Case-control association analysis of rheumatoid arthritis with candidate genes using related cases

The SUMO components in rheumatoid arthritis

Small ubiquitin-like modifier (SUMO) proteins can reversibly attach covalently or non-covalently to lysine residues of various substrates. The processes are named SUMOylation and de-SUMOylation, which maintain a dynamic balance in the physiological state, and are regulated by SUMO components. However, the dysregulation of components disturbs the balance and alters the functions of target proteins, which causes the occurrence of diseases. To date, certain SUMO components, including SUMO-1, SUMO-2/3, SAE1/Uba2, Ubc9, PIASs (protein inhibitors of activated signal transducer and activator of transcription) and SENPs (SUMO-specific proteases), have been found to participate in the pathogenesis of RA and their potential value as therapeutic targets also have been highlighted. In addition, single nucleotide polymorphisms (SNPs) in the SUMO components have been reported to be associated with disease susceptibility. Until now, only the SNP site of SUMO-4 has been reported in RA. Here we provided a systematic overview of the general characteristics of SUMO components and highlighted a summary of their impact on RA.

TIM-3 rs1036199 polymorphism increases susceptibility to autoimmune diseases: evidence based on 4200 subjects

Conflicting results have been reported regarding differing studies on the association between T-cell immunoglobulin and mucin domain 3 polymorphisms and autoimmune disease. The purpose of the present study was to evaluate the association of TIM-3 rs1036199 (4259 G/T) polymorphism with autoimmune disease susceptibility. A meta-analysis was performed to obtain a more precise evaluation of the association. Ten eligible studies were retrieved by searching PubMed, Embase and Web of Science databases, and statistical analyses were performed using STATA software. The pooled results indicated that TIM-3 rs1036199 polymorphism was significantly associated with an increased risk of overall autoimmune disease in allele comparison (G versus T: OR = 1.59, 95%CI: 1.17-2.17) and heterozygous comparison (GT versus TT: OR = 1.68, 95%CI: 1.37-2.06). Subgroup analyses based on disease type demonstrated that TIM-3 rs1036199 polymorphism was associated with an increased risk of rheumatic arthritis (G versus T: OR = 1.88, 95%CI: 1.45-2.44; GT versus TT: OR = 2.02, 95%CI: 1.53-2.65), especially in Asian populations.

PTPN22 polymorphisms, but not R620W, were associated with the genetic susceptibility of systemic lupus erythematosus and rheumatoid arthritis in a Chinese Han population

OBJECTIVES

The present study aimed to detect a possible association between PTPN22 gene polymorphisms and rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) in a Chinese Han population.

METHODS

7 PTPN22 SNPs were genotyped in 358 patients with RA and 713 patients with SLE, as well as 564 RA controls and 672 SLE controls by Restriction Fragment Length Polymorphism (RFLP). Association analyses were conducted on the whole data set. Significant relationships were also examined between clinical features and SNPs for both RA and SLE.

RESULTS

Rs2476601 was lack of polymorphism with a ⩽0.1% frequency in both SLE and RA patients and healthy controls in our study. The two SNPs rs1217414 and rs3811021 of PTPN22 shown strong association with both SLE (rs1217414T: padj = 6.07e-004, OR=0.57; rs3811021C: padj = 4.68e-005, OR=0.65) and RA (rs1217414T: padj = 2.01e-008, OR=0.26; rs3811021C: padj = 0.028, OR=0.70). And the rs3765598 revealed a strong risk factor for SLE (p=9.38e-009, padj = 6.57e-008, OR=1.93), but not for RA (p=0.48, OR=1.12). Moreover, protective haplotype ACTTC in RA (p=7.73e-016, padj = 5.51-015, OR[95%CI]=0.02[0.002-0.10]) and SLE (p=8.29e-018, padj = 5.80e-017, OR[95%CI]=0.11[0.06-0.21]) were observed. In addition, the distribution of risk haplotypes ACGTC and GCTTT in RA (ACGTC: p=0.0006, padj = 0.004, OR[95%CI]=1.85[1.29-2.63]; GCTTT: p=2.62e-005, padj = 1.85e-004, OR[95%CI]=2.40[1.57-3.65]) and SLE (ACGTC: p=0.0006, padj = 0.004, OR[95%CI]=1.85[1.29-2.63]; ACGTC: p=7.74e-011, padj = 6.81e-010, OR[95%CI]=2.21[1.12-3.34]; GCTTT: p=2.40[1.57-3.65], padj = 2.26e-006, OR[95%CI]=2.64[1.79-3.87]) were significant different from that in controls. Furthermore, significant association was observed between the PTPN22 rs3765598 and antinuclear antibodies 1 (ANA1) in SLE.

CONCLUSIONS

Our data provide strong evidence that the rs1217414 and rs3811021 in PTPN22 gene might be common protective factors contributed to SLE and RA susceptibility in the Chinese Han population. While, the rs3765598 might increase the genetic susceptibility of SLE, but not RA.

Network medicine analysis of chondrocyte proteins towards new treatments of osteoarthritis

Osteoarthritis (OA) is a progressive disorder with high incidence in the ageing human population that still has no treatment currently. This disorder induces the breakdown of articular cartilage, leading to the exposure and damage of bone surfaces. For a global understanding of OA development, the systematic integration of known OA-related proteins with protein-protein interaction (PPI) networks is required. In this work, the OA-related interactome was reconstructed using multiple data sources to have the most up-to-date information on OA-related proteins and their interactions. We then combined emergent concepts in network medicine to detect new unclassified OA-related proteins. The mapping of known OA-related proteins with PPI networks showed that these proteins are locally connected to each other and agglomerated in a large component. To expand this module, we applied a diffusion-based algorithm that probabilistically induces more searches in the vicinity of the seed OA-related proteins. As a result, the 10 topmost ranked proteins were connected to the OA disease module, supporting the local hypothesis. We computed structural modules and selected those that had the highest enrichment of OA-related proteins. The identified molecules show a link between structural topology and disease dysfunctionality. Interestingly, the protein Q6EEV6 was highlighted for OA association by both methods, reinforcing the potential involvement of this protein. These results suggest that similar disease-connected modules may exist in different human disorders, which could lead to systematic identification of genes or proteins that have a joint role in specific disease phenotypes.

PTPN22 association in systemic lupus erythematosus (SLE) with respect to individual ancestry and clinical sub-phenotypes

Protein tyrosine phosphatase non-receptor type 22 (PTPN22) is a negative regulator of T-cell activation associated with several autoimmune diseases, including systemic lupus erythematosus (SLE). Missense rs2476601 is associated with SLE in individuals with European ancestry. Since the rs2476601 risk allele frequency differs dramatically across ethnicities, we assessed robustness of PTPN22 association with SLE and its clinical sub-phenotypes across four ethnically diverse populations. Ten SNPs were genotyped in 8220 SLE cases and 7369 controls from in European-Americans (EA), African-Americans (AA), Asians (AS), and Hispanics (HS). We performed imputation-based association followed by conditional analysis to identify independent associations. Significantly associated SNPs were tested for association with SLE clinical sub-phenotypes, including autoantibody profiles. Multiple testing was accounted for by using false discovery rate. We successfully imputed and tested allelic association for 107 SNPs within the PTPN22 region and detected evidence of ethnic-specific associations from EA and HS. In EA, the strongest association was at rs2476601 (P = 4.7 × 10(-9), OR = 1.40 (95% CI = 1.25-1.56)). Independent association with rs1217414 was also observed in EA, and both SNPs are correlated with increased European ancestry. For HS imputed intronic SNP, rs3765598, predicted to be a cis-eQTL, was associated (P = 0.007, OR = 0.79 and 95% CI = 0.67-0.94). No significant associations were observed in AA or AS. Case-only analysis using lupus-related clinical criteria revealed differences between EA SLE patients positive for moderate to high titers of IgG anti-cardiolipin (aCL IgG >20) versus negative aCL IgG at rs2476601 (P = 0.012, OR = 1.65). Association was reinforced when these cases were compared to controls (P = 2.7 × 10(-5), OR = 2.11). Our results validate that rs2476601 is the most significantly associated SNP in individuals with European ancestry. Additionally, rs1217414 and rs3765598 may be associated with SLE. Further studies are required to confirm the involvement of rs2476601 with aCL IgG.

Polymorphisms of the TIM-1 and TIM-3 genes are not associated with systemic lupus erythematosus in a Chinese population

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune diseases, which affects multiple organ systems such as kidney. The imbalance of T-helper 1 (Th1)/Th2 cells is critical in the pathogenesis of SLE. The T-cell immunoglobulin mucin (TIM) proteins comprise a family of cell surface molecules expressed on T cells that regulate Th1- and Th2-cell-mediated immunity. Recent work has found increased expression of TIM-1 and TIM-3 ligand (galactin-9) mRNA in SLE patients and implied that TIM proteins might be involved in the pathogenesis of SLE. In this study, genotyping of single-nucleotide polymorphisms (SNPs) was performed for TIM-1 (rs1501909 and rs12522248) and TIM-3 (rs9313439 and rs10515746) in 202 SLE patients and 217 healthy individuals in a Chinese population. Results showed no significant differences existed between the patients with SLE and the controls as well as SLE patients with nephritis and those without nephritis, in all four SNPs. The findings suggest that the polymorphisms of TIM gene family might not contribute to SLE susceptibility in the Chinese population. However, it should be noted that the statistical power of our study is relatively low, which likely did not have adequate power to detect the actual correlation between the selected SNPs and SLE susceptibility; moreover, we cannot discard a possible association of other variants within the region covering TIM with SLE as a genetic risk factor, with larger samples in different populations.

Effective sample size: Quick estimation of the effect of related samples in genetic case-control association analyses

Affected relatives are essential for pedigree linkage analysis, however, they cause a violation of the independent sample assumption in case-control association studies. To avoid the correlation between samples, a common practice is to take only one affected sample per pedigree in association analysis. Although several methods exist in handling correlated samples, they are still not widely used in part because these are not easily implemented, or because they are not widely known. We advocate the effective sample size method as a simple and accessible approach for case-control association analysis with correlated samples. This method modifies the chi-square test statistic, p-value, and 95% confidence interval of the odds-ratio by replacing the apparent number of allele or genotype counts with the effective ones in the standard formula, without the need for specialized computer programs. We present a simple formula for calculating effective sample size for many types of relative pairs and relative sets. For allele frequency estimation, the effective sample size method captures the variance inflation exactly. For genotype frequency, simulations showed that effective sample size provides a satisfactory approximation. A gene which is previously identified as a type 1 diabetes susceptibility locus, the interferon-induced helicase gene (IFIH1), is shown to be significantly associated with rheumatoid arthritis when the effective sample size method is applied. This significant association is not established if only one affected sib per pedigree were used in the association analysis. Relationship between the effective sample size method and other methods - the generalized estimation equation, variance of eigenvalues for correlation matrices, and genomic controls - are discussed.

A towards-multidimensional screening approach to predict candidate genes of rheumatoid arthritis based on SNP, structural and functional annotations

Background

According to the Genetic Analysis Workshops (GAW), hundreds of thousands of SNPs have been tested for association with rheumatoid arthritis. Traditional genome-wide association studies (GWAS) have been developed to identify susceptibility genes using a "most significant SNPs/genes" model. However, many minor- or modest-risk genes are likely to be missed after adjustment of multiple testing. This screening process uses a strict selection of statistical thresholds that aim to identify susceptibility genes based only on statistical model, without considering multi-dimensional biological similarities in sequence arrangement, crystal structure, or functional categories/biological pathways between candidate and known disease genes.

Methods

Multidimensional screening approaches combined with traditional statistical genetics methods can consider multiple biological backgrounds of genetic mutation, structural, and functional annotations. Here we introduce a newly developed multidimensional screening approach for rheumatoid arthritis candidate genes that considers all SNPs with nominal evidence of Bayesian association (BFLn > 0), and structural and functional similarities of corresponding genes or proteins.

Results

Our multidimensional screening approach extracted all risk genes (BFLn > 0) by odd ratios of hypothesis H₁ to H₀, and determined whether a particular group of genes shared underlying biological similarities with known disease genes. Using this method, we found 6614 risk SNPs in our Bayesian screen result set. Finally, we identified 146 likely causal genes for rheumatoid arthritis, including CD4, FGFR1, and KDR, which have been reported as high risk factors by recent studies. We must denote that 790 (96.1%) of genes identified by GWAS could not easily be classified into related functional categories or biological processes associated with the disease, while our candidate genes shared underlying biological similarities (e.g. were in the same pathway or GO term) and contributed to disease etiology, but where common variations in each of these genes make modest contributions to disease risk. We also found 6141 risk SNPs that were too minor to be detected by conventional approaches, and associations between 58 candidate genes and rheumatoid arthritis were verified by literature retrieved from the NCBI PubMed module.

Conclusions

Our proposed approach to the analysis of GAW16 data for rheumatoid arthritis was based on an underlying biological similarities-based method applied to candidate and known disease genes. Application of our method could identify likely causal candidate disease genes of rheumatoid arthritis, and could yield biological insights that not detected when focusing only on genes that give the strongest evidence by multiple testing. We hope that our proposed method complements the "most significant SNPs/genes" model, and provides additional insights into the pathogenesis of rheumatoid arthritis and other diseases, when searching datasets for hundreds of genetic variances.

Identification of HAVCR1 gene haplotypes associated with mRNA expression levels and susceptibility to autoimmune diseases

Human HAVCR1 gene maps on 5q33.2, a region linked with susceptibility to allergic and autoimmune diseases. The aims of the present study were to define the haplotypes of HAVCR1 gene taking into account both HapMap Project SNP haplotypes and exon 4 variants, to investigate a possible relationship between these haplotypes and mRNA expression levels, and to assess whether HAVCR1 gene is involved in susceptibility to rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Genotyping of three ins/del variants in the exon 4 was performed by fragment length analysis. Five tag SNPs genotypes and mRNA levels were determined using TaqMan assays. We defined four major haplotypes in our population: the two major haplotypes (named haplotypes A and B) bear both the 5383_5397del variant and the two most common SNP sets found in the CEU population. Quantification analysis revealed that genotype B/B had the highest median of mRNA expression levels (vs. BX + XX, p < 0.0001). Additionally, frequency of the genotype BB was significantly higher in RA patients than in controls (12.3 vs. 5.9% in controls, p = 0.0046, p (c) = 0.014, OR = 2.23, 95% CI 1.23-4.10). Our results support a relationship between HAVCR1 haplotypes and mRNA expression levels, and suggest an association of this gene with autoimmune diseases.

Analyses of multiple single-nucleotide polymorphisms in the SUMO4/IDDM5 region in affected sib-pair families with type I diabetes

Previous studies suggested that the SUMO4 gene, located in the IDDM5 interval on chromosome 6q25, was associated with type I diabetes (T1D) and several other autoimmune diseases. Subsequent analyses of the SUMO4 variants with T1D suggested that the association was stronger and more consistent in the Asian populations. In addition, considerable heterogeneity has been observed in the Caucasian populations. In this report, a 40-kb genomic interval including the SUMO4 gene was tagged with 15 single-nucleotide polymorphisms. A total of 2317 affected sib-pair families from the Type I Diabetes Genetic Consortium were genotyped using both the Illumina and Sequenom genotyping platforms. In these Caucasian families, we found little evidence supporting an association between SUMO4 and T1D.

Density-based clustering in haplotype analysis for association mapping

Clustering of related haplotypes in haplotype-based association mapping has the potential to improve power by reducing the degrees of freedom without sacrificing important information about the underlying genetic structure. We have modified a generalized linear model approach for association analysis by incorporating a density-based clustering algorithm to reduce the number of coefficients in the model. Using the GAW 15 Problem 3 simulated data, we show that our novel method can substantially enhance power to detect association with the binary rheumatoid arthritis (RA) phenotype at the HLA-DRB1 locus on chromosome 6. In contrast, clustering did not appreciably improve performance at locus D, perhaps a consequence of a rare susceptibility allele and of the overwhelming effect of HLA-DRB1/locus C, 5 cM distal. Optimization of parameters governing the clustering algorithm identified a set of parameters that delivered nearly ideal performance in a variety of situations. The cluster-based score test was valid over a wide range of haplotype diversity, and was robust to severe departures from Hardy-Weinberg equilibrium encountered near HLA-DRB1 in RA case-control samples.