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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). PheCAP takes structured data and narrative notes from electronic medical records and enables patients with a particular clinical phenotype to be identified.

IMPORTANCE: Severe coronavirus disease 2019 (COVID-19) can occur in younger, predominantly male, patients without preexisting medical conditions. Some individuals may have primary immunodeficiencies that predispose to severe infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: To explore the presence of genetic variants associated with primary immunodeficiencies among young patients with COVID-19. DESIGN, SETTING, AND PARTICIPANTS: Case series of pairs of brothers without medical history meeting the selection criteria of young (age T; p.[Val795Phe]). In primary peripheral blood mononuclear cells from the patients, downstream type I interferon (IFN) signaling was transcriptionally downregulated, as measured by significantly decreased mRNA expression of IRF7, IFNB1, and ISG15 on stimulation with the TLR7 agonist imiquimod as compared with family members and controls. The production of IFN-γ, a type II IFN, was decreased in patients in response to stimulation with imiquimod. CONCLUSIONS AND RELEVANCE: In this case series of 4 young male patients with severe COVID-19, rare putative loss-of-function variants of X-chromosomal TLR7 were identified that were associated with impaired type I and II IFN responses. These preliminary findings provide insights into the pathogenesis of COVID-19.

Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

Objective Genetic factors underlying susceptibility to rheumatoid arthritis (RA) in Arab populations are largely unknown. This genome-wide association study (GWAS) was undertaken to explore the generalizability of previously reported RA loci to Arab subjects and to discover new Arab-specific genetic loci. Methods The Genetics of Rheumatoid Arthritis in Some Arab States Study was designed to examine the genetics and clinical features of RA patients from Jordan, the Kingdom of Saudi Arabia, Lebanon, Qatar, and the United Arab Emirates. In total, >7 million single-nucleotide polymorphisms (SNPs) were tested for association with RA overall and with seropositive or seronegative RA in 511 RA cases and 352 healthy controls. In addition, replication of 15 signals was attempted in 283 RA cases and 221 healthy controls. A genetic risk score of 68 known RA SNPs was also examined in this study population. Results Three loci (HLA region, intergenic 5q13, and 17p13 at SMTNL2/GGT6) reached genome-wide significance in the analyses of association with RA and with seropositive RA, and for all 3 loci, evidence of independent replication was demonstrated. Consistent with the findings in European and East Asian populations, the association of RA with HLA–DRB1 amino acid position 11 conferred the strongest effect (P = 4.8 × 10−16), and a weighted genetic risk score of previously associated RA loci was found to be associated with RA (P = 3.41 × 10−5) and with seropositive RA (P = 1.48 × 10−6) in this population. In addition, 2 novel associations specific to Arab populations were found at the 5q13 and 17p13 loci. Conclusion This first RA GWAS in Arab populations confirms that established HLA-region and known RA risk alleles contribute strongly to the risk and severity of disease in some Arab groups, suggesting that the genetic architecture of RA is similar across ethnic groups. Moreover, this study identified 2 novel RA risk loci in Arabs, offering further population-specific insights into the pathophysiology of RA.

Item does not contain fulltext OBJECTIVE: In many rheumatoid arthritis (RA) patients, disease is controlled with anti-tumor necrosis factor (anti-TNF) biologic therapies. However, in a significant number of patients, the disease fails to respond to anti-TNF therapy. We undertook the present study to examine the hypothesis that rare and low-frequency genetic variants might influence response to anti-TNF treatment. METHODS: We sequenced the coding region of 750 genes in 1,094 RA patients of European ancestry who were treated with anti-TNF. After quality control, 690 genes were included in the analysis. We applied single-variant association and gene-based association tests to identify variants associated with anti-TNF treatment response. In addition, given the key mechanistic role of TNF, we performed gene set analyses of 27 TNF pathway genes. RESULTS: We identified 14,420 functional variants, of which 6,934 were predicted as nonsynonymous 2,136 of which were further predicted to be "damaging." Despite the fact that the study was well powered, no single variant or gene showed study-wide significant association with change in the outcome measures disease activity or European League Against Rheumatism response. Intriguingly, we observed 3 genes, of 27 with nominal signals of association (P < 0.05), that were involved in the TNF signaling pathway. However, when we performed a rigorous gene set enrichment analysis based on association P value ranking, we observed no evidence of enrichment of association at genes involved in the TNF pathway (Penrichment = 0.15, based on phenotype permutations). CONCLUSION: Our findings suggest that rare and low-frequency protein-coding variants in TNF signaling pathway genes or other genes do not contribute substantially to anti-TNF treatment response in patients with RA.

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection(1). Drug targets with genetic support are more likely to be therapeutically valid(2,3). The translational use of genome-scale data such as from genome-wide association studies (GWAS) for drug target discovery in complex diseases remains challenging(4–6). Here we show that integration of functional genomic and immune-related annotations together with knowledge of network connectivity maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (“Priority index”, Pi) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets, and determines target-level trait relationships. Pi is an open access, scalable system accelerating early-stage drug target selection for immune-mediated disease.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene–gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large “real-world data” cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P-6; out of 72 novel associations with FDRPNPLA3; or asthma for rs1990760 (p.T946A) in IFIH1. We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.One Sentence SummaryMatching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P, Testing the association between genetic variants and a range of phenotypes can assist drug development. Here, in a phenome-wide association study in up to 697,815 individuals, Diogo et al. identify genotype–phenotype associations predicting efficacy, alternative indications or adverse drug effects.

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Objective To evaluate HLA-DRB1 genetic risk of rheumatoid arthritis (RA) in African Americans by 3 validated allele classification systems and by amino acid position and residue, and to compare genetic risk between African American and European ancestries. Methods Four-digit HLA-DRB1 genotyping was performed on 561 autoantibody-positive African American cases and 776 African American controls. Association analysis was performed on Tezenas du Montcel (TdM), de Vries (DV), and Mattey classification system alleles and separately by amino acid position and individual residues. Results TdM S2 and S3P alleles were associated with RA (odds ratio [95% confidence interval] 2.8 [2.0-3.9] and 2.1 [1.7-2.7], respectively). The DV (P = 3.2 × 10(-12)) and Mattey (P = 6.5 × 10(-13)) system alleles were both protective in African Americans. Amino acid position 11 (permutation P Conclusion With some exceptions, the genetic risk conferred by HLA-DRB1 in African Americans is similar to that in individuals of European ancestry at multiple levels: classification system (e.g., TdM), amino acid position (e.g., 11), and residue (Val11). Unlike that reported for individuals of European ancestry, amino acid position 57 was associated with RA in African Americans, but positions 71 and 74 were not. Asp11 (odds ratio 1 in European ancestry) corresponds to the 4-digit classical allele *09:01, which is also a risk allele for RA in Koreans.

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case-control status, and this estimated case-control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies.

Peripheral neuropathy is a common dose-limiting toxicity for patients treated with paclitaxel. For most individuals, there are no known risk factors that predispose patients to the adverse event, and pathogenesis for paclitaxel-induced peripheral neuropathy is unknown. Determining whether there is a heritable component to paclitaxel-induced peripheral neuropathy would be valuable in guiding clinical decisions and may provide insight into treatment of and mechanisms for the toxicity. Using genotype and patient information from the paclitaxel arm of CALGB 40101 (Alliance), a phase III clinical trial evaluating adjuvant therapies for breast cancer in women, we estimated the variance in maximum grade and dose at first instance of sensory peripheral neuropathy. Our results suggest that paclitaxel-induced neuropathy has a heritable component, driven in part by genes involved in axon outgrowth. Disruption of axon outgrowth may be one of the mechanisms by which paclitaxel treatment results in sensory peripheral neuropathy in susceptible patients.

The objective of this study was to study genetic factors that influence quantitative anticyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. We carried out a genome-wide association study (GWAS) meta-analysis using 1975 anti-CCP+ RA patients from three large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC) and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. GWAS-meta-analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a P-value of 2 × 10(-11) for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5 × 10(-8), and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r(2) in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a P-value of 3 × 10(-7). None of the known RA risk alleles (∼52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. In summary, anti-CCP level is a heritable trait, and HLA-DR3 and GP2 are associated with lower anti-CCP levels.

There is limited information on the epidemiology and treatment patterns of rheumatoid arthritis (RA) across the Arab region. We aim in this study to describe the demographic characteristics, clinical profile, and treatment patterns of patients of Arab ancestry with RA.This is a cross sectional study of 895 patients with established rheumatoid arthritis enrolled from five sites (Jordan, Lebanon, Qatar, Kingdom of Saudi Arabia (KSA), and United Arab Emirates). Demographic characteristics, clinical profile, and treatment patterns are compared between the five countries.The majority of our patients are women, have an average disease duration of 10 years, are married and non-smokers, with completed secondary education. We report a high (80%) ever-use of methotrexate (MTX) and steroids among our RA population, while the ever-use of disease modifying anti-rheumatic drugs (DMARDs) and TNF-inhibitors average around 67% and 33%, respectively. There are variations in RA treatment use between the five country sites. Highest utilization of steroids is identified in Jordan and KSA (p-value0.001), while the highest ever-use of TNF-inhibitors is reported in KSA (p-value0.001).Disparities in usage of RA treatments among Arab patients are noted across the five countries. National gross domestic product (GDP), as well as some other unique features in each country likely affect these. Developing treatment guidelines specific to this region could contribute in delivering standardized therapies to RA patients.

To report on the accuracy of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for identifying patients with polymyalgia rheumatica (PMR) and concurrent noninfectious inflammatory ocular conditions in a large healthcare organization database.Queries for patients with PMR and uveitis or scleritis were executed in two general teaching hospitals' databases. Patients with ocular infections or other rheumatologic conditions were excluded. Patients with PMR and ocular inflammation were identified, and medical records were reviewed to confirm accuracy.The query identified 10,697 patients with the ICD-9-CM code for PMR and 4154 patients with the codes for noninfectious inflammatory ocular conditions. The number of patients with both PMR and noninfectious uveitis or scleritis by ICD-9-CM codes was 66. On detailed review of the charts of these 66 patients, 31 (47%) had a clinical diagnosis of PMR, 43 (65%) had noninfectious uveitis or scleritis, and only 20 (30%) had PMR with concurrent noninfectious uveitis or scleritis confirmed based on clinical notes.While the use of ICD-9-CM codes has been validated for medical research of common diseases, our results suggest that ICD-9-CM codes may be of limited value for epidemiological investigations of diseases which can be more difficult to diagnose. The ICD-9-CM codes for rarer diseases (PMR, uveitis and scleritis) did not reflect the true clinical problem in a large proportion of our patients. This is particularly true when coding is performed by physicians outside the area of specialty of the diagnosis.

Objective A recent high-density fine-mapping (ImmunoChip) study of genetic associations in rheumatoid arthritis (RA) identified 14 risk loci with validated genome-wide significance, as well as a number of loci showing associations suggestive of significance (P = 5 × 10−5 < 5 × 10−8), but these have yet to be replicated. The aim of this study was to determine whether these potentially significant loci are involved in the pathogenesis of RA, and to explore whether any of the loci are associated with a specific RA serotype. Methods A total of 16 single-nucleotide polymorphisms (SNPs) were selected for genotyping and association analyses in 2 independent validation cohorts, comprising 6,106 RA cases and 4,290 controls. A meta-analysis of the data from the original ImmunoChip discovery cohort and from both validation cohorts was carried out, for a combined total of 17,581 RA cases and 20,160 controls. In addition, stratified analysis of patient subsets, defined according to their anti–cyclic citrullinated peptide (anti-CCP) antibody status, was performed. Results A significant association with RA risk (P < 0.05) was replicated for 6 of the SNPs assessed in the validation cohorts. All SNPs in the validation study had odds ratios (ORs) for RA susceptibility in the same direction as those in the ImmunoChip discovery study. One SNP, rs72928038, mapping to an intron of BACH2, achieved genome-wide significance in the meta-analysis (P = 1.2 × 10−8, OR 1.12), and a second SNP, rs911263, mapping to an intron of RAD51B, was significantly associated in the anti-CCP–positive RA subgroup (P = 4 × 10−8, OR 0.89), confirming that both are RA susceptibility loci. Conclusion This study provides robust evidence for an association of RA susceptibility with genes involved in B cell differentiation (BACH2) and DNA repair (RAD51B). The finding that the RAD51B gene exhibited different associations based on serologic subtype adds to the expanding knowledge base in defining subgroups of RA.

Objective Differences in lipid levels associated with cardiovascular (CV) risk between rheumatoid arthritis (RA) patients and the general population remain unclear. Determining these differences is important in understanding the role of lipids in CV risk in RA. Methods We studied 2,005 RA subjects from 2 large academic medical centers. We extracted electronic medical record data on the first low-density lipoprotein (LDL) measurement, and total cholesterol and high-density lipoprotein (HDL) measurements within 1 year of the LDL measurement. Subjects with an electronic statin prescription prior to the first LDL measurement were excluded. We compared lipid levels in RA patients to recently published levels from the general US population using the t-test and stratifying by published parameters, i.e., 2007–2010, and women. We determined lipid trends using separate linear regression models for total cholesterol, LDL cholesterol, and HDL cholesterol, testing the association between year of measurement (1989–2010) and lipid level, adjusted by age and sex. Lipid trends in RA were qualitatively compared to the published general population trends. Results Women with RA had a significantly lower total cholesterol (186 versus 200 mg/dl; P = 0.002) and LDL cholesterol (105 versus 118 mg/dl; P = 0.001) compared to the general population (2007–2010). HDL cholesterol was not significantly different in the 2 groups. In the RA cohort, total cholesterol and LDL cholesterol significantly decreased each year, while HDL cholesterol increased (all with P < 0.0001), consistent with overall trends observed in a previous study. Conclusion RA patients appear to have an overall lower total cholesterol and LDL cholesterol than the general population despite the general overall risk of CV disease in RA from observational studies.

Autoimmune disease results from a loss of tolerance to self-antigens in genetically susceptible individuals. Completely understanding this process requires that targeted antigens be identified, and so a number of techniques have been developed to determine immune receptor specificities. We previously reported the construction of a phage-displayed synthetic human peptidome and a proof-of-principle analysis of antibodies from three patients with neurological autoimmunity. Here we present data from a large-scale screen of 298 independent antibody repertoires, including those from 73 healthy sera, using phage immunoprecipitation sequencing. The resulting database of peptide-antibody interactions characterizes each individual’s unique autoantibody fingerprint, and includes specificities found to occur frequently in the general population as well as those associated with disease. Screening type 1 diabetes (T1D) patients revealed a prematurely polyautoreactive phenotype compared with their matched controls. A collection of cerebrospinal fluids and sera from 63 multiple sclerosis patients uncovered novel, as well as previously reported antibody-peptide interactions. Finally, a screen of synovial fluids and sera from 64 rheumatoid arthritis patients revealed novel disease-associated antibody specificities that were independent of seropositivity status. This work demonstrates the utility of performing PhIP-Seq screens on large numbers of individuals and is another step toward defining the full complement of autoimmunoreactivities in health and disease.

Objectives: While genetic determinants of low density lipoprotein (LDL) cholesterol levels are well characterised in the general population, they are understudied in rheumatoid arthritis (RA). Our objective was to determine the association of established LDL and RA genetic alleles with LDL levels in RA cases compared with non-RA controls. Methods: Using data from electronic medical records, we linked validated RA cases and non-RA controls to discarded blood samples. For each individual, we extracted data on: first LDL measurement, age, gender and year of LDL measurement. We genotyped subjects for 11 LDL and 44 non-HLA RA alleles, and calculated RA and LDL genetic risk scores (GRS). We tested the association between each GRS and LDL level using multivariate linear regression models adjusted for age, gender, year of LDL measurement and RA status. Results: Among 567 RA cases and 979 controls, 80% were female and mean age at the first LDL measurement was 55 years. RA cases had significantly lower mean LDL levels than controls (117.2 vs 125.6 mg/dl, respectively, p, National Institutes of Health (U.S.) (Grant U54-LM008748)

OBJECTIVES: Psychiatric comorbidity is common in Crohn's disease (CD) and ulcerative colitis (UC). Inflammatory bowel disease (IBD)-related surgery or hospitalizations represent major events in the natural history of the disease. The objective of this study is to examine whether there is a difference in the risk of psychiatric comorbidity following surgery in CD and UC. METHODS: We used a multi-institution cohort of IBD patients without a diagnosis code for anxiety or depression preceding their IBD-related surgery or hospitalization. Demographic-, disease-, and treatment-related variables were retrieved. Multivariate logistic regression analysis was performed to individually identify risk factors for depression and anxiety. RESULTS: Our study included a total of 707 CD and 530 UC patients who underwent bowel resection surgery and did not have depression before surgery. The risk of depression 5 years after surgery was 16% and 11% in CD and UC patients, respectively. We found no difference in the risk of depression following surgery in the CD and UC patients (adjusted odds ratio, 1.11; 95% confidence interval, 0.84–1.47). Female gender, comorbidity, immunosuppressant use, perianal disease, stoma surgery, and early surgery within 3 years of care predicted depression after CD surgery; only the female gender and comorbidity predicted depression in UC patients. Only 12% of the CD cohort had ≥4 risk factors for depression, but among them nearly 44% subsequently received a diagnosis code for depression. CONCLUSIONS: IBD-related surgery or hospitalization is associated with a significant risk for depression and anxiety, with a similar magnitude of risk in both diseases., National Institutes of Health (U.S.) (U54-LM008748)

Contains fulltext : 118482.pdf (Publisher’s version ) (Open Access) BACKGROUND: Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach. METHODS: We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p

Understanding the implications of genome-wide association studies (GWAS) for disease biology requires both identification of causal variants and definition of how these variants alter gene function. The non-coding triallelic dinucleotide polymorphism CCR6DNP is associated with risk for rheumatoid arthritis, and is considered likely causal because allelic variation correlates with expression of the chemokine receptor CCR6. Using transcription activator-like effector nuclease (TALEN) gene editing, we confirmed that CCR6DNP regulates CCR6. To identify the associated transcription factor, we applied a novel assay, Flanking Restriction Enhanced Pulldown (FREP), to identify specific association of poly (ADP-ribose) polymerase 1 (PARP-1) with CCR6DNP consistent with the established allelic risk hierarchy. Correspondingly, manipulation of PARP-1 expression or activity impaired CCR6 expression in several lineages. These findings show that CCR6DNP is a causal variant through which PARP-1 regulates CCR6, and introduce a highly efficient approach to interrogate non-coding genetic polymorphisms associated with human disease., Author Summary Genome-wide association studies (GWAS) identify loci associated with human disease risk, but bridging the gap between locus and mechanism has proven particularly difficult in cases where associated variants do not alter coding. We aimed to develop a generalizable approach to this problem. Previously, a dual nucleotide polymorphism within the first intron of CCR6 (termed the CCR6DNP) had been associated with risk for rheumatoid arthritis, but the pathway by which this variant altered gene expression could not be determined. Here, we employed sequence perturbation to confirm a regulatory role for the CCR6DNP. Next, using a new technique termed Flanking Restriction Enhanced Pulldown (FREP), we identified PARP-1 as the protein that regulates CCR6 expression through allelic association with the CCR6DNP, a finding confirmed by chromatin immunoprecipitation and functional assays. These findings reveal an unexpected regulatory pathway for CCR6 implicated in rheumatoid arthritis and other disease by human genetics, and more generally introduce a novel approach to identifying regulatory protein-DNA interactions.

Our modern health care system demands therapeutic interventions that improve the lives of patients. Unfortunately, decreased productivity in therapeutics research and development (R&D) has driven drug costs up while delivering insufficient value to patients. Here, I discuss a model of translational medicine that connects four components of the early R&D pipeline-causal human biology, therapeutic modality, biomarkers of target modulation, and proof-of-concept clinical trials. Whereas the individual components of this model are not new, technological advances and a disciplined approach to integrating all four areas offer hope for improving R&D productivity.

Segmental duplications (SDs) comprise about 5% of the human genome and are enriched for immune genes. SD loci often show copy numbers variations (CNV), which are difficult to tag with genotyping methods. CNV in the Fcγ receptor region (FCGR) has been suggested to be associated with rheumatic diseases. The objective of this study was to delineate association of FCGR-CNV with rheumatoid arthritis (RA), coeliac disease and Inflammatory bowel disease incidence. We developed a method to accurately quantify CNV in SD loci based on the intensity values from the Immunochip platform and applied it to the FCGR locus. We determined the method's validity using three independent assays: segregation analysis in families, arrayCGH, and whole genome sequencing. Our data showed the presence of two separate CNVs in the FCGR locus. The first region encodes FCGR2A, FCGR3A and part of FCGR2C gene, the second encodes another part of FCGR2C, FCGR3B and FCGR2B. Analysis of CNV status in 4578 individuals with RA and 5457 controls indicated association of duplications in the FCGR3B gene in antibody-negative RA (P=0.002, OR=1.43). Deletion in FCGR3B was associated with increased risk of antibody-positive RA, consistently with previous reports (P=0.023, OR=1.23). A clear genotype-phenotype relationship was observed: CNV polymorphisms of the FCGR3A gene correlated to CD16A expression (encoded by FCGR3A) on CD8 T-cells. In conclusion, our method allows determining the CNV status of the FCGR locus, we identified association of CNV in FCGR3B to RA and showed a functional relationship between CNV in the FCGR3A gene and CD16A expression.European Journal of Human Genetics advance online publication, 13 May 2015; doi:10.1038/ejhg.2015.95.

The discovery of the Janus kinase (JAK)-signal transducer and activator of transcripton (STAT) signaling pathway, a landmark in cell biology, provided a simple mechanism for gene regulation that dramatically advanced our understanding of the action of hormones, interferons, colony-stimulating factors, and interleukins. As we learn more about the complexities of immune responses, new insights into the functions of this pathway continue to be revealed, aided by technology that permits genome-wide views. As we celebrate the 20(th) anniversary of the discovery of this paradigm in cell signaling, it is particularly edifying to see how this knowledge has rapidly been translated to human immune disease. Not only have genome-wide association studies demonstrated that this pathway is highly relevant to human autoimmunity, but targeting JAKs is now a reality in immune-mediated disease.

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10−8) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10−12] and rs2872507 at the 17q12 locus [p = 1.7 × 10−9]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.

Individuals who lack a functional copy of a gene — gene knockouts — can reveal the gene's role. Most knockout research has used model organisms, but now a comprehensive catalogue of human knockouts is in sight. See Letter p.235 Understanding the function of every human gene is one of the major goals in biomedicine and analysing phenotypes in individuals with loss of function mutations in a particular gene is one way to gain such insight. Sekar Kathiresan and colleagues systematically examined predicted loss-of-function mutations from sequencing data, making preliminary efforts to investigate their functional relevance through phenotypic association. They sequenced the exomes of 10,503 individuals living in Pakistan and participating in the PROMIS study. They identified 49,138 rare predicted loss-of-function mutations that are estimated to knock out 1,317 genes, each in at least one participant, and tested for association with a panel of 201 traits measured in blood samples. The authors demonstrate the usefulness of a reverse-genetics approach, which involves recruiting participants by genotype, and discuss a framework for a human knockout project.

Background Robust replication is a sine qua non for the rigorous documentation of proposed associations in the genome-wide association (GWA) setting. Currently, associations of common variants reaching P ≤ 5 × 10(-8) are considered replicated. However, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. Methods We defined as 'borderline' associations those with P > 5 × 10(-8) and P ≤ 1 × 10(-7). The eligible associations were retrieved using the 'Catalog of Published Genome-Wide Association Studies'. For each association we assessed whether it reached P ≤ 5 × 10(-8) with inclusion of additional data from subsequent GWA studies. Results Thirty-four eligible genotype-phenotype associations were evaluated with data and clarifications contributed from diverse investigators. Replication data from subsequent GWA studies could be obtained for 26 of them. Of those, 19 associations (73%) reached P ≤ 5 × 10(-8) for the same or a related trait implicating either the exact same allele or one in very high linkage disequilibrium and 17 reached P 10(-6) [corresponding false-discovery rate 19% (95% CI 7-39%)]. Conclusion A substantial proportion, but not all, of the associations with borderline genome-wide significance represent replicable, possibly genuine associations. Our empirical evaluation suggests a possible relaxation in the current GWS threshold.

A common allele at the TAGAP gene locus demonstrates a suggestive, but not conclusive association with risk of rheumatoid arthritis (RA). To fine map the locus, we conducted comprehensive imputation of CEU HapMap single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS) of 5500 RA cases and 22 621 controls (all of European ancestry). After controlling for population stratification with principal components analysis, the strongest signal of association was to an imputed SNP, rs212389 (P 3.9 x 10(-8), odds ratio 0.87). This SNP remained highly significant upon conditioning on the previous RA risk variant (rs394581, P = 2.2 x 10(-5)) or on a SNP previously associated with celiac disease and type I diabetes (rs1738074, P = 1.7 x 10(-4)). Our study has refined the TAGAP signal of association to a single haplotype in RA, and in doing so provides conclusive statistical evidence that the TAGAP locus is associated with RA risk. Our study also underscores the utility of comprehensive imputation in large GWAS data sets to fine map disease risk alleles. Genes and Immunity (2011) 12, 314-318; doi:10.1038/gene.2011.8; published online 10 March 2011

Objective Large-scale genetic association studies have identified >20 rheumatoid arthritis (RA) risk alleles among individuals of European ancestry. The influence of these risk alleles has not been comprehensively studied in African Americans. We therefore sought to examine whether these validated RA risk alleles are associated with RA risk in an African American population. Methods Twenty-seven candidate single-nucleotide polymorphisms (SNPs) were genotyped in 556 autoantibody-positive African Americans with RA and 791 healthy African American control subjects. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for each SNP were compared with previously published ORs for RA patients of European ancestry. We then calculated a composite genetic risk score (GRS) for each individual based on the sum of all risk alleles. Results Overlap of the ORs and 95% CIs between the European and African American populations was observed for 24 of the 27 candidate SNPs. Conversely, 3 of the 27 SNPs (CCR6 rs3093023, TAGAP rs394581, and TNFAIP3 rs6920220) demonstrated ORs in the opposite direction from those reported for RA patients of European ancestry. The GRS analysis indicated a small but highly significant probability that African American patients relative to control subjects were enriched for the risk alleles validated in European RA patients (P = 0.00005). Conclusion The majority of RA risk alleles previously validated for RA patients of European ancestry showed similar ORs in our population of African Americans with RA. Furthermore, the aggregate GRS supports the hypothesis that these SNPs are risk alleles for RA in the African American population. Future large-scale genetic studies are needed to validate these risk alleles and identify novel RA risk alleles in African Americans.

We investigated the prevalence of xenotropic murine leukemia virus-related virus (XMRV) among 293 participants seen at academic hospitals in Boston, Massachusetts. Participants were recruited from the following 5 groups of patients: chronic fatigue syndrome (n = 32), human immunodeficiency virus infection (n = 43), rheumatoid arthritis (n = 97), hematopoietic stem-cell or solid organ transplant (n = 26), or a general cohort of patients presenting for medical care (n = 95). XMRV DNA was not detected in any participant samples. We found no association between XMRV and patients with chronic fatigue syndrome or chronic immunomodulatory conditions.

The objective of this review is to report on the progress of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) Registry data collection and summarize previous research in understanding therapeutic response to DMARDs using clinical and genetic data. The BRASS Registry, established in 2003, is a large, single-centre, prospective and observational cohort of 1100 RA patients. Patients with either new-onset or established RA disease are recruited from the practices of rheumatologists. Annual visits collect information on demographics, 28-joint DAS-CRP3 (DAS-28-CRP3), medication use, comorbidities and functional status (Modified Health Assessment Questionnaire, Short Form Health Survey 12). Two published studies have utilized BRASS to examine genetic predictors of treatment response. In a cross-sectional study, examining the association between candidate single nucleotide polymorphisms (SNPs) and disease activity in a subset of 120 RA patients on MTX monotherapy, the minor allele of ATIC rs4673993 was associated with low disease activity (P=0.01, DAS-28-CRP3≤3.2). In an international collaboration, 55 BRASS patients receiving anti-TNF therapy were genotyped for 31 SNPs associated with the risk of RA. With our collaborators, we discovered an SNP at the protein tyrosine phosphatase, receptor type, C (PTPRC) gene locus that was associated with EULAR 'good response'. With accurate data collection and the capacity to run genome-wide association studies and SNP analyses, the BRASS Registry has the ability to determine the contribution of genetic variants to disease onset and to assess their usefulness as biomarkers for treatment response and drug toxicity.

Objective: Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone. Methods: Subjects with ≥1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti–cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an “RA Mart” (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms. Results: A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions). Conclusion: We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone., National Library of Medicine (U.S.) (Award U54LM008748), National Institutes of Health (U.S.). i2b2 (Informatics for Integrating Biology and the Bedside) (Grant U54-LM008748)

BackgroundRecent discoveries of risk alleles have made it possible to define genetic risk profiles for patients with rheumatoid arthritis (RA). This study examined whether a cumulative score based on 22 validated genetic risk alleles for seropositive RA would identify high-risk, asymptomatic individuals who might benefit from preventive interventions.MethodsEight human leucocyte antigen (HLA) alleles and 14 single-nucleotide polymorphisms representing 13 validated RA risk loci were genotyped among 289 white seropositive cases and 481 controls from the US Nurses' Health Studies (NHS) and 629 white cyclic-citrullinated peptide antibody-positive cases and 623 controls from the Swedish Epidemiologic Investigation of Rheumatoid Arthritis (EIRA). A weighted genetic risk score (GRS) was created, in which the weight for each risk allele is the log of the published odds ratio (OR). Logistic regression was used to study associations with incident RA. Area under the curve (AUC) statistics were compared from a clinical-only model and clinical plus genetic model in each cohort.ResultsPatients with GRS >1.25 SD of the mean had a significantly higher OR of seropositive RA in both NHS (OR=2.9, 95%CI 1.8 to 4.6) and EIRA (OR 3.4, 95% CI 2.3 to 5.0) referent to the population average. In NHS, the AUC for a clinical model was 0.57 and for a clinical plus genetic model was 0.66, and in EIRA was 0.63 and 0.75, respectively.ConclusionThe combination of 22 risk alleles into a weighted GRS significantly stratifies individuals for RA risk beyond clinical risk factors alone. Given the low incidence of RA, the clinical utility of a weighted GRS is limited in the general population.

Objective. Previous studies have demonstrated that the PRL 1149 T (minor) allele decreases prolactin expression and may be associated with autoimmune disease. The aim of this study was to determine the role of the PRL 1149 G/T polymorphism (rs1341239) in rheumatoid arthritis (RA) susceptibility. Methods. We examined the association between PRL 1149 G/T and RA risk in 4 separate study populations, consisting of a total of 3,405 RA cases and 4,111 controls of self-reported white European ancestry. Samples were genotyped using 1 of 3 genotyping platforms, and strict quality control metrics were applied. We tested for association using a 2-tailed CochranMantel-Haenszel additive, fixed-effects model. Results. In the individual populations, odds ratios (ORs) for an association between PRL 1149 T and RA risk ranged from 0.80 to 0.97. In a joint meta-analysis across all 4 populations, the OR for an association between PRL 1149 T and RA risk was 0.90 (95% confidence interval 0.84–0.96, P 0.001). Conclusion. Our findings indicate a possible association between the PRL 1149 T allele and decreased RA risk. The effect size is small but similar to ORs for other genetic polymorphisms associated with complex traits, including RA.

We carried out a genome-wide association study of genetic predictors of anti-cyclic citrullinated peptide antibody (anti-CCP) level in 531 self-reported non-Hispanic Caucasian Rheumatoid Arthritis (RA) patients enrolled in the Brigham Rheumatoid Arthritis Sequential Study (BRASS). For replication, we then analyzed 289 single nucleotide polymorphisms (SNPs) with P0.001 in BRASS in an independent population of 849 RA patients from the North American Rheumatoid Arthritis Consortium (NARAC). BRASS and NARAC samples were genotyped using the Affymetrix 100K and Illumina 550K platforms respectively. Association between SNPs and anti-CCP titer was tested using general linear models. The five most significant SNPs from BRASS all were within the major histocompatibility complex (MHC) region (Por = 3.5 x 10(-6)). After controlling for the human leukocyte antigen shared epitope (HLA-SE), the top SNPs still yielded P values0.0002. In NARAC, a single SNP from the MHC region near BTNL2 and HLA-DRA, rs1980493 (r(2) = 0.85 with the top five SNPs from BRASS), was associated significantly with CCP titer (P = 6.1 x 10(-5)) even after adjustment for the HLA-SE (P = 0.0002). The top SNPs found in BRASS and NARAC had r(2) = 0.46 and 0.64, respectively, to HLA-DRB1 DR3 alleles. These results confirm that the most significant genome region affecting anti-CCP titers in RA is the MHC region. We identified a SNP in moderate linkage disequilibrium (LD) with HLA-DR3, which may influence anti-CCP titer independently of the HLA-SE.