Faculty Bibliography

OBJECTIVE: To describe a large, multicenter prospective cohort study of first-degree relatives (FDRs) of probands with rheumatoid arthritis (RA), and outline the use of such a study in investigating the natural history of RA development. METHODS: A total of 1,058 FDRs, none of whom met the American College of Rheumatology criteria for RA, were enrolled in a prospective study investigating genetic and environmental influences on the development of RA-related autoimmunity. Demographic, epidemiologic, genetic, autoantibody, and physical examination data from the initial study enrollment visit were described for these FDRs, and the relationship was examined between genetic factors, autoantibodies, inflammation, and joint disease. RESULTS: Fifty-five percent of the FDRs had > or =1 copy of the shared epitope, 20% had > or =1 copy of the PTPN22 polymorphism, and approximately 16% were positive for rheumatoid factor (RF; including isotypes) and/or anti-cyclic citrullinated peptide antibody. IgM-RF positivity is associated with > or =1 tender joint on examination (odds ratio [OR] 2.50, 95% confidence interval [95% CI] 1.27-4.89; P < 0.01) and elevated C-reactive protein (CRP) levels (OR 5.31, 95% CI 1.45-19.52; P = 0.01). CONCLUSION: FDRs without RA demonstrate high prevalences of genetic risk factors and RA-related autoantibodies. Additionally, an RF association with tender joints and elevated CRP levels suggests that autoantibodies are a valid intermediate marker of RA-related autoimmunity in this cohort. This prospective FDR cohort will be a valuable resource for evaluating the relationship between genetic and epidemiologic factors and the development of RA-related autoimmunity

ABSTRACT : For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors

Biomarker development for prediction of patient response to therapy is one of the goals of molecular profiling of human tissues. Due to the large number of transcripts, relatively limited number of samples, and high variability of data, identification of predictive biomarkers is a challenge for data analysis. Furthermore, many genes may be responsible for drug response differences, but often only a few are sufficient for accurate prediction. Here we present an analysis approach, the Convergent Random Forest (CRF) method, for the identification of highly predictive biomarkers. The aim is to select from genome-wide expression data a small number of non-redundant biomarkers that could be developed into a simple and robust diagnostic tool. Our method combines the Random Forest classifier and gene expression clustering to rank and select a small number of predictive genes. We evaluated the CRF approach by analyzing four different data sets. The first set contains transcript profiles of whole blood from rheumatoid arthritis patients, collected before anti-TNF treatment, and their subsequent response to the therapy. In this set, CRF identified 8 transcripts predicting response to therapy with 89% accuracy. We also applied the CRF to the analysis of three previously published expression data sets. For all sets, we have compared the CRF and recursive support vector machines (RSVM) approaches to feature selection and classification. In all cases the CRF selects much smaller number of features, five to eight genes, while achieving similar or better performance on both training and independent testing sets of data. For both methods performance estimates using cross-validation is similar to performance on independent samples. The method has been implemented in R and is available from the authors upon request: Jadwiga.Bienkowska@biogenidec.com

To discover new rheumatoid arthritis (RA) risk loci, we systematically examined 370 SNPs from 179 independent loci with P < 0.001 in a published meta-analysis of RA genome-wide association studies (GWAS) of 3,393 cases and 12,462 controls. We used Gene Relationships Across Implicated Loci (GRAIL), a computational method that applies statistical text mining to PubMed abstracts, to score these 179 loci for functional relationships to genes in 16 established RA disease loci. We identified 22 loci with a significant degree of functional connectivity. We genotyped 22 representative SNPs in an independent set of 7,957 cases and 11,958 matched controls. Three were convincingly validated: CD2-CD58 (rs11586238, P = 1 x 10(-6) replication, P = 1 x 10(-9) overall), CD28 (rs1980422, P = 5 x 10(-6) replication, P = 1 x 10(-9) overall) and PRDM1 (rs548234, P = 1 x 10(-5) replication, P = 2 x 10(-8) overall). An additional four were replicated (P < 0.0023): TAGAP (rs394581, P = 0.0002 replication, P = 4 x 10(-7) overall), PTPRC (rs10919563, P = 0.0003 replication, P = 7 x 10(-7) overall), TRAF6-RAG1 (rs540386, P = 0.0008 replication, P = 4 x 10(-6) overall) and FCGR2A (rs12746613, P = 0.0022 replication, P = 2 x 10(-5) overall). Many of these loci are also associated to other immunologic diseases

The human MHC represents the strongest susceptibility locus for autoimmune diseases. However, the identification of the true predisposing gene(s) has been handicapped by the strong linkage disequilibrium across the region. Furthermore, most studies to date have been limited to the examination of a subset of the HLA and non-HLA genes with a marker density and sample size insufficient for mapping all independent association signals. We genotyped a panel of 1,472 SNPs to capture the common genomic variation across the 3.44 megabase (Mb) classic MHC region in 10,576 DNA samples derived from patients with systemic lupus erythematosus, Crohn's disease, ulcerative colitis, rheumatoid arthritis, myasthenia gravis, selective IgA deficiency, multiple sclerosis, and appropriate control samples. We identified the primary association signals for each disease and performed conditional regression to identify independent secondary signals. The data demonstrate that MHC associations with autoimmune diseases result from complex, multilocus effects that span the entire region

The definition of European population genetic substructure and its application to understanding complex phenotypes is becoming increasingly important. In the current study using over 4,000 subjects genotyped for 300,000 single-nucleotide polymorphisms (SNPs), we provide further insight into relationships among European population groups and identify sets of SNP ancestry informative markers (AIMs) for application in genetic studies. In general, the graphical description of these principal components analyses (PCA) of diverse European subjects showed a strong correspondence to the geographical relationships of specific countries or regions of origin. Clearer separation of different ethnic and regional populations was observed when northern and southern European groups were considered separately and the PCA results were influenced by the inclusion or exclusion of different self-identified population groups including Ashkenazi Jewish, Sardinian, and Orcadian ethnic groups. SNP AIM sets were identified that could distinguish the regional and ethnic population groups. Moreover, the studies demonstrated that most allele frequency differences between different European groups could be controlled effectively in analyses using these AIM sets. The European substructure AIMs should be widely applicable to ongoing studies to confirm and delineate specific disease susceptibility candidate regions without the necessity of performing additional genome-wide SNP studies in additional subject sets

Genome-wide association studies have recently identified at least 15 susceptibility loci for systemic lupus erythematosus (SLE). To confirm additional risk loci, we selected SNPs from 2,466 regions that showed nominal evidence of association to SLE (P < 0.05) in a genome-wide study and genotyped them in an independent sample of 1,963 cases and 4,329 controls. This replication effort identified five new SLE susceptibility loci (P < 5 x 10(-8)): TNIP1 (odds ratio (OR) = 1.27), PRDM1 (OR = 1.20), JAZF1 (OR = 1.20), UHRF1BP1 (OR = 1.17) and IL10 (OR = 1.19). We identified 21 additional candidate loci with P< or = 1 x 10(-5). A candidate screen of alleles previously associated with other autoimmune diseases suggested five loci (P < 1 x 10(-3)) that may contribute to SLE: IFIH1, CFB, CLEC16A, IL12B and SH2B3. These results expand the number of confirmed and candidate SLE susceptibility loci and implicate several key immunologic pathways in SLE pathogenesis

Previous studies have suggested that the ancestral haplotype 18.2 (AH18.2) carries additional susceptibility gene to Type 1 Diabetes (T1D) on the Major Histocompatibility Complex (MHC). We analyzed 10 DR3/TNFa1b5 homozygous subjects in order to establish the conservation of the AH18.2 and then compared this conserved region with other DR3 haplotype, the AH8.1. The Illumina's HumanHap550 Bead chip was used to perform an extensive genotyping of the MHC region. The AH18.2 was highly conserved between DDR1 and HLA-DQA1 genes; therefore most probably the second susceptibility gene is located within this region. We can exclude the region centromeric to HLA-DRA gene and telomeric to DDR1 gene. A comparison between the AH18.2 and AH8.1 haplotypes showed that 233 SNPs were different in the aforementioned conserved region. These data suggest that the 1.65 Mb MHC region between DDR1 and HLA-DRA genes is likely to carry additional susceptibility alleles for T1D on the AH18.2 haplotype

Cross-sectional studies of patients with systemic lupus erythematosus (SLE) have demonstrated an association between activation of type I interferon (IFN) pathway and disease activity. This study examined longitudinal changes in IFN-regulated gene expression in peripheral blood using microarrays. A cross-section of 66 patients from the Autoimmune Biomarkers Collaborative Network SLE archive was evaluated. We also examined paired samples from a 15 patient subset collected during a period of low disease activity (Baseline) and at a subsequent flare event, and baseline scores of 29 patients who maintained low disease activity. IFN response (IFNr) scores were calculated from three IFN-regulated genes. Overall, higher IFNr scores were associated with increased disease activity. However, IFNr scores were not significantly different between the paired Baseline and Flare samples. An extended longitudinal analysis in 11 patients indicated little change in IFNr scores over time, even during dynamic disease activity. In patients with low disease activity, IFNr scores were not different between patients who experienced a subsequent flare and those who maintained low disease activity. In summary, although higher IFNr scores were associated with greater disease activity, IFNr scores of individual patients did not correlate with changes in disease severity or flare risk

OBJECTIVE: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares of disease activity and irreversible damage to multiple organ systems. An earlier study showed that SLE patients carrying an interferon (IFN) gene expression signature in blood have elevated serum levels of IFN-regulated chemokines. These chemokines were associated with more-severe and active disease and showed promise as SLE disease activity biomarkers. This study was designed to validate IFN-regulated chemokines as biomarkers of SLE disease activity in 267 SLE patients followed up longitudinally. METHODS: To validate the potential utility of serum chemokine levels as biomarkers of disease activity, we measured serum levels of CXCL10 (IFNgamma-inducible 10-kd protein), CCL2 (monocyte chemotactic protein 1), and CCL19 (macrophage inflammatory protein 3beta) in an independent cohort of 267 SLE patients followed up longitudinally over 1 year (1,166 total clinic visits). RESULTS: Serum chemokine levels correlated with lupus activity at the current visit (P = 2 x 10(-10)), rising at the time of SLE flare (P = 2 x 10(-3)) and decreasing as disease remitted (P = 1 x 10(-3)); they also performed better than the currently available laboratory tests. Chemokine levels measured at a single baseline visit in patients with a Systemic Lupus Erythematosus Disease Activity Index of < or =4 were predictive of lupus flare over the ensuing year (P = 1 x 10(-4)). CONCLUSION: Monitoring serum chemokine levels in SLE may improve the assessment of current disease activity, the prediction of future disease flares, and the overall clinical decision-making