results

We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium.

Conclusions

In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.

Immunogenetics of juvenile idiopathic arthritis: A comprehensive review

Juvenile idiopathic arthritis (JIA) is the most common chronic inflammatory arthropathy of childhood. Juvenile idiopathic arthritis is believed to be a complex genetic trait influenced by both genetic and environmental factors. Twin and family studies suggest a substantial role for genetic factors in the predisposition to JIA. Describing the genetics is complicated by the heterogeneity of JIA; the International League of Associations for Rheumatology (ILAR) has defined seven categories of JIA based on distinct clinical and laboratory features. Utilizing a variety of techniques including candidate gene studies, the use of genotyping arrays such as Immunochip, and genome wide association studies (GWAS), both human leukocyte antigen (HLA) and non-HLA susceptibility loci associated with JIA have been described. Several of these polymorphisms (e.g. HLA class II, PTPN22, STAT4) are shared with other common autoimmune conditions; other novel polymorphisms that have been identified may be unique to JIA. Associations with oligoarticular and RF-negative polyarticular JIA are the best characterized. A strong association between HLA DRB1:11:03/04 and DRB1:08:01, and a protective effect of DRB1:15:01 have been described. HLA DPB1:02:01 has also been associated with oligoarticular and RF-negative polyarticular JIA. Besides PTPN22, STAT4 and PTPN2 variants, IL2, IL2RA, IL2RB, as well as IL6 and IL6R loci also harbor variants associated with oligoarticular and RF-negative polyarticular JIA. RF-positive polyarticular JIA is associated with many of the shared epitope encoding HLA DRB1 alleles, as well as PTPN22, STAT4 and TNFAIP3 variants. ERA is associated with HLA B27. Most other associations between JIA categories and HLA or non-HLA variants need confirmation. The formation of International Consortia to ascertain and analyze large cohorts of JIA categories, validation of reported findings in independent cohorts, and functional studies will enhance our understanding of the genetic underpinnings of JIA.

The genetics of rheumatoid arthritis: risk and protection in different stages of the evolution of RA

There is now a general consensus that RA has a spectrum of disease stages that can begin many years before the onset of clinical symptoms. It is widely thought that understanding the complex interplay between genetics and environment, and their role in pathogenesis, is essential in gaining further insight into the mechanisms that drive disease development and progression. More than 100 genetic susceptibility loci have now been identified for RA through studies that have focused on patients with established RA compared with healthy controls. Studying the early preclinical phases of disease will provide valuable insights into the biological events that precede disease and could potentially identify biomarkers to predict disease onset and future therapeutic targets. In this review we will cover recent advances in the knowledge of genetic and environmental risk factors and speculate on how these factors may influence the transition from one stage of disease to another.

Prevention of rheumatic diseases: strategies, caveats, and future directions

Rheumatic diseases affect a significant portion of the population and lead to increased health care costs, disability, and premature mortality; effective preventive measures for these diseases could lead to substantial improvements in public health. Natural history studies show that for most rheumatic diseases there is a period of preclinical disease development during which abnormal biomarkers or other processes can be detected. These changes are useful to understand mechanisms of disease pathogenesis; in addition, they may be applied to estimate a personal risk of future disease while individuals are still relatively asymptomatic and ultimately be used to identify individuals who may be targeted for preventive interventions.

Judging health risk as a function of risk factors and type of illness: Do people weight risk factors in a flexible way?

We examined the extent to which lay people and health professionals are able to assess occurrence risks for multifactorial diseases. We asked 341 participants to assess the risk of developing lung cancer, coronary artery disease or rheumatoid arthritis in 16 scenarios, each featuring a combination of four factors (family history, daily alcohol intake, daily tobacco consumption and genetic test results). Participants considered all factors. However, they accorded more weight to tobacco and genetic test results. Moreover, it appears that where one of the factors (e.g. the presence of the incriminated gene) exerted a strong influence, the influence of the other factor(s) was correspondingly weaker. The health risk judgements of health professionals were more dependent on the specific disease and were also influenced to a greater degree by genetic information than lay people.

Differential effect of genetic burden on disease phenotypes in Crohn's disease and ulcerative colitis: analysis of a North American cohort

OBJECTIVES

Crohn's disease (CD) and ulcerative colitis (UC) are chronic immunologically mediated diseases with a progressive relapsing remitting course. There is considerable heterogeneity in disease course and accurate prediction of natural history has been challenging. The phenotypic implication of increasing genetic predisposition to CD or UC is unknown.

METHODS

The data source for our study was a prospective cohort of CD and UC patients recruited from a tertiary referral center. All patients underwent genotyping on the Illumina Immunochip. A genetic risk score (GRS) incorporating strength of association (log odds ratio) and allele dose for each of the 163 inflammatory bowel disease (IBD) risk loci was calculated and phenotypic associations examined across GRS quartiles.

RESULTS

Our study cohort included 1,105 patients (697 CD, 408 UC). Increasing genetic burden was associated with earlier age of diagnosis of CD (Ptrend=0.008). Patients in the highest GRS quartile were likely to develop disease 5 years earlier than those in the lowest quartile. Increasing genetic burden was also associated with ileal involvement in CD (Ptrend <0.0001). The effect of genetic burden was independent of the NOD2 locus and was stronger among those with no NOD2 variants, and in never smokers. UC patients with an involved first-degree relative had a higher genetic burden, but GRS was not associated with disease phenotype in UC.

CONCLUSIONS

Increasing genetic burden is associated with early age of diagnosis in CD, but not UC. The expanded panel of IBD risk loci explains only a fraction of variance of disease phenotype, suggesting limited clinical utility of genetics in predicting natural history.

Genetic risk scores and number of autoantibodies in patients with rheumatoid arthritis

OBJECTIVE

Certain HLA-DRB1 alleles and single-nucleotide polymorphisms (SNPs) are associated with rheumatoid arthritis (RA). Our objective was to examine the combined effect of these associated variants, calculated as a cumulative genetic risk score (GRS) on RA predisposition, as well as the number of autoantibodies (none, one or two present).

METHOD

We calculated four GRSs in 4956 patients and 4983 controls from four European countries. All four scores contained data on 22 non-HLA-risk SNPs, and three scores also contained HLA-DRB1 genotypes but had different HLA typing resolution. Most patients had data on both rheumatoid factor (RF) and anti-citrullinated proteins antibodies (ACPA). The GRSs were standardised (std.GRS) to account for population heterogeneity. Discrimination between patients and controls was examined by receiveroperating characteristics curves, and the four std.GRSs were compared across subgroups according to autoantibody status.

RESULTS

The std.GRS improved its discriminatory ability between patients and controls when HLA-DRB1 data of higher resolution were added to the combined score. Patients had higher mean std.GRS than controls (p=7.9×10(-156)), and this score was significantly higher in patients with autoantibodies (shown for both RF and ACPA). Mean std.GRS was also higher in those with two versus one autoantibody (p=3.7×10(-23)) but was similar in patients without autoantibodies and controls (p=0.12).

CONCLUSIONS

The GRS was associated with the number of autoantibodies and to both RF and ACPA positivity. ACPA play a more important role than RF with regards to the genetic risk profile, but stratification of patients according to both RF and ACPA may optimise future genetic studies.

RAvariome: a genetic risk variants database for rheumatoid arthritis based on assessment of reproducibility between or within human populations

Rheumatoid arthritis (RA) is a common autoimmune inflammatory disease of the joints and is caused by both genetic and environmental factors. In the past six years, genome-wide association studies (GWASs) have identified many risk variants associated with RA. However, not all associations reported from GWASs are reproduced when tested in follow-up studies. To establish a reliable set of RA risk variants, we systematically classified common variants identified in GWASs by the degree of reproducibility among independent studies. We collected comprehensive genetic associations from 90 papers of GWASs and meta-analysis. The genetic variants were assessed according to the statistical significance and reproducibility between or within nine geographical populations. As a result, 82 and 19 single nucleotide polymorphisms (SNPs) were confirmed as intra- and inter-population-reproduced variants, respectively. Interestingly, majority of the intra-population-reproduced variants from European and East Asian populations were not common in two populations, but their nearby genes appeared to be the components of common pathways. Furthermore, a tool to predict the individual’s genetic risk of RA was developed to facilitate personalized medicine and preventive health care. For further clinical researches, the list of reliable genetic variants of RA and the genetic risk prediction tool are provided by open access database RAvariome.

Database URL: http://hinv.jp/hinv/rav/

A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk

BACKGROUND

There is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA).

METHODS

A weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests.

RESULTS

Individuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity.

CONCLUSIONS

Our study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models.

Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

Rheumatoid arthritis (RA) is a common, incurable disease with major individual and health service costs. Preventing its development is therefore an important goal. Being able to predict who will develop RA would allow researchers to look at ways to prevent it. Many factors have been found that increase someone's risk of RA. These are divided into genetic and environmental (such as smoking) factors. The risk of RA associated with each factor has previously been reported. Here, we demonstrate a method that combines these risk factors in a process called “prediction modelling” to estimate someone's lifetime risk of RA. We show that firstly, our prediction models can identify people with very high-risks of RA and secondly, they can be used to identify people at risk of developing RA at a younger age. Although these findings are an important first step towards preventing RA, as only a minority of people tested had substantially increased disease risks our models could not be used to screen the general population. Instead they need testing in people already at risk of RA such as relatives of affected patients. In this context they could identify enough numbers of high-risk people to allow preventive methods to be evaluated.

Susceptibility to childhood-onset rheumatoid arthritis: investigation of a weighted genetic risk score that integrates cumulative effects of variants at five genetic loci

OBJECTIVE

Children with childhood-onset rheumatoid arthritis (RA) include those with rheumatoid factor or anti-citrullinated protein antibody-positive juvenile idiopathic arthritis. To test the hypothesis that adult-onset RA-associated variants are also associated with childhood-onset RA, we investigated RA-associated variants at 5 loci in a cohort of patients with childhood-onset RA. We also assessed the cumulative association of these variants in susceptibility to childhood-onset RA using a weighted genetic risk score (wGRS).

METHODS

A total of 155 children with childhood-onset RA and 684 healthy controls were genotyped for 5 variants in the PTPN22, TRAF1/C5, STAT4, and TNFAIP3 loci. High-resolution HLA-DRB1 genotypes were available for 149 cases and 373 controls. We tested each locus for association with childhood-onset RA via logistic regression. We also computed a wGRS for each subject, with weights based on the natural log of the published odds ratios (ORs) for the alleles investigated, and used logistic regression to test the wGRS for association with childhood-onset RA.

RESULTS

Childhood-onset RA was associated with TNFAIP3 rs10499194 (OR 0.60 [95% confidence interval 0.44-0.83]), PTPN22 rs2476601 (OR 1.61 [95% confidence interval 1.11-2.31]), and STAT4 rs7574865 (OR 1.41 [95% confidence interval 1.06-1.87]) variants. The wGRS was significantly different between cases and controls (P < 2 × 10(-16) ). Individuals in the third to fifth quintiles of wGRS had a significantly increased disease risk compared to baseline (individuals in the first quintile). Higher wGRS was associated with increased risk of childhood-onset RA, especially among males.

CONCLUSION

The magnitude and direction of the association between TNFAIP3, STAT4, and PTPN22 variants and childhood-onset RA are similar to those observed in RA, suggesting that adult-onset RA and childhood-onset RA share common genetic risk factors. Using a wGRS, we have demonstrated the cumulative association of RA-associated variants with susceptibility to childhood-onset RA.

Major trends in the manifestations and treatment of rheumatoid arthritis in a multiethnic cohort in Singapore

We analyzed the epidemiological changes of rheumatoid arthritis (RA) over three decades using patients from a single center in Singapore. All patients who fulfill the 1987 American College of Rheumatology criteria for RA were invited to enroll in a prospective disease registry. We analyzed the patient demographics, disease manifestation, management and patient-reported outcomes, including quality of life (QoL), in the three categories according to the year of disease onset: before 1989 (group I), 1990-1999 (group II) and after 2000 (group III). There were 1,153 patients with 231, 532 and 390 in groups I, II and III, respectively. The mean disease durations were 25, 12 and 4.8 years, respectively. The majority was female (84.1 %) and Chinese (76.6 %) with no socio-demographic differences across the three periods. The age of onset rises and the prevalence of rheumatoid factor falls with the proximity of disease onset. Patients with most recent disease onset had the earliest access to the rheumatologist. They also had the highest tender and swollen joint counts, lowest deformed joint count and highest remission rate. Patients in group I report better mental and emotional QoL though many developed marked disability. We have documented changes of the manifestations of RA that are dependent and independent of improved treatment. Significant differences in accessibility to the rheumatologist, RA activity, functional capacity, quality of life and comorbidities were seen in subsequent cohorts due to treatment evolution and more efficient healthcare delivery.

Investigation of Caucasian rheumatoid arthritis susceptibility loci in African patients with the same disease

INTRODUCTION

The largest genetic risk to develop rheumatoid arthritis (RA) arises from a group of alleles of the HLA DRB1 locus ('shared epitope', SE). Over 30 non-HLA single nucleotide polymorphisms (SNPs) predisposing to disease have been identified in Caucasians, but they have never been investigated in West/Central Africa. We previously reported a lower prevalence of the SE in RA patients in Cameroon compared to European patients and aimed in the present study to investigate the contribution of Caucasian non-HLA RA SNPs to disease susceptibility in Black Africans.

METHODS

RA cases and controls from Cameroon were genotyped for Caucasian RA susceptibility SNPs using Sequenom MassArray technology. Genotype data were also available for 5024 UK cases and 4281 UK controls and for 119 Yoruba individuals in Ibadan, Nigeria (YRI, HapMap). A Caucasian aggregate genetic-risk score (GRS) was calculated as the sum of the weighted risk-allele counts.

RESULTS

After genotyping quality control procedures were performed, data on 28 Caucasian non-HLA susceptibility SNPs were available in 43 Cameroonian RA cases and 44 controls. The minor allele frequencies (MAF) were tightly correlated between Cameroonian controls and YRI individuals (correlation coefficient 93.8%, p = 1.7E-13), and they were pooled together. There was no correlation between MAF of UK and African controls; 13 markers differed by more than 20%. The MAF for markers at PTPN22, IL2RA, FCGR2A and IL2/IL21 was below 2% in Africans. The GRS showed a strong association with RA in the UK. However, the GRS did not predict RA in Africans (OR = 0.71, 95% CI 0.29 - 1.74, p = 0.456). Random sampling from the UK cohort showed that this difference in association is unlikely to be explained by small sample size or chance, but is statistically significant with p<0.001.

CONCLUSIONS

The MAFs of non-HLA Caucasian RA susceptibility SNPs are different between Caucasians and Africans, and several polymorphisms are barely detectable in West/Central Africa. The genetic risk of developing RA conferred by a set of 28 Caucasian susceptibility SNPs is significantly different between the UK and Africa with p<0.001. Taken together, these observations strengthen the hypothesis that the genetic architecture of RA susceptibility is different in different ethnic backgrounds.

Markers of treatment response to methotrexate in rheumatoid arthritis: where do we stand?

Methotrexate (MTX) is the most commonly used disease-modifying antirheumatic drug (DMARD) for the treatment of rheumatoid arthritis (RA). However, despite its efficacy and affordability, additional DMARDs or biologic agents are often required in order to achieve the recommended goals of low disease activity or remission. Although well tolerated by most, some patients develop important side effects such as cytopenias, gastrointestinal adverse events (stomatitis, nausea), or abnormal liver function tests, which may limit its use and may result in additional health care costs. Given the clinical implications of widespread use of MTX in RA, various studies have evaluated the role of potential biomarkers in predicting treatment effectiveness of MTX. These biomarkers include RBC MTX polyglutamate (PG) levels; genetic variation in genes from relevant biological and metabolic pathways; gene expression profiles; serum proteins. This paper provides an update on the current data regarding biomarkers of treatment response to MTX.

Genetics of rheumatoid arthritis: an impressionist perspective

Rheumatoid arthritis (RA) is the most common rheumatic disease. The genetic basis of RA is supported through the identification of more than 30 susceptibility genetic variants. Each of these genes individually makes only a slight contribution to the risk of disease. Moreover, there is significant disparity in the genetic variants associated with different RA subgroups and patient ethnicities, which emphasizes the intricate nature of the disease's pathogenesis, and the complexities involved in large-scale genetic studies. This review evaluates critically the recent literature on the genetic contribution to RA and assesses the methodology used to identify these risk alleles.

Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!

One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance.