OBJECTIVE: Genetic variants affect both the development and severity of rheumatoid arthritis (RA). Recent studies have expanded the number of RA susceptibility variants. We tested the hypothesis that these associated with disease severity in a clinical trial cohort of patients with early, active RA. METHODS: We evaluated 524 patients with RA enrolled in the Combination Anti-Rheumatic Drugs in Early RA (CARDERA) trials. We tested validated susceptibility variants - 69 single-nucleotide polymorphisms (SNP), 15 HLA-DRB1 alleles, and amino acid polymorphisms in 6 HLA molecule positions - for their associations with progression in Larsen scoring, 28-joint Disease Activity Scores, and Health Assessment Questionnaire (HAQ) scores over 2 years using linear mixed-effects and latent growth curve models. RESULTS: HLA variants were associated with joint destruction. The *04:01 SNP (rs660895, p = 0.0003), *04:01 allele (p = 0.0002), and HLA-DRβ1 amino acids histidine at position 13 (p = 0.0005) and valine at position 11 (p = 0.0012) significantly associated with radiological progression. This association was only significant in anticitrullinated protein antibody (ACPA)-positive patients, suggesting that while their effects were not mediated by ACPA, they only predicted joint damage in ACPA-positive RA. Non-HLA variants did not associate with radiograph damage (assessed individually and cumulatively as a weighted genetic risk score). Two SNP - rs11889341 (STAT4, p = 0.0001) and rs653178 (SH2B3-PTPN11, p = 0.0004) - associated with HAQ scores over 6-24 months. CONCLUSION: HLA susceptibility variants play an important role in determining radiological progression in early, active ACPA-positive RA. Genome-wide and HLA-wide analyses across large populations are required to better characterize the genetic architecture of radiological progression in RA.

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset.

Although dietary gluten is the trigger for celiac disease, risk is strongly influenced by genetic variation in the major histocompatibility complex (MHC) region. We fine mapped the MHC association signal to identify additional risk factors independent of the HLA-DQA1 and HLA-DQB1 alleles and observed five new associations that account for 18% of the genetic risk. Taking these new loci together with the 57 known non-MHC loci, genetic variation can now explain up to 48% of celiac disease heritability.

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3 x 10(-21)), A928V (rs35018800, OR = 0.53, P = 1.2 x 10(-9)), and I684S (rs12720356, OR = 0.86, P = 4.6 x 10(-7)). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, Pomnibus = 6 x 10(-18)), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P(omnibus) = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

OBJECTIVE: A highly polygenic aetiology and high degree of allele-sharing between ancestries have been well elucidated in genetic studies of rheumatoid arthritis. Recently, the high-density genotyping array Immunochip for immune disease loci identified 14 new rheumatoid arthritis risk loci among individuals of European ancestry. Here, we aimed to identify new rheumatoid arthritis risk loci using Korean-specific Immunochip data. METHODS: We analysed Korean rheumatoid arthritis case-control samples using the Immunochip and genome-wide association studies (GWAS) array to search for new risk alleles of rheumatoid arthritis with anticitrullinated peptide antibodies. To increase power, we performed a meta-analysis of Korean data with previously published European Immunochip and GWAS data for a total sample size of 9299 Korean and 45,790 European case-control samples. RESULTS: We identified eight new rheumatoid arthritis susceptibility loci (TNFSF4, LBH, EOMES, ETS1-FLI1, COG6, RAD51B, UBASH3A and SYNGR1) that passed a genome-wide significance threshold (p<5×10(-8)), with evidence for three independent risk alleles at 1q25/TNFSF4. The risk alleles from the seven new loci except for the TNFSF4 locus (monomorphic in Koreans), together with risk alleles from previously established RA risk loci, exhibited a high correlation of effect sizes between ancestries. Further, we refined the number of single nucleotide polymorphisms (SNPs) that represent potentially causal variants through a trans-ethnic comparison of densely genotyped SNPs. CONCLUSIONS: This study demonstrates the advantage of dense-mapping and trans-ancestral analysis for identification of potentially causal SNPs. In addition, our findings support the importance of T cells in the pathogenesis and the fact of frequent overlap of risk loci among diverse autoimmune diseases.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Systemic juvenile idiopathic arthritis (sJIA) is an often severe, potentially life-threatening childhood inflammatory disease, the pathophysiology of which is poorly understood. To determine whether genetic variation within the MHC locus on chromosome 6 influences sJIA susceptibility, we performed an association study of 982 children with sJIA and 8,010 healthy control subjects from nine countries. Using meta-analysis of directly observed and imputed SNP genotypes and imputed classic HLA types, we identified the MHC locus as a bona fide susceptibility locus with effects on sJIA risk that transcended geographically defined strata. The strongest sJIA-associated SNP, rs151043342 [P = 2.8 × 10(-17), odds ratio (OR) 2.6 (2.1, 3.3)], was part of a cluster of 482 sJIA-associated SNPs that spanned a 400-kb region and included the class II HLA region. Conditional analysis controlling for the effect of rs151043342 found that rs12722051 independently influenced sJIA risk [P = 1.0 × 10(-5), OR 0.7 (0.6, 0.8)]. Meta-analysis of imputed classic HLA-type associations in six study populations of Western European ancestry revealed that HLA-DRB1*11 and its defining amino acid residue, glutamate 58, were strongly associated with sJIA [P = 2.7 × 10(-16), OR 2.3 (1.9, 2.8)], as was the HLA-DRB1*11-HLA-DQA1*05-HLA-DQB1*03 haplotype [6.4 × 10(-17), OR 2.3 (1.9, 2.9)]. By examining the MHC locus in the largest collection of sJIA patients assembled to date, this study solidifies the relationship between the class II HLA region and sJIA, implicating adaptive immune molecules in the pathogenesis of sJIA.

OBJECTIVE: Rheumatoid arthritis (RA) is a chronic disease leading to joint destruction. Although many studies have addressed factors potentially correlated with the speed of joint destruction, less attention has been paid to the distribution of joint destruction in patients with RA. In this study, destruction of the hand bones in patients with RA was classified into 2 anatomic subgroups, the fingers and the non-fingers, with the aim of analyzing which factors are associated with destruction of the finger joints. METHODS: A total of 1,215 Japanese patients with RA were recruited from 2 different populations. The degree of joint destruction was assessed using the total modified Sharp/van der Heijde score (SHS) of radiographic joint damage. The SHS score of joint damage in the finger joints was used as the dependent variable, and the SHS score in the non-finger joints was used as a covariate. Age, sex, disease duration, smoking, C-reactive protein level, treatment for RA, and positivity for and levels of anti-citrullinated protein antibodies and rheumatoid factor (RF) were evaluated as candidate correlates. Overall effect sizes were assessed in a meta-analysis. In addition, associations observed in the Japanese patients were compared to those in a cohort of 157 Dutch RA patients in the BeSt study (a randomized, controlled trial involving 4 different strictly specified treatment strategies for early RA). RESULTS: Not surprisingly, disease duration in Japanese patients with RA was associated with the finger SHS score (P ≤ 0.00037). Both positivity for and levels of RF showed significant associations with the finger SHS score after adjustment for covariates (P = 0.0022 and P = 8.1 × 10(-7) , respectively). These associations were also true in relation to the time-averaged finger SHS score. An association between RF positivity and the finger SHS score was also observed in Dutch patients with RA in the BeSt study (P = 0.049). CONCLUSION: Positivity for and levels of RF are associated with finger joint destruction independent of non-finger joint destruction and other covariates. Our findings suggest that there are different mechanisms of joint destruction operating in the finger joints of patients with RA.

Previous genome-wide association studies (GWAS) of HIV-1-infected populations have been underpowered to detect common variants with moderate impact on disease outcome and have not assessed the phenotypic variance explained by genome-wide additive effects. By combining the majority of available genome-wide genotyping data in HIV-infected populations, we tested for association between ∼8 million variants and viral load (HIV RNA copies per milliliter of plasma) in 6,315 individuals of European ancestry. The strongest signal of association was observed in the HLA class I region that was fully explained by independent effects mapping to five variable amino acid positions in the peptide binding grooves of the HLA-B and HLA-A proteins. We observed a second genome-wide significant association signal in the chemokine (C-C motif) receptor (CCR) gene cluster on chromosome 3. Conditional analysis showed that this signal could not be fully attributed to the known protective CCR5Δ32 allele and the risk P1 haplotype, suggesting further causal variants in this region. Heritability analysis demonstrated that common human genetic variation-mostly in the HLA and CCR5 regions-explains 25% of the variability in viral load. This study suggests that analyses in non-European populations and of variant classes not assessed by GWAS should be priorities for the field going forward.

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.