High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis

A regulatory variant in CCR6 is associated with susceptibility to antitopoisomerase-positive systemic sclerosis

OBJECTIVE

Recognition of the well-known pleiotropism of autoimmune genes supports the concept of a shared pathogenesis across autoimmune diseases such as rheumatoid arthritis (RA) and systemic sclerosis (SSc). Studies have reproducibly demonstrated an association between susceptibility to RA and polymorphisms of the CCR6 gene, a surface marker for Th17 cells, and the causal variant was recently identified. The present study was thus undertaken to investigate whether CCR6 polymorphisms could also be associated with susceptibility to SSc.

METHODS

Twelve tag single-nucleotide polymorphisms (SNPs) of CCR6, including the known RA-associated SNP rs3093023, were genotyped in a total of 2,411 SSc patients and 7,084 healthy individuals from 3 European populations (France, Italy, and Germany). Meta-analyses of the data were performed to assess whether an association exists between CCR6 polymorphisms and susceptibility to SSc or its main subtypes. Direct sequencing of DNA was performed to ascertain whether the functional dinucleotide polymorphism of CCR6 previously identified in RA (CCR6DNP) was also present in SSc.

RESULTS

Combined analyses revealed an association between the rs10946216 SNP and SSc susceptibility (odds ratio [OR] 1.13, 95% confidence interval [95% CI] 1.05-1.21, adjusted P [P(adj)] = 0.026). The rs3093023 A allele and rs10946216 T allele were in high linkage disequilibrium, and both were found to confer disease susceptibility in the antitopoisomerase-positive subset of SSc patients (OR 1.27, 95% CI 1.13-1.42, P(adj) = 1.5 × 10(-3) and OR 1.32, 95% CI 1.17-1.48, P(adj) = 9.0 × 10(-5), respectively, relative to healthy controls). Direct sequencing of the DNA of 78 individuals supported the hypothesis that the regulatory dinucleotide CCR6DNP could be the causal variant in SSc.

CONCLUSION

The results of this study establish CCR6 as a new susceptibility factor for antitopoisomerase-positive SSc, as demonstrated in 3 European Caucasian populations, confirming the notion that SSc and RA could conceivably share autoimmune risk alleles. The results also suggest a potential role of the interleukin-17 pathway in SSc.

Identification of BACH2 and RAD51B as rheumatoid arthritis susceptibility loci in a meta-analysis of genome-wide data

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.

ACPA-positive and ACPA-negative rheumatoid arthritis differ in their requirements for combination DMARDs and corticosteroids: secondary analysis of a randomized controlled trial

INTRODUCTION

UK guidelines recommend that all early active rheumatoid arthritis (RA) patients are offered combination disease-modifying antirheumatic drugs (DMARDs) and short-term corticosteroids. Anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative RA may differ in their treatment responses. We used data from a randomized controlled trial - the Combination Anti-Rheumatic Drugs in Early RA (CARDERA) trial - to examine whether responses to intensive combination treatments in early RA differ by ACPA status.

METHODS

The CARDERA trial randomized 467 early active RA patients to receive: (1) methotrexate, (2) methotrexate/ciclosporin, (3) methotrexate/prednisolone or (4) methotrexate/ciclosporin/prednisolone in a factorial-design. Patients were assessed every six months for two years. In this analysis we evaluated 431 patients with available ACPA status. To minimize multiple testing we used a mixed-effects repeated measures ANOVA model to test for an interaction between ACPA and treatment on mean changes from baseline for each outcome (Larsen, disease activity scores on a 28-joint count (DAS28), Health Assessment Questionnaire (HAQ), EuroQol, SF-36 physical component summary (PCS) and mental component summary (MCS) scores). When a significant interaction was present, mean changes in outcomes were compared by treatment group at each time point using t-tests stratified by ACPA status. Odds ratios (ORs) for the onset of new erosions with treatment were calculated stratified by ACPA.

RESULTS

ACPA status influenced the need for combination treatments to reduce radiological progression. ACPA-positive patients had significant reductions in Larsen score progression with all treatments. ACPA-positive patients receiving triple therapy had the greatest benefits: two-year mean Larsen score increases comprised 3.66 (95% confidence interval (CI) 2.27 to 5.05) with triple therapy and 9.58 (95% CI 6.76 to 12.39) with monotherapy; OR for new erosions with triple therapy versus monotherapy was 0.32 (95% CI 0.14 to 0.72; P = 0.003). ACPA-negative patients had minimal radiological progression irrespective of treatment. Corticosteroid's impact on improving DAS28/PCS scores was confined to ACPA-positive RA.

CONCLUSIONS

ACPA status influences the need for combination DMARDs and high-dose tapering corticosteroids in early RA. In CARDERA, combination therapy was only required to prevent radiological progression in ACPA-positive patients; corticosteroids only provided significant disease activity and physical health improvements in ACPA-positive disease. This suggests ACPA is an important biomarker for guiding treatment decisions in early RA.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN32484878.

Familial risks and heritability of rheumatoid arthritis: role of rheumatoid factor/anti-citrullinated protein antibody status, number and type of affected relatives, sex, and age

OBJECTIVE

To estimate familial aggregation of rheumatoid arthritis (RA) in 3 large population-representative samples and to test if familial aggregation is affected by rheumatoid factor (RF)/anti-citrullinated protein antibody (ACPA) status, type of relative, sex, and age at onset of RA.

METHODS

A register-based nested case-control study was performed in the Swedish total population. Data on patients with RA were ascertained through the nationwide Swedish Patient Register (n = 88,639), the clinical Swedish Rheumatology Quality Register (n = 11,519), and the Epidemiological Investigation of Rheumatoid Arthritis case-control study (n = 2,871). Data on first- and second-degree relatives were obtained through the Swedish Multigeneration Register. Familial risks were calculated using conditional logistic regression.

RESULTS

Consistent across data sources, the familial odds ratio for RA was ∼3 in first-degree relatives of RA patients and 2 in second-degree relatives. Familial risks were similar among siblings, parents, and offspring. Familial aggregation was not modified by sex, but was higher in RA patients with early-onset disease and in RF/ACPA-positive RA patients. The observed familial risks were consistent with a heritability of ∼50% for ACPA-positive RA and ∼20% for ACPA-negative RA.

CONCLUSION

The pattern of risks suggests that familial factors influence RA in men and women equally and that these factors are of less importance for late-onset RA. Familial factors are more important for seropositive RA, but there is significant familial overlap between seropositive RA and seronegative RA. Even if the familial risk is assumed to be completely due to genetics, the observed risks suggest that heritability of RA is lower than previously reported, in particular for ACPA-negative RA.

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci.

The influence of polygenic risk scores on heritability of anti-CCP level in RA

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.

The role of histone deacetylases in rheumatoid arthritis fibroblast-like synoviocytes

RA (rheumatoid arthritis) is an inflammatory disease of synovial joints affecting approximately 1% of the population. One of the main cell types involved in damage to RA joint tissue is the FLSs (fibroblast-like synoviocytes). These have a semi-transformed, auto-aggressive phenotype typified by loss of contact inhibition, reduced apoptosis and the production of matrix-degrading enzymes. The mechanisms involved in the development of this phenotype are unclear; however, increasing evidence implicates alterations in the epigenetic regulation of gene expression. Reduced acetylation of amino acids in the tails of histone proteins is an epigenetic mark associated with transcriptional repression and is controlled by the HDAC (histone deacetylase) enzyme family. To date, evidence has implicated HDACs in the auto-aggressive phenotype of FLSs, and administration of HDAC inhibitors to both animal models of RA and individuals with juvenile arthritis has shown efficacy in attenuating inflammation and tissue damage. This highlights a role for HDACs in disease pathogenesis and, more importantly, that HDACs are potential novel therapeutic targets.

Quantifying missing heritability at known GWAS loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.

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.

Lysosomal β-glucuronidase regulates Lyme and rheumatoid arthritis severity

Lyme disease, caused by the spirochete Borrelia burgdorferi, is the most prevalent arthropod-borne illness in the United States and remains a clinical and social challenge. The spectrum of disease severity among infected patients suggests that host genetics contribute to pathogenic outcomes, particularly in patients who develop arthritis. Using a forward genetics approach, we identified the lysosomal enzyme β-glucuronidase (GUSB), a member of a large family of coregulated lysosomal enzymes, as a key regulator of Lyme-associated arthritis severity. Severely arthritic C3H mice possessed a naturally occurring hypomorphic allele, Gusbh. C57BL/6 mice congenic for the C3H Gusb allele were prone to increased Lyme-associated arthritis severity. Radiation chimera experiments revealed that resident joint cells drive arthritis susceptibility. C3H mice expressing WT Gusb as a transgene were protected from severe Lyme arthritis. Importantly, the Gusbh allele also exacerbated disease in a serum transfer model of rheumatoid arthritis. A known GUSB function is the prevention of lysosomal accumulation of glycosaminoglycans (GAGs). Development of Lyme and rheumatoid arthritis in Gusbh-expressing mice was associated with heightened accumulation of GAGs in joint tissue. We propose that GUSB modulates arthritis pathogenesis by preventing accumulation of proinflammatory GAGs within inflamed joint tissue, a trait that may be shared by other lysosomal exoglycosidases.

Genome-wide association study of dermatomyositis reveals genetic overlap with other autoimmune disorders

OBJECTIVE

To identify new genetic associations with juvenile and adult dermatomyositis (DM).

METHODS

We performed a genome-wide association study (GWAS) of adult and juvenile DM patients of European ancestry (n = 1,178) and controls (n = 4,724). To assess genetic overlap with other autoimmune disorders, we examined whether 141 single-nucleotide polymorphisms (SNPs) outside the major histocompatibility complex (MHC) locus, and previously associated with autoimmune diseases, predispose to DM.

RESULTS

Compared to controls, patients with DM had a strong signal in the MHC region consisting of GWAS-level significance (P < 5 × 10(-8)) at 80 genotyped SNPs. An analysis of 141 non-MHC SNPs previously associated with autoimmune diseases showed that 3 SNPs linked with 3 genes were associated with DM, with a false discovery rate (FDR) of <0.05. These genes were phospholipase C-like 1 (PLCL1; rs6738825, FDR = 0.00089), B lymphoid tyrosine kinase (BLK; rs2736340, FDR = 0.0031), and chemokine (C-C motif) ligand 21 (CCL21; rs951005, FDR = 0.0076). None of these genes was previously reported to be associated with DM.

CONCLUSION

Our findings confirm the MHC as the major genetic region associated with DM and indicate that DM shares non-MHC genetic features with other autoimmune diseases, suggesting the presence of additional novel risk loci. This first identification of autoimmune disease genetic predispositions shared with DM may lead to enhanced understanding of pathogenesis and novel diagnostic and therapeutic approaches.

Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases

While studies to associate genomic variants to complex traits have gradually become increasingly productive, the molecular mechanisms that underlie these associations are rarely understood. Because only a small fraction of trait-associated variants can be linked to coding sequences, investigators have speculated that many of the underlying causal alleles influence non-coding gene regulatory sites. Recent studies have successfully identified examples of mechanisms for non-coding alleles at individual loci. Now, genome-wide chromatin assays have resulted in maps of dozens of genomic annotations of the non-coding genome across multiple different tissues, cell types and cell lines. This gives a tremendous opportunity to integrate these annotations with complex trait signals to globally interpret associated variants, and prioritize likely causal alleles. Here, we review the examples of mechanisms by which non-coding, common alleles result in phenotypes. We discuss the efforts to integrate common trait-associated variants with genomic annotations. Finally, we highlight some caveats of these approaches and outline future directions for improvement.

Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Clinical implications of shared genetics and pathogenesis in autoimmune diseases

Many endocrine diseases, including type 1 diabetes mellitus, Graves disease, Addison disease and Hashimoto disease, originate as an autoimmune reaction that affects disease-specific target organs. These autoimmune diseases are characterized by the development of specific autoantibodies and by the presence of autoreactive T cells. They are caused by a complex genetic predisposition that is attributable to multiple genetic variants, each with a moderate-to-low effect size. Most of the genetic variants associated with a particular autoimmune endocrine disease are shared between other systemic and organ-specific autoimmune and inflammatory diseases, such as rheumatoid arthritis, coeliac disease, systemic lupus erythematosus and psoriasis. Here, we review the shared and specific genetic background of autoimmune diseases, summarize their treatment options and discuss how identifying the genetic and environmental factors that predispose patients to an autoimmune disease can help in the diagnosis and monitoring of patients, as well as the design of new treatments.

Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and "Measles" pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.

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

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

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

Predicting disease risk using bootstrap ranking and classification algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

Genome-wide association studies are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction have relatively poor performance, with one possible explanation being the fact they rely on a noisy ranking of genetic variants given to them as input. To improve the predictive power, we devised BootRank, a ranking method less sensitive to noise. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data, and that combining BootRank with different classification algorithms improves performance compared to previous studies that used these data. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

Genetic insights into common pathways and complex relationships among immune-mediated diseases

Shared aetiopathogenic factors among immune-mediated diseases have long been suggested by their co-familiality and co-occurrence, and molecular support has been provided by analysis of human leukocyte antigen (HLA) haplotypes and genome-wide association studies. The interrelationships can now be better appreciated following the genotyping of large immune disease sample sets on a shared SNP array: the 'Immunochip'. Here, we systematically analyse loci shared among major immune-mediated diseases. This reveals that several diseases share multiple susceptibility loci, but there are many nuances. The most associated variant at a given locus frequently differs and, even when shared, the same allele often has opposite associations. Interestingly, risk alleles conferring the largest effect sizes are usually disease-specific. These factors help to explain why early evidence of extensive 'sharing' is not always reflected in epidemiological overlap.

Combining genetic mapping with genome-wide expression in experimental autoimmune encephalomyelitis highlights a gene network enriched for T cell functions and candidate genes regulating autoimmunity

The experimental autoimmune encephalomyelitis (EAE) is an autoimmune disease of the central nervous system commonly used to study multiple sclerosis (MS). We combined clinical EAE phenotypes with genome-wide expression profiling in spleens from 150 backcross rats between susceptible DA and resistant PVG rat strains during the chronic EAE phase. This enabled correlation of transcripts with genotypes, other transcripts and clinical EAE phenotypes and implicated potential genetic causes and pathways in EAE. We detected 2285 expression quantitative trait loci (eQTLs). Sixty out of 599 cis-eQTLs overlapped well-known EAE QTLs and constitute positional candidate genes, including Ifit1 (Eae7), Atg7 (Eae20-22), Klrc3 (eEae22) and Mfsd4 (Eae17). A trans-eQTL that overlaps Eae23a regulated a large number of small RNAs and implicates a master regulator of transcription. We defined several disease-correlated networks enriched for pathways involved in cell-mediated immunity. They include C-type lectins, G protein coupled receptors, mitogen-activated protein kinases, transmembrane proteins, suppressors of transcription (Jundp2 and Nr1d1) and STAT transcription factors (Stat4) involved in interferon signaling. The most significant network was enriched for T cell functions, similar to genetic findings in MS, and revealed both established and novel gene interactions. Transcripts in the network have been associated with T cell proliferation and differentiation, the TCR signaling and regulation of regulatory T cells. A number of network genes and their family members have been associated with MS and/or other autoimmune diseases. Combining disease and genome-wide expression phenotypes provides a link between disease risk genes and distinct molecular pathways that are dysregulated during chronic autoimmune inflammation.

Validating therapeutic targets through human genetics

More than 90% of the compounds that enter clinical trials fail to demonstrate sufficient safety and efficacy to gain regulatory approval. Most of this failure is due to the limited predictive value of preclinical models of disease, and our continued ignorance regarding the consequences of perturbing specific targets over long periods of time in humans. 'Experiments of nature' - naturally occurring mutations in humans that affect the activity of a particular protein target or targets - can be used to estimate the probable efficacy and toxicity of a drug targeting such proteins, as well as to establish causal rather than reactive relationships between targets and outcomes. Here, we describe the concept of dose-response curves derived from experiments of nature, with an emphasis on human genetics as a valuable tool to prioritize molecular targets in drug development. We discuss empirical examples of drug-gene pairs that support the role of human genetics in testing therapeutic hypotheses at the stage of target validation, provide objective criteria to prioritize genetic findings for future drug discovery efforts and highlight the limitations of a target validation approach that is anchored in human genetics.

Mapping of immune-mediated disease genes

Genetic studies in immune-mediated diseases have yielded a large number of disease-associated loci. Here we review the progress being made in 12 such diseases, for which 199 independently associated non-HLA loci have been identified by genome-wide association studies since 2007. It is striking that many of the loci are not unique to a single disease but shared between different immune-mediated diseases. The challenge now is to understand how the unique and shared genetic factors can provide insight into the underlying disease biology. We annotated disease-associated variants using the Encyclopedia of DNA Elements (ENCODE) database and demonstrate that, of the predisposing disease variants, the majority have the potential to be regulatory. We also demonstrate that many of these variants affect the expression of nearby genes. Furthermore, we summarize results from the Immunochip, a custom array, which allows a detailed comparison between five of the diseases that have so far been analyzed using this platform.

Identification of the tyrosine-protein phosphatase non-receptor type 2 as a rheumatoid arthritis susceptibility locus in europeans

OBJECTIVES

Genome-wide association studies have facilitated the identification of over 30 susceptibility loci for rheumatoid arthritis (RA). However, evidence for a number of potential susceptibility genes have not so far reached genome-wide significance in studies of Caucasian RA.

METHODS

A cohort of 4286 RA patients from across Europe and 5642 population matched controls were genotyped for 25 SNPs, then combined in a meta-analysis with previously published data.

RESULTS

Significant evidence of association was detected for nine SNPs within the European samples. When meta-analysed with previously published data, 21 SNPs were associated with RA susceptibility. Although SNPs in the PTPN2 gene were previously reported to be associated with RA in both Japanese and European populations, we show genome-wide evidence for a different SNP within this gene associated with RA susceptibility in an independent European population (rs7234029, P = 4.4×10(-9)).

CONCLUSIONS

This study provides further genome-wide evidence for the association of the PTPN2 locus (encoding the T cell protein tyrosine phosphastase) with Caucasian RA susceptibility. This finding adds to the growing evidence for PTPN2 being a pan-autoimmune susceptibility gene.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis

We used the Immunochip array to analyze 2,816 individuals with juvenile idiopathic arthritis (JIA), comprising the most common subtypes (oligoarticular and rheumatoid factor-negative polyarticular JIA), and 13,056 controls. We confirmed association of 3 known JIA risk loci (the human leukocyte antigen (HLA) region, PTPN22 and PTPN2) and identified 14 loci reaching genome-wide significance (P < 5 × 10(-8)) for the first time. Eleven additional new regions showed suggestive evidence of association with JIA (P < 1 × 10(-6)). Dense mapping of loci along with bioinformatics analysis refined the associations to one gene in each of eight regions, highlighting crucial pathways, including the interleukin (IL)-2 pathway, in JIA disease pathogenesis. The entire Immunochip content, the HLA region and the top 27 loci (P < 1 × 10(-6)) explain an estimated 18, 13 and 6% of the risk of JIA, respectively. In summary, this is the largest collection of JIA cases investigated so far and provides new insight into the genetic basis of this childhood autoimmune disease.

Human genetics in rheumatoid arthritis guides a high-throughput drug screen of the CD40 signaling pathway

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10⁻⁹). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10⁻⁹), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA–approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.

A current challenge in human genetics is to follow-up “hits” from genome-wide association studies (GWAS) to guide drug discovery for complex traits. Previously, we identified a common variant in the CD40 locus as associated with risk of rheumatoid arthritis (RA). Here, we fine-map the CD40 signal of association through a combination of dense genotyping and exonic sequencing in large patient collections. Further, we demonstrate that the RA risk allele is a gain-of-function allele that increases the amount of CD40 on the surface of primary human B lymphocyte cells from healthy control individuals. Based on these observations, we develop a high-throughput assay to recapitulate the biology of the RA risk allele in a system suitable for a small molecule drug screen. After a series of primary screens and counter screens, we identify small molecules that inhibit CD40-mediated NF-kB signaling in human B cells. While this is only the first step towards a more comprehensive effort to identify CD40-specific inhibitors that may be used to treat RA, our study demonstrates a successful strategy to progress from a GWAS to a drug screen for complex traits such as RA.

Integrative analyses for functional mechanisms underlying associations for rheumatoid arthritis

OBJECTIVE

Extensive association analyses including genome-wide association studies (GWAS) and powerful metaanalysis studies have identified a long list of loci associated with rheumatoid arthritis (RA) in very large populations, but most of them established statistical associations of genetic markers and RA only at the DNA level, without supporting evidence of functional relevance. Our study serves as a trial to detect the functional mechanisms underlying associations for RA by searching publicly available datasets and results.

METHODS

Based on publicly available datasets and results, we performed integrative analyses (gene relationships across implicated loci analysis, differential gene expression analysis, and functional annotation clustering analysis) and combined them with the expression quantitative trait locus (eQTL) results to dissect functional mechanisms underlying the associations for RA.

RESULTS

By searching 2 GWAS, Integrator and PheGenI, we selected 98 RA association results (p < 10(-5)). Among these associations, we found that 8 single-nucleotide polymorphisms (SNP; rs1600249, rs2736340, rs3093023, rs3093024, rs4810485, rs615672, rs660895, and rs9272219) serve as cis-effect regulators of the corresponding eQTL genes (BLK and CD4 in non-HLA region; CCR6, HLA-DQA1, and HLA-DQB1 in HLA region) that also were differentially expressed in RA-related cell groups. These 5 genes are closely related with immune response in function.

CONCLUSION

Our results showed the functional mechanisms underlying the associations of 8 SNP and the corresponding genes. This study is an example of mining publicly available datasets and results in validation of significant disease-association results. Using public data resources for integrative analyses may provide insights into the molecular genetic mechanisms underlying human diseases.

Enrichment of vitamin D response elements in RA-associated loci supports a role for vitamin D in the pathogenesis of RA

The aim of this study was to explore the role of vitamin D in rheumatoid arthritis (RA) pathogenesis by investigating the enrichment of vitamin D response elements (VDREs) in confirmed RA susceptibility loci and testing variants associated with vitamin D levels for association with RA. Bioinformatically, VDRE genomic positions were overlaid with non-HLA (human leukocyte antigen)-confirmed RA susceptibility regions. The number of VDREs at RA loci was compared to a randomly selected set of genomic loci to calculate an average relative risk (RR). Single-nucleotide polymorphisms (SNPs) in the DHCR7/NADSYN1 (nicotinamide adenine dinucleotide synthase 1) and CYP2R1 loci, previously associated with circulating vitamin D levels, were tested in UK RA cases (n=3870) and controls (n=8430). Significant enrichment of VDREs was seen at RA loci (P=9.23 × 10(-8)) when regions were defined either by gene (RR 5.50) or position (RR 5.86). SNPs in the DHCR7/NADSYN1 locus showed evidence of positive association with RA, rs4944076 (P=0.008, odds ratio (OR) 1.14, 95% confidence interval (CI) 1.03-1.24). The significant enrichment of VDREs at RA-associated loci and the modest association of variants in loci-controlling levels of circulating vitamin D supports the hypothesis that vitamin D has a role in the development of RA.

Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases

Inflammation, which is directly regulated by interleukin-6 (IL-6) signaling, is implicated in the etiology of several chronic diseases. Although a common, non-synonymous variant in the IL-6 receptor gene (IL6R Asp358Ala; rs2228145 A>C) is associated with the risk of several common diseases, with the 358Ala allele conferring protection from coronary heart disease (CHD), rheumatoid arthritis (RA), atrial fibrillation (AF), abdominal aortic aneurysm (AAA), and increased susceptibility to asthma, the variant's effect on IL-6 signaling is not known. Here we provide evidence for the association of this non-synonymous variant with the risk of type 1 diabetes (T1D) in two independent populations and confirm that rs2228145 is the major determinant of the concentration of circulating soluble IL-6R (sIL-6R) levels (34.6% increase in sIL-6R per copy of the minor allele 358Ala; rs2228145 [C]). To further investigate the molecular mechanism of this variant, we analyzed expression of IL-6R in peripheral blood mononuclear cells (PBMCs) in 128 volunteers from the Cambridge BioResource. We demonstrate that, although 358Ala increases transcription of the soluble IL6R isoform (P = 8.3×10⁻²²) and not the membrane-bound isoform, 358Ala reduces surface expression of IL-6R on CD4+ T cells and monocytes (up to 28% reduction per allele; P≤5.6×10⁻²²). Importantly, reduced expression of membrane-bound IL-6R resulted in impaired IL-6 responsiveness, as measured by decreased phosphorylation of the transcription factors STAT3 and STAT1 following stimulation with IL-6 (P≤5.2×10⁻⁷). Our findings elucidate the regulation of IL-6 signaling by IL-6R, which is causally relevant to several complex diseases, identify mechanisms for new approaches to target the IL-6/IL-6R axis, and anticipate differences in treatment response to IL-6 therapies based on this common IL6R variant.

Interleukin-6 (IL-6) is a complex cytokine, which plays a critical role in the regulation of inflammatory responses. Genetic variation in the IL-6 receptor gene is associated with the risk of several human diseases with an inflammatory component, including coronary heart disease, rheumatoid arthritis, and asthma. A common non-synonymous single nucleotide polymorphism in this gene (Asp358Ala) has been suggested to be the causal variant in this region by affecting the circulatory concentrations of soluble IL-6R (sIL-6R). In this study we extend the genetic association of this variant to type 1 diabetes and provide evidence that this variant exerts its functional mechanism by regulating the balance between sIL-6R (generated through cleavage of the surface receptor and by alternative splicing of a soluble IL6R isoform) and membrane-bound IL-6R. These data show for the first time that the minor allele of this non-synonymous variant (Ala358) directly controls the surface levels of IL-6R on individual immune cells and that these differences in protein levels translate into a functional impairment in IL-6R signaling. These findings may have implications for clinical trials targeting inflammatory mechanisms involving IL-6R signaling and may provide tools for identifying patients with specific benefit from therapeutic intervention in the IL-6R signaling pathway.

From Identification to Characterization of the Multiple Sclerosis Susceptibility Gene CLEC16A

Multiple sclerosis (MS) is an inflammatory, demyelinating disorder of the central nervous system that develops in genetically susceptible individuals, probably triggered by common environmental factors. Human leukocyte antigen (HLA) loci were early shown to confer the strongest genetic associations in MS. Now, more than 50 non-HLA MS susceptibility loci are identified, of which the majority are located in immune-regulatory genes. Single nucleotide polymorphisms (SNPs) in the C-type lectin-like domain family 16A (CLEC16A) gene were among the first non-HLA genetic variants that were confirmed to be associated with MS. Fine-mapping has indicated a primary association in MS and also other autoimmune diseases to intronic CLEC16A SNPs. Here, we review the identification of MS susceptibility variants in the CLEC16A gene region, functional studies of the CLEC16A molecule and the recent progress in understanding the implications thereof for MS development. This may serve as an example of the importance for further molecular investigation of the loci identified in genetic studies, with the aim to translate this knowledge into the clinic.

Genetics and epigenetics of rheumatoid arthritis

Investigators have made key advances in rheumatoid arthritis (RA) genetics in the past 10 years. Although genetic studies have had limited influence on clinical practice and drug discovery, they are currently generating testable hypotheses to explain disease pathogenesis. Firstly, we review here the major advances in identifying RA genetic susceptibility markers both within and outside of the MHC. Understanding how genetic variants translate into pathogenic mechanisms and ultimately into phenotypes remains a mystery for most of the polymorphisms that confer susceptibility to RA, but functional data are emerging. Interplay between environmental and genetic factors is poorly understood and in need of further investigation. Secondly, we review current knowledge of the role of epigenetics in RA susceptibility. Differences in the epigenome could represent one of the ways in which environmental exposures translate into phenotypic outcomes. The best understood epigenetic phenomena include post-translational histone modifications and DNA methylation events, both of which have critical roles in gene regulation. Epigenetic studies in RA represent a new area of research with the potential to answer unsolved questions.

Two rare disease-associated Tyk2 variants are catalytically impaired but signaling competent

Tyk2 belongs to the Janus protein tyrosine kinase family and is involved in signaling of immunoregulatory cytokines (type I and III IFNs, IL-6, IL-10, and IL-12 families) via its interaction with shared receptor subunits. Depending on the receptor complex, Tyk2 is coactivated with either Jak1 or Jak2, but a detailed molecular characterization of the interplay between the two enzymes is missing. In human populations, the Tyk2 gene presents high levels of genetic diversity with >100 nonsynonymous variants being detected. In this study, we characterized two rare Tyk2 variants, I684S and P1104A, which have been associated with susceptibility to autoimmune disease. Specifically, we measured their in vitro catalytic activity and their ability to mediate Stat activation in fibroblasts and genotyped B cell lines. Both variants were found to be catalytically impaired but rescued signaling in response to IFN-α/β, IL-6, and IL-10. These data, coupled with functional study of an engineered Jak1 P1084A, support a model of nonhierarchical activation of Janus kinases in which one catalytically competent Jak is sufficient for signaling provided that its partner behaves as proper scaffold, even if inactive. Through the analysis of IFN-α and IFN-γ signaling in cells with different Jak1 P1084A levels, we also illustrate a context in which a hypomorphic Jak can hamper signaling in a cytokine-specific manner. Given the multitude of Tyk2-activating cytokines, the cell context-dependent requirement for Tyk2 and the catalytic defect of the two disease-associated variants studied in this paper, we predict that these alleles are functionally significant in complex immune disorders.

Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis

The extent to which variants in the protein-coding sequence of genes contribute to risk of rheumatoid arthritis (RA) is unknown. In this study, we addressed this issue by deep exon sequencing and large-scale genotyping of 25 biological candidate genes located within RA risk loci discovered by genome-wide association studies (GWASs). First, we assessed the contribution of rare coding variants in the 25 genes to the risk of RA in a pooled sequencing study of 500 RA cases and 650 controls of European ancestry. We observed an accumulation of rare nonsynonymous variants exclusive to RA cases in IL2RA and IL2RB (burden test: p = 0.007 and p = 0.018, respectively). Next, we assessed the aggregate contribution of low-frequency and common coding variants to the risk of RA by dense genotyping of the 25 gene loci in 10,609 RA cases and 35,605 controls. We observed a strong enrichment of coding variants with a nominal signal of association with RA (p < 0.05) after adjusting for the best signal of association at the loci (p(enrichment) = 6.4 × 10(-4)). For one locus containing CD2, we found that a missense variant, rs699738 (c.798C>A [p.His266Gln]), and a noncoding variant, rs624988, reside on distinct haplotypes and independently contribute to the risk of RA (p = 4.6 × 10(-6)). Overall, our results indicate that variants (distributed across the allele-frequency spectrum) within the protein-coding portion of a subset of biological candidate genes identified by GWASs contribute to the risk of RA. Further, we have demonstrated that very large sample sizes will be required for comprehensively identifying the independent alleles contributing to the missing heritability of RA.

Chromatin marks identify critical cell types for fine mapping complex trait variants

If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant cell types. However, it is unclear which of the many marks are most useful in defining cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.