Genetics of rheumatoid arthritis contributes to biology and drug discovery.

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Intelligent use and clinical benefits of electronic health records in rheumatoid arthritis.

In the past 10 years, electronic health records (EHRs) have had growing impact in clinical care. EHRs efficiently capture and reuse clinical information, which can directly benefit patient care by guiding treatments and providing effective reminders for best practices. The increased adoption has also lead to more complex implementations, including robust, disease-specific tools, such as for rheumatoid arthritis (RA). In addition, the data collected through normal clinical care is also used in secondary research, helping to refine patient treatment for the future. Although few studies have directly demonstrated benefits for direct clinical care of RA, the opposite is true for EHR-based research - RA has been a particularly fertile ground for clinical and genomic research that have leveraged typically advanced informatics methods to accurately define RA populations. We discuss the clinical impact of EHRs in RA treatment and their impact on secondary research, and provide recommendations for improved utility in future EHR installations.

A gene-based association method for mapping traits using reference transcriptome data.

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

Magnetic resonance imaging of the cardiac manifestations of Churg-Strauss.

Through the detection of acute inflammation, edema, and fibrosis, cardiac magnetic resonance imaging provides a complete and safe evaluation of the myocardium in Churg-Strauss disease and is a useful tool for following the disease course.

Integration of sequence data from a Consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk gene.

Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2 × 10(-6)). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted.

Applying active learning to high-throughput phenotyping algorithms for electronic health records data.

OBJECTIVES: Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms. METHODS: We integrated an uncertainty sampling AL approach with support vector machines-based phenotyping algorithms and evaluated its performance using three annotated disease cohorts including rheumatoid arthritis (RA), colorectal cancer (CRC), and venous thromboembolism (VTE). We investigated performance using two types of feature sets: unrefined features, which contained at least all clinical concepts extracted from notes and billing codes; and a smaller set of refined features selected by domain experts. The performance of the AL was compared with a passive learning (PL) approach based on random sampling. RESULTS: Our evaluation showed that AL outperformed PL on three phenotyping tasks. When unrefined features were used in the RA and CRC tasks, AL reduced the number of annotated samples required to achieve an area under the curve (AUC) score of 0.95 by 68% and 23%, respectively. AL also achieved a reduction of 68% for VTE with an optimal AUC of 0.70 using refined features. As expected, refined features improved the performance of phenotyping classifiers and required fewer annotated samples. CONCLUSIONS: This study demonstrated that AL can be useful in ML-based phenotyping methods. Moreover, AL and feature engineering based on domain knowledge could be combined to develop efficient and generalizable phenotyping methods.

Portability of an algorithm to identify rheumatoid arthritis in electronic health records.

OBJECTIVES: Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. MATERIALS AND METHODS: Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. RESULTS: Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. DISCUSSION: These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. CONCLUSION: Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.

Electronic Health Records (EHRs) provide a real-world patient cohort for clinical and genomic research. Phenotype identification using informatics algorithms has been shown to replicate known genetic associations found in clinical trials and observational cohorts. However, development of accurate phenotype identification methods can be challenging, requiring significant time and effort. We applied Support Vector Machines (SVMs) to both naïve (i.e., non-curated) and expert-defined collections of EHR features to identify Rheumatoid Arthritis cases using billing codes, medication exposures, and natural language processing-derived concepts. SVMs trained on naïve and expert-defined data outperformed an existing deterministic algorithm; the best performing naïve system had precision of 0.94 and recall of 0.87, compared to precision of 0.75 and recall of 0.51 for the deterministic algorithm. We show that with an expert defined feature set as few as 50-100 training samples are required. This study demonstrates that SVMs operating on non-curated sets of attributes can accurately identify cases from an EHR.

Milnacipran for the management of fibromyalgia syndrome.

Fibromyalgia syndrome (FMS) is a widespread pain condition associated with fatigue, cognitive dysfunction, sleep disturbance, depression, anxiety, and stiffness. Milnacipran is one of three medications currently approved by the Food and Drug Administration in the United States for the management of adult FMS patients. This review is the second in a three-part series reviewing each of the approved FMS drugs and serves as a primer on the use of milnacipran in FMS treatment including information on pharmacology, pharmacokinetics, safety and tolerability. Milnacipran is a mixed serotonin and norepinephrine reuptake inhibitor thought to improve FMS symptoms by increasing neurotransmitter levels in descending central nervous system inhibitory pathways. Milnacipran has proven efficacy in managing global FMS symptoms and pain as well as improving symptoms of fatigue and cognitive dysfunction without affecting sleep. Due to its antidepressant activity, milnacipran can also be beneficial to FMS patients with coexisting depression. However, side effects can limit milnacipran tolerability in FMS patients due to its association with headache, nausea, tachycardia, hyper- and hypotension, and increased risk for bleeding and suicidality in at-risk patients. Tolerability can be maximized by starting at low dose and slowly up-titrating if needed. As with all medications used in FMS management, milnacipran works best when used as part of an individualized treatment regimen that includes resistance and aerobic exercise, patient education and behavioral therapies.