Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records

Development and internal validation of a prediction model for rheumatoid arthritis: a case-control study

This study measured sociodemographic characteristics, dietary habits, lifestyle habits, genetics, and other factors that may contribute to the development of Rheumatoid Arthritis (RA). Independent risk factors for RA were identified by logistic regression analysis, and a prediction model was constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction accuracy of the model, and the calibration of the model was evaluated by the Hosmer-Lemeshow test. A total of 432 participants, comprising 216 healthy individuals and 216 patients diagnosed with RA at two hospitals in Sichuan, China, from March 2022 to January 2023 were included in this study. Logistic regression analysis revealed that occupation type, place of residence, history of mumps, dietary combination, sweet, damp dwelling, fish, vaccine history, and rs805297 were significantly associated with the pathogenesis of RA. The model constructed in this study showed good prediction, with an AUC of 0.912. The Youden index was 0.699, the sensitivity was 0.847 and the specificity was 0.852. The Hosmer-Lemeshow test results (χ2 = 8.441, P = 0.392) indicated that the model had good diagnostic value. The internal validation AUC was 0.942. We propose a new promising model for identifying individuals at risk of developing RA.

Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel Doubly Robust Augmented Model Accuracy Transfer InferenCe (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population with labeled source data. DRAMATIC derives and evaluates a potentially misspecified risk model for a binary response, leveraging high-dimensional adjustment features from both source and target data. It builds on an imputation model for the response mean and a density ratio model to characterize distributional shifts. The method constructs doubly robust estimators that are valid when either model is correctly specified and certain sparsity assumptions hold. Simulations show negligible bias in point estimation and satisfactory empirical coverage levels in confidence intervals. The utility of DRAMATIC is illustrated by transferring a genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms

OBJECTIVE

High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity).

MATERIALS AND METHODS

ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB).

RESULTS

ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average.

DISCUSSION

ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software.

CONCLUSION

When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.

Proteomics profiling of CD4 + T-cell-derived exosomes from patients with rheumatoid arthritis

OBJECTIVES

Our study profiled the CD4 + T-cell-derived exosomes from patients with rheumatoid arthritis (RA) using proteomics.

METHODS

Proteomic analysis of CD4 + T-cell-derived exosomes was performed by tandem mass tags (TMT) combined with LC-MS/MS. We validated the most significantly upregulated and downregulated proteins using ELISA and WB.

RESULTS

The proteomic results showed that there were 3 upregulated differentially expressed proteins and 31 downregulated differentially expressed proteins in the RA group. The results indicated that dihydropyrimidinase-related protein 3 (DPYSL3) was significantly upregulated in CD4 + T-cell-derived exosomes, whereas proteasome activator complex subunit 1 (PSME1) was significantly downregulated in the RA group. Bioinformatics analysis showed that proteins were enriched in "positive regulation of gene expression", "antigen processing and presentation", "acute-phase response" and "PI3K-AKT signaling" pathways. ELISA verified that compared to the control group, the RA group showed significant upregulation of DPYSL3, and downregulation of PSME1 in CD4 + T-cell-derived exosomes.

CONCLUSIONS

The proteomic analysis results of CD4 + T-cell-derived exosomes from patients with RA suggest that these differentially expressed proteins may be involved in RA pathogenesis. DPYSL3 and PSME1 may become useful biomarkers for RA.

Pharmacogenomics of Monoclonal Antibodies for the Treatment of Rheumatoid Arthritis

Precision medicine refers to a highly individualized and personalized approach to patient care. Pharmacogenomics is the study of how an individual’s genomic profile affects their drug response, enabling stable and effective drug selection, minimizing side effects, and maximizing therapeutic efficacy. Rheumatoid arthritis (RA) is an autoimmune disease that causes chronic inflammation in the joints. It mainly starts in peripheral joints, such as the hands and feet, and progresses to large joints, which causes joint deformation and bone damage due to inflammation of the synovial membrane. Here, we review various pharmacogenetic studies investigating the association between clinical response to monoclonal antibody therapy and their target genetic polymorphisms. Numerous papers have reported that some single nucleotide polymorphisms (SNPs) are related to the therapeutic response of several monoclonal antibody drugs including adalimumab, infliximab, rituximab, and tocilizumab, which target tumor necrosis factor (TNF), CD20 of B-cells, and interleukin (IL)-6. Additionally, there are some pharmacogenomic studies reporting on the association between the clinical response of monoclonal antibodies having various mechanisms, such as IL-1, IL-17, IL-23, granulocyte-macrophage colony-stimulating factor (GM-CSF) and the receptor activator of nuclear factor-kappa B (RANK) inhibition. Biological therapies are currently prescribed on a “trial and error” basis for RA patients. If appropriate drug treatment is not started early, joints may deform, and long-term treatment outcomes may worsen. Pharmacogenomic approaches that predict therapeutic responses for RA patients have the potential to significantly improve patient quality of life and reduce treatment costs.

The U.S. National Library of Medicine’s impact on precision and genomic medicine

Precision medicine offers the potential to improve health through deeper understandings of the lifestyle, biological, and environmental influences on health. Under Dr. Donald A. B. Lindberg’s leadership, the U.S. National Library of Medicine (NLM) has developed the central reference resources for biomedical research and molecular laboratory medicine that enable precision medicine. The hosting and curation of biomedical knowledge repositories and data by NLM enable quality information reachable for providers and researchers throughout the world. NLM has been supporting the innovation of electronic health record systems to implement computability and secondary use for biomedical research, producing the scale of linked health and molecular datasets necessary for precision medicine discovery.

Systematic Evaluation of Rheumatoid Arthritis Risk by Integrating Lifestyle Factors and Genetic Risk Scores

Background

Effective identification of high-risk rheumatoid arthritis (RA) individuals is still a challenge. Whether the combined effects of multiple previously reported genetic loci together with lifestyle factors can improve the prediction of RA risk remains unclear.

Methods

Based on previously reported results and a large-scale Biobank dataset, we constructed a polygenic risk score (PRS) for RA to evaluate the combined effects of the previously identified genetic loci in both case-control and prospective cohorts. We then evaluated the relationships between several lifestyles and RA risk and determined healthy lifestyles. Then, the joint effects of healthy lifestyles and genetic risk on RA risk were evaluated.

Results

We found a positive association between PRS and RA risk (OR = 1.407, 95% confidence interval (CI) = 1.354~1.463; HR = 1.316, 95% CI = 1.257~1.377). Compared with the low genetic risk group, the group with intermediate or high genetic risk had a higher risk (OR = 1.347, 95% CI = 1.213~1.496; HR = 1.246, 95% CI = 1.108~1.400) (OR = 2.169, 95% CI = 1.946~2.417; HR = 1.762, 95% CI = 1.557~1.995). After adjusting for covariates, we found protective effects of three lifestyles (no current smoking, regular physical activity, and moderate body mass index) on RA risk and defined them as healthy lifestyles. Compared with the individuals with low genetic risks and favorable lifestyles, those with high genetic risks and unfavorable lifestyles had as high as OR of 4.637 (95%CI = 3.767~5.708) and HR of 3.532 (95%CI = 2.799~4.458).

Conclusions

In conclusion, the integration of PRS and lifestyles can improve the prediction of RA risk. High RA risk can be alleviated by adopting healthy lifestyles but aggravated by adopting unfavorable lifestyles.

ATLAS: an automated association test using probabilistically linked health records with application to genetic studies

OBJECTIVE

Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.

MATERIALS AND METHODS

Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling.

RESULTS

In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers.

DISCUSSION

Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power.

CONCLUSION

ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.

Subcutaneous Tocilizumab in Monotherapy or in Combination With Nonbiologic Disease-Modifying Antirheumatic Drugs in Latin American Patients With Moderate to Severe Active Rheumatoid Arthritis: A Multicenter, Phase IIIb Study

OBJECTIVE

To assess the efficacy and safety of subcutaneous tocilizumab (TCZ) in combination with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) in Latin American patients with rheumatoid arthritis (RA) and inadequate response to previous csDMARDs.

METHODS

ML28700 was a multicenter, open-label, single-arm trial. Previously treated RA patients who had not received treatment with TCZ or any biological agent (n = 284) and with a baseline Disease Activity Score in 28 joints based on erythrocyte sedimentation rate (DAS28-ESR) of 3.2 or greater were assigned to receive subcutaneous TCZ (162 mg/wk) in association with csDMARD for 24 weeks. Patients who achieved remission (DAS28-ESR <2.6) at week 24 continued with TCZ as monotherapy until week 52; otherwise, they continued with their assigned treatment. The primary efficacy end point was remission rate (DAS28-ESR <2.6) at weeks 24 and 52. Secondary objectives included disease activity scores, safety, and quality of life.

RESULTS

At week 24, a total of 169 patients (59.5%; 95% confidence interval, 53.5%-65.3%) achieved remission, 91 patients (32.0%) had low disease activity, and 46 patients (8.4%) were not responders. Sustained remission at week 52 was achieved by 80.8% (n = 126) of patients who continued with TCZ monotherapy versus 44.6% (n = 37) of those on combination therapy. A total of 241 patients (84.9%; 95% confidence interval, 80.2%-88.8%) had at least 1 adverse event during follow-up. Adverse events led to drug modification in 32 patients (11.3%) or discontinuation in 21 patients (7.4%).

CONCLUSIONS

Subcutaneous TCZ is an efficacious therapy with long-lasting results and tolerable adverse events in Latin American patients with RA.Trial registration no.: NCT02011334 Tozura Study Program.

Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu.

Development of an Algorithm to Identify Cases of Nonalcoholic Steatohepatitis Cirrhosis in the Electronic Health Record

BACKGROUND AND AIMS

Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.

METHODS

We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.

RESULTS

The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and ≥ 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.

CONCLUSIONS

We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis.

Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms

OBJECTIVE

The objective of this study was to compare the performance of an RA algorithm developed and trained in 2010 utilizing natural language processing and machine learning, using updated data containing ICD10, new RA treatments, and a new electronic medical records (EMR) system.

METHODS

We extracted data from subjects with ≥1 RA International Classification of Diseases (ICD) codes from the EMR of two large academic centres to create a data mart. Gold standard RA cases were identified from reviewing a random 200 subjects from the data mart, and a random 100 subjects who only have RA ICD10 codes. We compared the performance of the following algorithms using the original 2010 data with updated data: (i) a published 2010 RA algorithm; (ii) updated algorithm, incorporating ICD10 RA codes and new DMARDs; and (iii) published algorithm using ICD codes only, ICD RA code ≥3.

RESULTS

The gold standard RA cases had mean age 65.5 years, 78.7% female, 74.1% RF or antibodies to cyclic citrullinated peptide (anti-CCP) positive. The positive predictive value (PPV) for ≥3 RA ICD was 54%, compared with 56% in 2010. At a specificity of 95%, the PPV of the 2010 algorithm and the updated version were both 91%, compared with 94% (95% CI: 91, 96%) in 2010. In subjects with ICD10 data only, the PPV for the updated 2010 RA algorithm was 93%.

CONCLUSION

The 2010 RA algorithm validated with the updated data with similar performance characteristics as the 2010 data. While the 2010 algorithm continued to perform better than the rule-based approach, the PPV of the latter also remained stable over time.

Presence of autoantibodies in "seronegative" rheumatoid arthritis associates with classical risk factors and high disease activity

BACKGROUND

Rheumatoid arthritis (RA) is classified as seropositive or seronegative, depending on the presence/absence of rheumatoid factor (RF), primarily IgM RF, and/or anti-citrullinated protein antibodies (ACPA), commonly detected using anti-cyclic citrullinated peptide (CCP) assays. Known risk factors associate with the more severe seropositive form of RA; less is known about seronegative RA. Here, we examine risk factors and clinical phenotypes in relation to presence of autoantibodies in the RA subset that is traditionally defined as seronegative.

METHODS

Anti-CCP2 IgG, 19 ACPA fine-specificities, IgM/IgG/IgA RF, anti-carbamylated-protein (CarP) antibodies, and 17 other autoantibodies, were analysed in 2755 RA patients and 370 controls. Antibody prevalence, levels, and co-occurrence were examined, and associations with risk factors and disease activity during 5 years were investigated for different antibody-defined RA subsets.

RESULTS

Autoantibodies were detected in a substantial proportion of the traditionally defined seronegative RA subset, with ACPA fine-specificities found in 30%, IgA/IgG RF in 9.4%, and anti-CarP antibodies in 16%, with a 9.6% co-occurrence of at least two types of RA-associated autoantibodies. HLA-DRB1 shared epitope (SE) associated with the presence of ACPA in anti-CCP2-negative RA; in anti-CCP2-positive RA, the SE association was defined by six ACPA fine-specificities with high co-occurrence. Smoking associated with RF, but not with ACPA, in anti-CCP2-negative RA. Presence of ACPA and RF, but not anti-CarP antibodies, in conventionally defined "seronegative" RA, associated with worse clinical outcome.

CONCLUSIONS

"Seronegative" RA is not truly a seronegative disease subset. Additional screening for ACPA fine-specificities and IgA/IgG RF defines a group of patients that resembles seropositive patients with respect to risk factors and clinical picture and may contribute to earlier diagnosis for a subset of anti-CCP2-/IgM RF- patients with a high need for active treatment.

Impact of Diverse Data Sources on Computational Phenotyping

Electronic health records (EHRs) are widely adopted with a great potential to serve as a rich, integrated source of phenotype information. Computational phenotyping, which extracts phenotypes from EHR data automatically, can accelerate the adoption and utilization of phenotype-driven efforts to advance scientific discovery and improve healthcare delivery. A list of computational phenotyping algorithms has been published but data fragmentation, i.e., incomplete data within one single data source, has been raised as an inherent limitation of computational phenotyping. In this study, we investigated the impact of diverse data sources on two published computational phenotyping algorithms, rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM), using Mayo EHRs and Rochester Epidemiology Project (REP) which links medical records from multiple health care systems. Results showed that both RA (less prevalent) and T2DM (more prevalent) case selections were markedly impacted by data fragmentation, with positive predictive value (PPV) of 91.4 and 92.4%, false-negative rate (FNR) of 26.6 and 14% in Mayo data, respectively, PPV of 97.2 and 98.3%, FNR of 5.2 and 3.3% in REP. T2DM controls also contain biases, with PPV of 91.2% and FNR of 1.2% for Mayo. We further elaborated underlying reasons impacting the performance.

Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS

Motivated by applications in phenome-wide association studies (PheWAS), we consider in this paper simultaneous testing of columns of high-dimensional cross-covariance matrices and develop a multiple testing procedure with theoretical guarantees. It is shown that the proposed testing procedure maintains a desired false discovery rate (FDR) and false discovery proportion (FDP) under mild regularity conditions. We also provide results on the magnitudes of the signals that can be detected with high power. Simulation studies demonstrate that the proposed procedure can be substantially more powerful than existing FDR controlling procedures in the presence of correlation of unknown structure. The proposed multiple testing procedure is applied to a PheWAS of two auto-immune genetic markers using a rheumatoid arthritis patient cohort constructed from the electronic medical records of Partners Healthcare System.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

Objective

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).

The use of electronic health records for psychiatric phenotyping and genomics

The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

The potential of PTPN22 as a therapeutic target for rheumatoid arthritis

INTRODUCTION

PTPN22 encodes a lymphoid-specific tyrosine phosphatase (LYP) that is a master regulator of the immune response. This gene is a major susceptibility factor for a wide range of autoimmune conditions, including rheumatoid arthritis (RA) for which it represents the strongest non-HLA contributor to disease risk. A missense PTPN22 allele (R620W) affecting the protein-protein interaction of LYP with other relevant players was described as the functional variant of the association. This review will focus on the role of PTPN22 in the pathogenic mechanisms underlying RA predisposition and discuss the possibility of developing LYP-based treatment strategies with a potential application in clinical practice. Areas covered: This review covers the literature showing how PTPN22 is implicated in signalling pathways involved in the autoimmune and autoinflammatory processes underlying RA. Insights obtained from studies aimed at developing novel selective LYP suppressors for treating RA are summarized. Expert opinion: Targeting key risk factors during the early steps of the disease may represent a good strategy to accomplish complete disease remission. As cumulating evidences suggest that PTPN22 R620W is a gain-of-function variant, a growing interest in developing LYP inhibitors has arisen. The potential efficacy and possible application of such compounds are discussed.

Association Between Anti-Citrullinated Fibrinogen Antibodies and Coronary Artery Disease in Rheumatoid Arthritis

OBJECTIVE

Antibodies against citrullinated fibrinogen (anti-Cit-fibrinogen) have been implicated in rheumatoid arthritis (RA) and associated with cardiovascular risk in RA. The objective of this study was to examine the association between anti-Cit-fibrinogens and coronary artery disease (CAD) outcomes.

METHODS

We performed the study in an RA cohort based in a large academic institution linked with electronic medical record data containing information on CAD outcomes from medical record review. Using a published bead-based assay method, we measured 10 types of anti-Cit-fibrinogens. We applied a score test to determine the association between the anti-Cit-fibrinogens as a group with CAD outcomes. Principal components analysis (PCA) was performed to assess whether the anti-Cit-fibrinogens clustered into groups. Each group was then additionally tested for association with CAD. Sensitivity analyses were also performed using a published International Classification of Disease, Ninth Revision code group for ischemic heart disease (IHD) as the outcome.

RESULTS

We studied 1,006 RA subjects (mean ± SD age 61.0 ± 13.0 years; 72.2% anti-cyclic citrullinated peptide positive). As a group, anti-Cit-fibrinogen was associated with CAD (P = 1.1 × 10^-4 ). From the PCA analysis, we observed 3 main groups, of which only 1 group, containing 7 of the 10 anti-Cit-fibrinogens, was significantly associated with CAD outcomes (P = 0.015). In the sensitivity analysis, all anti-Cit-fibrinogens as a group remained significantly associated with IHD (P = 2.9 × 10^-4 ).

CONCLUSION

Anti-Cit-fibrinogen antibodies as a group were associated with CAD outcomes in our RA cohort, with the strongest signal for association arising from a subset of the autoantibodies.

Defining Phenotypes from Clinical Data to Drive Genomic Research

The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.

Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p = 0.004). These h2g were lower or similar to the h2g observed by the ICCBD + PGCBD (0.23, p = 3.17E-80, total N = 33,181). However, the rg between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10-5), coded-strict (1.00, p = 2.40 × 10-4), and coded-broad (0.74, p = 8.11 × 10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

Effects of Amerindian Genetic Ancestry on Clinical Variables and Therapy in Patients with Rheumatoid Arthritis

OBJECTIVE

To define whether Amerindian genetic ancestry correlates with clinical and therapeutic variables in admixed individuals with rheumatoid arthritis (RA) from Latin America.

METHODS

Patients with RA (n = 1347) and healthy controls (n = 1012) from Argentina, Mexico, Chile, and Peru were included. Samples were genotyped for the Immunochip v1 using the Illumina platform. Clinical data were obtained through interviews or the clinical history.

RESULTS

Percentage of Amerindian ancestry was comparable between cases and controls. Morning stiffness (p < 0.0001, OR 0.05), rheumatoid factor (RF; p < 0.0001, OR 0.22), radiographic changes (p < 0.0001, OR 0.05), and higher number of criteria were associated with lower Amerindian ancestry after Bonferroni correction. Higher Amerindian ancestry correlated only with weight loss (p_Bonferroni < 0.0001, OR 2.85). Increased Amerindian ancestry correlated with higher doses of azathioprine (p < 0.0001, OR 163.6) and sulfasalazine (p < 0.0001, OR 48.6), and inversely with methotrexate (p = 0.001, OR 0.35), leflunomide (p = 0.001, OR 0.16), and nonsteroidal antiinflammatory drugs (p_Bonferroni = 0.001, OR 0.37). Only the presence of RF and weight loss were modified after confounders adjustment.

CONCLUSION

Amerindian ancestry protects against most major clinical criteria of RA, but regarding the association of RF with increased European ancestry, age, sex, and smoking are modifiers. Ancestry also correlates with the therapeutic profiles.

Phenome-Wide Association Study of Rheumatoid Arthritis Subgroups Identifies Association Between Seronegative Disease and Fibromyalgia

OBJECTIVE

The differences between seronegative and seropositive rheumatoid arthritis (RA) have not been widely reported. We performed electronic health record (EHR)-based phenome-wide association studies (PheWAS) to identify disease associations in seropositive and seronegative RA.

METHODS

A validated algorithm identified RA subjects from the de-identified version of the Vanderbilt University Medical Center EHR. Serotypes were determined by rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (ACPA) values. We tested EHR-derived phenotypes using PheWAS comparing seropositive RA and seronegative RA, yielding disease associations. PheWAS was also performed in RF-positive versus RF-negative subjects and ACPA-positive versus ACPA-negative subjects. Following PheWAS, select phenotypes were then manually reviewed, and fibromyalgia was specifically evaluated using a validated algorithm.

RESULTS

A total of 2,199 RA individuals with either RF or ACPA testing were identified. Of these, 1,382 patients (63%) were classified as seropositive. Seronegative RA was associated with myalgia and myositis (odds ratio [OR] 2.1, P = 3.7 × 10^-10 ) and back pain. A manual review of the health record showed that among subjects coded for Myalgia and Myositis, ∼80% had fibromyalgia. Follow-up with a specific EHR algorithm for fibromyalgia confirmed that seronegative RA was associated with fibromyalgia (OR 1.8, P = 4.0 × 10^-6 ). Seropositive RA was associated with chronic airway obstruction (OR 2.2, P = 1.4 × 10^-4 ) and tobacco use (OR 2.2, P = 7.0 × 10^-4 ).

CONCLUSION

This PheWAS of RA patients identifies a strong association between seronegativity and fibromyalgia. It also affirms relationships between seropositivity and chronic airway obstruction and between seropositivity and tobacco use. These findings demonstrate the utility of the PheWAS approach to discover novel phenotype associations within different subgroups of a disease.

Phenome-Wide Association Study of Autoantibodies to Citrullinated and Noncitrullinated Epitopes in Rheumatoid Arthritis

OBJECTIVE

Patients with rheumatoid arthritis (RA) develop autoantibodies against a spectrum of antigens, but the clinical significance of these autoantibodies is unclear. Using a phenome-wide association study (PheWAS) approach, we examined the association between autoantibodies and clinical subphenotypes of RA.

METHODS

This study was conducted in a cohort of RA patients identified from the electronic medical records (EMRs) of 2 tertiary care centers. Using a published multiplex bead assay, we measured 36 autoantibodies targeting epitopes implicated in RA. We extracted all International Classification of Diseases, Ninth Revision (ICD-9) codes for each subject and grouped them into disease categories (PheWAS codes), using a published method. We tested for the association of each autoantibody (grouped by the targeted protein) with PheWAS codes. To determine significant associations (at a false discovery rate [FDR] of ≤0.1), we reviewed the medical records of 50 patients with each PheWAS code to determine positive predictive values (PPVs).

RESULTS

We studied 1,006 RA patients; the mean ± SD age of the patients was 61.0 ± 12.9 years, and 79.0% were female. A total of 3,568 unique ICD-9 codes were grouped into 625 PheWAS codes; the 206 PheWAS codes with a prevalence of ≥3% were studied. Using the PheWAS method, we identified 24 significant associations of autoantibodies to epitopes at an FDR of ≤0.1. The associations that were strongest and had the highest PPV for the PheWAS code were autoantibodies against fibronectin and obesity (P = 6.1 × 10^-4 , PPV 100%), and that between fibrinogen and pneumonopathy (P = 2.7 × 10^-4 , PPV 96%). Pneumonopathy codes included diagnoses for cryptogenic organizing pneumonia and obliterative bronchiolitis.

CONCLUSION

We demonstrated application of a bioinformatics method, the PheWAS, to screen for the clinical significance of RA-related autoantibodies. Using the PheWAS approach, we identified potentially significant links between variations in the levels of autoantibodies and comorbidities of interest in RA.

Replication of Associations of Genetic Loci Outside the HLA Region With Susceptibility to Anti-Cyclic Citrullinated Peptide-Negative Rheumatoid Arthritis

Objective

Genetic polymorphisms within the HLA region explain only a modest proportion of anti–cyclic citrullinated peptide (anti‐CCP)–negative rheumatoid arthritis (RA) heritability. However, few non‐HLA markers have been identified so far. This study was undertaken to replicate the associations of anti‐CCP–negative RA with non‐HLA genetic polymorphisms demonstrated in a previous study.

Methods

The Rheumatoid Arthritis Consortium International densely genotyped 186 autoimmune‐related regions in3,339 anti‐CCP–negative RA patients and 15,870 controls across 6 different populations using the Illumina ImmunoChip array. We performed a case–control replication study of the anti‐CCP–negative markers with the strongest associations in that discovery study, in an independent cohort of anti‐CCP–negative UK RA patients. Individuals from the arcOGENConsortium and Wellcome Trust Case Control Consortium were used as controls. Genotyping in cases was performed using Sequenom MassArray technology. Genome‐wide data from controls were imputed using the 1000 Genomes Phase I integrated variant call set release version 3 as a reference panel.

Results

After genotyping and imputation quality control procedures, data were available for 15 non‐HLA single‐nucleotide polymorphisms in 1,024 cases and 6,348 controls. We confirmed the known markers ANKRD55 (meta‐analysis odds ratio [OR] 0.80; P = 2.8 × 10⁻¹³)and BLK (OR 1.13; P = 7.0 × 10⁻⁶) and identified new and specific markers of anti‐CCP–negative RA (prolactin [PRL] [OR 1.13; P = 2.1 × 10⁻⁶] and NFIA [OR 0.85; P = 2.5 × 10⁻⁶]). Neither of these loci is associated with other common, complex autoimmune diseases.

Conclusion

Anti‐CCP–negative RA and anti‐CCP–positive RA are genetically different disease subsets that only partially share susceptibility factors. Genetic polymorphisms located near the PRL and NFIA genes represent examples of genetic susceptibility factors specific for anti‐CCP–negative RA.

Anti-Apolipoprotein A-1 IgG Predict All-Cause Mortality and Are Associated with Fc Receptor-Like 3 Polymorphisms

BACKGROUND

Autoantibodies against apolipoprotein A-1 (anti-apoA-1 IgG) have emerged as an independent biomarker for cardiovascular disease and mortality. However, their association with all-cause mortality in the community, as well as their genetic determinants, have not been studied.

OBJECTIVE

To determine whether anti-apoA-1 IgG: (a) predict all-cause mortality in the general population and (b) are associated with single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS).

METHODS

Clinical, biological, and genetic data were obtained from the population-based, prospective CoLaus study, including 5,220 participants (mean age 52.6 years, 47.3% men) followed over a median duration of 5.6 years. The primary study outcome was all-cause mortality.

RESULTS

After multivariate adjustment, anti-apoA-1 IgG positivity independently predicted all-cause mortality: hazard ratio (HR) = 1.54, 95% confidence interval (95% CI): 1.11-2.13, P = 0.01. A dose-effect relationship was also observed, each SD of logarithmically transformed anti-apoA-1 IgG being associated with a 15% increase in mortality risk: HR = 1.15, 95% CI: 1.02-1.28, P = 0.028. The GWAS yielded nine SNPs belonging to the Fc receptor-like 3 (FCRL3) gene, which were significantly associated with anti-apoA-1 IgG levels, with the lead SNP (rs6427397, P = 1.54 × 10-9) explaining 0.67% of anti-apoA-1 IgG level variation.

CONCLUSION

Anti-apoA-1 IgG levels (a) independently predict all-cause mortality in the general population and (b) are linked to FCRL3, a susceptibility gene for numerous autoimmune diseases. Our findings indicate that preclinical autoimmunity to anti-apoA-1 IgG may represent a novel mortality risk factor.

Combining clinical and genomics queries using i2b2 - Three methods

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical "Big Data" challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

Differences in the Spectrum of Anti-Citrullinated Protein Antibody Fine Specificities Between Malaysian and Swedish Patients With Rheumatoid Arthritis: Implications for Disease Pathogenesis

Objective

Antibodies to the citrullinated protein antigens (ACPAs) are important in the diagnosis and pathogenesis of rheumatoid arthritis (RA). However, the prevalence of ACPAs with different fine specificities in different populations is unclear. This study sought to examine the fine specificity of the antibody responses toward citrullinated proteins in RA patients from Malaysia, an area where genetic and environmental determinants of RA are different from those in more frequently studied cohorts of Caucasian subjects.

Methods

A multiplex analytic microarray system was used to analyze the occurrence of antibodies to 10 different citrullinated peptides (filaggrin [fil307–324], vimentin [Vim2–17, Vim60–75], fibrinogen [Fibα563–583, Fibα580–600, Fibβ36–52, Fibβ62–81a, Fibβ62–81b], enolase [Eno5–21], and type II collagen [CitCII355–378]) in serum samples from 4,089 RA patients (1,231 Malaysian and 2,858 Swedish) and 827 healthy control subjects (249 Malaysian and 578 Swedish). The positive reaction threshold for each peptide was set separately for each population based on a specificity of 98%.

Results

Distinct differences in the frequencies of 5 ACPA fine specificities (Vim60–75, Vim2–17, Fibβ62–81b, Eno5–21, and CitCII355–378) were found between the Malaysian and Swedish RA populations, despite a nearly identical percentage of patients in each population who were positive for anti–cyclic citrullinated peptide 2 antibodies. In Malaysian RA patients compared with Swedish RA patients, the frequencies of antibodies to Vim60–75 (54% versus 44%, corrected P [P_corr] = 1.06 × 10⁻⁸) and CitCII355–378 (17% versus 13%, P_corr = 0.02) were significantly higher, while the frequencies of antibodies to Vim2–17 (25% versus 32%, P_corr = 1.91 × 10⁻⁴), Fibβ62–81b (15% versus 30%, P_corr = 2.47 × 10⁻²²), and Eno5–21 (23% versus 50%, P_corr = 3.64 × 10⁻⁵⁷) were significantly lower.

Conclusion

Serum ACPA fine specificities differ between RA patients in different populations, although the total proportions of individuals positive for ACPAs are similar. Differing patterns of ACPA fine specificity could be attributed to variations in genetic and/or environmental factors.

Update on the genetic architecture of rheumatoid arthritis

Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.

Genetic variants in ANCA-associated vasculitis: a meta-analysis

BACKGROUND

Genetic factors may influence the pathogenic pathways leading to antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). We performed a meta-analysis to determine the genetic variants most likely associated with AAV and investigated whether diagnostic and serological subtypes within AAV have distinct genetic backgrounds.

METHODS

Studies investigating the association between genetic variants and AAV in humans were searched in PubMed, EMBASE and Web of Science. All variants investigated in at least two studies were selected. Subsequently, all studies assessing these variants were included in this meta-analysis. Additionally, data on these variants from the largest genome-wide association studies in AAV were included to increase the validity of this meta-analysis.

RESULTS

The literature search yielded 5180 articles. 62 articles investigating 140 genetic variants were included, 33 of which were associated with AAV in a meta-analysis. These genetic variants were in or near the following genes: CD226, CTLA-4, FCGR2A, HLA-B, HLA-DP, HLA-DQ, HLA-DR, HSD17B8, IRF5, PTPN22, RING1/RXRB, RXRB, STAT4, SERPINA1 and TLR9. Moreover, we identified genetic distinctions between granulomatosis with polyangiitis and microscopic polyangiitis and between proteinase 3 ANCA vasculitis and myeloperoxidase ANCA vasculitis. In 76% of the genetic variants, subdivision based on ANCA serotype resulted in higher ORs than subdivision based on clinical diagnosis.

CONCLUSIONS

This meta-analysis identified 33 genetic variants associated with AAV, supporting a role for alpha-1-antitrypsin, the major histocompatibility complex system, and several distinct inflammatory processes in AAV pathogenesis. Our results indicate that subdivision of AAV based on ANCA serotype has a stronger genetic basis than subdivision based on clinical diagnosis.

Rheumatoid arthritis onset in postmenopausal women: Does the ACPA seropositive subset result from genetic effects, estrogen deficiency, skewed profile of CD4(+) T-cells, and their interactions?

Rheumatoid arthritis (RA) incidence displays a differentiated age-dependent female-to-male ratio in which women outnumber men. Evidence that the peak incidence of RA in women coincides with menopause age, suggests a potential estrogenic role to disease etiology. Estrogens exert physiologically both stimulatory and inhibitory effects on the immune system. Epidemiologic and animal model studies with estrogen deprivation or supplementation suggested estrogens as to play, mainly, a protective role in RA immunopathology. In this review, we propose that some yet unidentified disturbances associated with estrogen circulating levels, differentiated by the menopausal status, play a major role in women's RA susceptibility. We focus on the interaction between estrogen deprivation and genetic risk alleles for anti-citrullinated protein antibodies (ACPA) seropositive RA, as a major driving force for increased immune reactivity and RA susceptibility, in postmenopausal women. This opens up new fields for research concerning the association among different irregular estrogenic conditions, the cytokine milieu, and age/menopausal status bias in RA.

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.

A genome-wide association study identifies SLC8A3 as a susceptibility locus for ACPA-positive rheumatoid arthritis

OBJECTIVE

RA patients with serum ACPA have a strong and specific genetic background. The objective of the study was to identify new susceptibility genes for ACPA-positive RA using a genome-wide association approach.

METHODS

A total of 924 ACPA-positive RA patients with joint damage in hands and/or feet, and 1524 healthy controls were genotyped in 582 591 single-nucleotide polymorphisms (SNPs) in the discovery phase. In the validation phase, the most significant SNPs in the genome-wide association study representing new candidate loci for RA were tested in an independent cohort of 863 ACPA-positive patients with joint damage and 1152 healthy controls. All individuals from the discovery and validation cohorts were Caucasian and of Southern European ancestry.

RESULTS

In the discovery phase, 60 loci not previously associated with RA risk showed evidence for association at P < 5×10(-4) and were tested for replication in the validation cohort. A total of 12 loci were replicated at the nominal level (P < 0.05, same direction of effect as in the discovery phase). When combining the discovery and validation cohorts, an intronic SNP in the Solute Carrier family 8 gene (SLC8A3) was found to be associated with ACPA-positive RA at a genome-wide level of significance RA [odds ratio (95% CI): 1.42 (1.25, 1.6), Pcombined = 3.19×10(-8)].

CONCLUSIONS

SLC8A3 was identified as a new risk locus for ACPA-positive RA. This study demonstrates the advantage of analysing relevant subsets of RA patients to identify new genetic risk variants.

How does established rheumatoid arthritis develop, and are there possibilities for prevention?

Established rheumatoid arthritis (RA) is a chronic state with more or less joint damage and inflammation, which persists after a phase of early arthritis. Autoimmunity is the main determinant of persistence. Although the autoimmune response is already fully developed in the phase of early arthritis, targeted treatment within the first months produces better results than delayed treatment. Prevention of established RA currently depends on the success of remission-targeted treatment of early disease. Early recognition is aided by the new criteria for RA. Further improvement may be possible by even earlier recognition and treatment in the at-risk phase. This requires the improvement of prediction models and strategies, and more intervention studies. Such interventions should also be directed at modifiable risk factors such as smoking and obesity. The incidence of RA has declined for decades in parallel with the decrease of smoking rates; however, a recent increase has occurred that is associated with obesity.

Common Genetic Variants Influence Circulating Vitamin D Levels in Inflammatory Bowel Diseases

BACKGROUND

The accuracy and utility of electronic health record (EHR)-derived phenotypes in replicating genotype-phenotype relationships have been infrequently examined. Low circulating vitamin D levels are associated with severe outcomes in inflammatory bowel disease (IBD); however, the genetic basis for vitamin D insufficiency in this population has not been examined previously.

METHODS

We compared the accuracy of physician-assigned phenotypes in a large prospective IBD registry to that identified by an EHR algorithm incorporating codified and structured data. Genotyping for IBD risk alleles was performed on the Immunochip and a genetic risk score calculated and compared between EHR-defined patients and those in the registry. Additionally, 4 vitamin D risk alleles were genotyped and serum 25-hydroxy vitamin D [25(OH)D] levels compared across genotypes.

RESULTS

A total of 1131 patients captured by our EHR algorithm were also included in our prospective registry (656 Crohn's disease, 475 ulcerative colitis). The overall genetic risk score for Crohn's disease (P = 0.13) and ulcerative colitis (P = 0.32) was similar between EHR-defined patients and a prospective registry. Three of the 4 vitamin D risk alleles were associated with low vitamin D levels in patients with IBD and contributed an additional 3% of the variance explained. Vitamin D genetic risk score did not predict normalization of vitamin D levels.

CONCLUSIONS

EHR cohorts form valuable data sources for examining genotype-phenotype relationships. Vitamin D risk alleles explain 3% of the variance in vitamin D levels in patients with IBD.

Patient Electronic Health Records as a Means to Approach Genetic Research in Gastroenterology

Electronic health records (EHRs) are being increasingly utilized and form a unique source of extensive data gathered during routine clinical care. Through use of codified and free text concepts identified using clinical informatics tools, disease labels can be assigned with a high degree of accuracy. Analysis linking such EHR-assigned disease labels to a biospecimen repository has demonstrated that genetic associations identified in prospective cohorts can be replicated with adequate statistical power and novel phenotypic associations identified. In addition, genetic discovery research can be performed utilizing clinical, laboratory, and procedure data obtained during care. Challenges with such research include the need to tackle variability in quality and quantity of EHR data and importance of maintaining patient privacy and data security. With appropriate safeguards, this novel and emerging field of research offers considerable promise and potential to further scientific research in gastroenterology efficiently, cost-effectively, and with engagement of patients and communities.

Extracting research-quality phenotypes from electronic health records to support precision medicine

The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

OBJECTIVE

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.

MATERIALS AND METHODS

Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype.

RESULTS

The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features.

DISCUSSION

Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable.

CONCLUSION

The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.

Development of phenotype algorithms using electronic medical records and incorporating natural language processing

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

Anti-citrullinated peptide/protein antibody (ACPA)-negative RA shares a large proportion of susceptibility loci with ACPA-positive RA: a meta-analysis of genome-wide association study in a Japanese population

Introduction

Although susceptibility genes for anti-citrullinated peptide/protein antibodies (ACPA)-positive rheumatoid arthritis (RA) have been successfully discovered by genome-wide association studies (GWAS), little is known about the genetic background of ACPA-negative RA. We intended to elucidate genetic background of ACPA-negative RA.

Method

We performed a meta-analysis of GWAS comprising 670 ACPA-negative RA and 16,891 controls for 1,948,138 markers, followed by a replication study of the top 35 single nucleotide polymorphisms (SNPs) using 916 cases and 3,764 controls. Inverse-variance method was applied to assess overall effects. To assess overlap of susceptibility loci between ACPA-positive and -negative RA, odds ratios (ORs) of the 21 susceptibility markers to RA in Japanese were compared between the two subsets. In addition, SNPs were stratified by the p-values in GWAS meta-analysis for either ACPA-positive RA or ACPA-negative RA to address the question whether weakly-associated genes were also shared. The correlations between ACPA-positive RA and the subpopulations of ACPA-negative RA (rheumatoid factor (RF)-positive and RF-negative subsets) were also addressed.

Results

Rs6904716 in LEMD2 of the human leukocyte antigen (HLA) locus showed a borderline association with ACPA-negative RA (overall p = 5.7 × 10⁻⁸), followed by rs6986423 in CSMD1 (p = 2.4 × 10⁻⁶) and rs17727339 in FCRL3 (p = 1.4 × 10⁻⁵). ACPA-negative RA showed significant correlations of ORs with ACPA-positive RA for the 21 susceptibility SNPs and non-HLA SNPs with p-values far from significance. These significant correlations with ACPA-positive RA were true for ACPA-negative RF-positive and ACPA-negative RF-negative RA. On the contrary, positive correlations were not observed between the ACPA-negative two subpopulations.

Conclusion

Many of the susceptibility loci were shared between ACPA-positive and -negative RA.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-015-0623-4) contains supplementary material, which is available to authorized users.

Variation at FCGR2A and functionally related genes is associated with the response to anti-TNF therapy in rheumatoid arthritis

Objective

Anti-TNF therapies have been highly efficacious in the management of rheumatoid arthritis (RA), but 25–30% of patients do not show a significant clinical response. There is increasing evidence that genetic variation at the Fc receptor FCGR2A is associated with the response to anti-TNF therapy. We aimed to validate this genetic association in a patient cohort from the Spanish population, and also to identify new genes functionally related to FCGR2A that are also associated with anti-TNF response.

Methods

A total of 348 RA patients treated with an anti-TNF therapy were included and genotyped for FCGR2A polymorphism rs1081274. Response to therapy was determined at 12 weeks, and was tested for association globally and independently for each anti-TNF drug (infliximab, etanercept and adalimumab). Using gene expression profiles from macrophages obtained from synovial fluid of RA patients, we searched for genes highly correlated with FCGR2A expression. Tag SNPs were selected from each candidate gene and tested for association with the response to therapy.

Results

We found a significant association between FCGR2A and the response to adalimumab (P=0.022). Analyzing the subset of anti-CCP positive RA patients (78%), we also found a significant association between FCGR2A and the response to infliximab (P=0.035). DHX32 and RGS12 were the most consistently correlated genes with FCGR2A expression in RA synovial fluid macrophages (P<0.001). We found a significant association between the genetic variation at DHX32 (rs12356233, corrected P=0.019) and a nominally significant association between RGS12 and the response to adalimumab (rs4690093, uncorrected P=0.040). In the anti-CCP positive group of patients, we also found a nominally significant association between RGS12 and the response to infliximab (rs2857859, uncorrected P=0.042).

Conclusions

In the present study we have validated the FCGR2A association in an independent population, and we have identified new genes associated with the response to anti-TNF therapy in RA.

TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3x10^-21), A928V (rs35018800, OR = 0.53, P = 1.2x10^-9), and I684S (rs12720356, OR = 0.86, P = 4.6x10^-7). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, P_omnibus = 6x10^-18), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P_omnibus = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

Validation of electronic health record phenotyping of bipolar disorder cases and controls

OBJECTIVE

The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects.

METHOD

EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses was calculated against diagnoses from direct semistructured interviews of 190 patients by trained clinicians blind to EHR diagnosis.

RESULTS

The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR-classified control subject received a diagnosis of bipolar disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses.

CONCLUSIONS

Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.