IMPORTANCE: Advances have been made in identifying genetic susceptibility loci for autoimmune diseases, but evidence is needed regarding their association with prognosis and treatment response. OBJECTIVE: To assess whether specific HLA-DRB1 haplotypes associated with rheumatoid arthritis (RA) susceptibility are also associated with radiological severity, mortality, and response to tumor necrosis factor (TNF) inhibitor drugs. DESIGN, SETTING, AND PARTICIPANTS: The Norfolk Arthritis Register (NOAR; 1691 patients and 2811 radiographs; recruitment: 1989-2008; 2008 as final follow-up) was used as a discovery cohort and the Early Rheumatoid Arthritis Study (421 patients and 3758 radiographs; recruitment: 1986-1999; 2005 as final follow-up) as an independent replication cohort for studies of radiographic outcome. Mortality studies were performed in the NOAR cohort (2432 patients; recruitment: 1990-2007; 2011 as final follow-up) and studies of treatment response in the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort (1846 patients enrolled at initiation of TNF inhibitor; recruitment: 2006-2010; 2011 as final follow-up). Longitudinal statistical modeling was performed to integrate multiple radiograph records per patient over time. All patients were from the United Kingdom and had self-reported white ancestry. EXPOSURES: Sixteen HLA-DRB1 haplotypes defined by amino acids at positions 11, 71, and 74. MAIN OUTCOMES AND MEASURES: Radiological outcome using the Larsen score (range: 0 [none] to 200 [severe joint damage]) and erosions of the hands and feet on radiographs, all-cause mortality, and treatment response measured by change in Disease Activity Score based on 28 joint counts and European League Against Rheumatism (EULAR) response. RESULTS: Patients with RA and valine at position 11 of HLA-DRB1 had the strongest association with radiological damage (OR, 1.75 [95% CI, 1.51-2.05], P = 4.6E-13). By year 5, the percentages of patients with erosions of the hands and feet were 48% of noncarriers (150/314) of valine at position 11, 61% of heterozygote carriers (130/213), and 74% of homozygote carriers (43/58). Valine at position 11 also was associated with higher all-cause mortality in patients with inflammatory polyarthritis (hazard ratio, 1.16 [95% CI, 1.03-1.31], P = .01) (noncarriers: 319 deaths in 1398 patients over 17,196 person-years, mortality rate of 1.9% per year; carriers: 324 deaths in 1116 patients in 13,208 person-years, mortality rate of 2.5% per year) and with better EULAR response to TNF inhibitor therapy (OR, 1.14 [95% CI, 1.01-1.30], P = .04) (noncarriers: 78% [439/561 patients] with moderate or good EULAR response; heterozygote carriers: 81% [698/866]; and homozygote carriers: 86% [277/322]). The risk hierarchy defined by HLA-DRB1 haplotypes was correlated between disease susceptibility, severity, and mortality, but inversely correlated with TNF inhibitor treatment response. CONCLUSIONS AND RELEVANCE: Among patients with RA, the HLA-DRB1 locus, which is associated with disease susceptibility, was also associated with radiological severity, mortality, and treatment response. Replication of these findings in other cohorts is needed as a next step in evaluating the role of HLA-DRB1 haplotype analysis for management of RA.

Human leukocyte antigen (HLA) genes confer substantial risk for autoimmune diseases on a log-additive scale. Here we speculated that differences in autoantigen-binding repertoires between a heterozygote's two expressed HLA variants might result in additional non-additive risk effects. We tested the non-additive disease contributions of classical HLA alleles in patients and matched controls for five common autoimmune diseases: rheumatoid arthritis (ncases = 5,337), type 1 diabetes (T1D; ncases = 5,567), psoriasis vulgaris (ncases = 3,089), idiopathic achalasia (ncases = 727) and celiac disease (ncases = 11,115). In four of the five diseases, we observed highly significant, non-additive dominance effects (rheumatoid arthritis, P = 2.5 × 10(-12); T1D, P = 2.4 × 10(-10); psoriasis, P = 5.9 × 10(-6); celiac disease, P = 1.2 × 10(-87)). In three of these diseases, the non-additive dominance effects were explained by interactions between specific classical HLA alleles (rheumatoid arthritis, P = 1.8 × 10(-3); T1D, P = 8.6 × 10(-27); celiac disease, P = 6.0 × 10(-100)). These interactions generally increased disease risk and explained moderate but significant fractions of phenotypic variance (rheumatoid arthritis, 1.4%; T1D, 4.0%; celiac disease, 4.1%) beyond a simple additive model.

Variation in the human leukocyte antigen (HLA) genes accounts for one-half of the genetic risk in type 1 diabetes (T1D). Amino acid changes in the HLA-DR and HLA-DQ molecules mediate most of the risk, but extensive linkage disequilibrium complicates the localization of independent effects. Using 18,832 case-control samples, we localized the signal to 3 amino acid positions in HLA-DQ and HLA-DR. HLA-DQβ1 position 57 (previously known; P = 1 × 10(-1,355)) by itself explained 15.2% of the total phenotypic variance. Independent effects at HLA-DRβ1 positions 13 (P = 1 × 10(-721)) and 71 (P = 1 × 10(-95)) increased the proportion of variance explained to 26.9%. The three positions together explained 90% of the phenotypic variance in the HLA-DRB1-HLA-DQA1-HLA-DQB1 locus. Additionally, we observed significant interactions for 11 of 21 pairs of common HLA-DRB1-HLA-DQA1-HLA-DQB1 haplotypes (P = 1.6 × 10(-64)). HLA-DRβ1 positions 13 and 71 implicate the P4 pocket in the antigen-binding groove, thus pointing to another critical protein structure for T1D risk, in addition to the HLA-DQ P9 pocket.

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3' UTR regulation. We also showed that (1) 15%-36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

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.

OBJECTIVE: For decades it has been known that the HLA-DRB1 shared epitope (SE) alleles are associated with an increased risk of development and progression of rheumatoid arthritis (RA). Recently, the following variations in the peptide-binding grooves of HLA molecules that predispose to RA development have been identified: Val and Leu at HLA-DRB1 position 11, Asp at HLA-B position 9, and Phe at HLA-DPB1 position 9. This study was undertaken to investigate whether these variants are also associated with radiographic progression in RA, independent of SE and anti-citrullinated protein antibody (ACPA) status. METHODS: A total of 4,911 radiograph sets from 1,878 RA patients included in the Leiden Early Arthritis Clinic (The Netherlands), Umeå (Sweden), Hospital Clinico San Carlos-Rheumatoid Arthritis (Spain), and National Data Bank for Rheumatic Diseases (US) cohorts were studied. HLA was imputed using single-nucleotide polymorphism data from an Immunochip, and the amino acids listed above were tested in relation to radiographic progression per cohort using an additive model. Results from the 4 cohorts were combined in inverse-variance weighted meta-analyses using a fixed-effects model. Analyses were conditioned on SE and ACPA status. RESULTS: Val and Leu at HLA-DRB1 position 11 were associated with more radiographic progression (meta-analysis P = 5.11 × 10(-7)); this effect was independent of SE status (meta-analysis P = 0.022) but not independent of ACPA status. Phe at HLA-DPB1 position 9 was associated with more severe radiographic progression (meta-analysis P = 0.024), though not independent of SE status. Asp at HLA-B position 9 was not associated with radiographic progression. CONCLUSION: Val and Leu at HLA-DRB1 position 11 conferred a risk of a higher rate of radiographic progression independent of SE status but not independent of ACPA status. These findings support the relevance of these amino acids at position 11.

BACKGROUND: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. METHODS: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. RESULTS: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090). CONCLUSIONS: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.

PURPOSE: Age-related macular degeneration (AMD) has a substantial genetic risk component, as evidenced by the risk from common genetic variants uncovered in the first genome-wide association studies. More recently, it has become apparent that rare genetic variants also play an independent role in AMD risk. We sought to determine if rare variants in complement factor H (CFH) played a role in AMD risk. METHODS: We had previously collected DNA from a large population of patients with advanced age-related macular degeneration (A-AMD) and controls for targeted deep sequencing of candidate AMD risk genes. In this analysis, we tested for an increased burden of rare variants in CFH in 1665 cases and 752 controls from this cohort. RESULTS: We identified 65 missense, nonsense, or splice-site mutations with a minor allele frequency ≤ 1%. Rare variants with minor allele frequency ≤ 1% (odds ratio [OR] = 1.5, P = 4.4 × 10⁻²), 0.5% (OR = 1.6, P = 2.6 × 10⁻²), and all singletons (OR = 2.3, P = 3.3 × 10⁻²) were enriched in A-AMD cases. Moreover, we observed loss-of-function rare variants (nonsense, splice-site, and loss of a conserved cysteine) in 10 cases and serum levels of FH were decreased in all 5 with an available sample (haploinsufficiency). Further, rare variants in the major functional domains of CFH were increased in cases (OR = 3.2; P = 1.4 × 10⁻³) and the magnitude of the effect correlated with the disruptive nature of the variant, location in an active site, and inversely with minor allele frequency. CONCLUSIONS: In this large A-AMD cohort, rare variants in the CFH gene were enriched and tended to be located in functional sites or led to low serum levels. These data, combined with those indicating a similar, but even more striking, increase in rare variants found in CFI, strongly implicate complement activation in A-AMD etiopathogenesis as CFH and CFI interact to inhibit the alternative pathway.

BACKGROUND: There is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA). METHODS: A weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests. RESULTS: Individuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity. CONCLUSIONS: Our study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models.