Objective. To investigate whether smoking and HLA-DR shared epitope (SE) genes may interact in triggering immune reactions to citrulline-modified proteins.Methods. In a case-control study involving patients with recent-onset rheumatoid arthritis (RA), we studied interactions between a major environmental risk factor (smoking), major susceptibility genes included in the SE of HLA-DR, and the presence of the most specific autoimmunity known for RA (i.e., antibodies to proteins modified by citrullination). Immunostaining for citrullinated proteins in cells from bronchoalveolar lavage fluid was used to investigate whether smoking is associated with citrullination in the lungs.Results. Previous smoking was dose-dependently associated with occurrence of anticitrulline antibodies in RA patients. The presence of SE genes was a risk factor only for anticitrulline-positive RA, and not for anticitrulline-negative RA. A major geneenvironment interaction between smoking and HLA-DR SE genes was evident for anticitrulline-positive RA, but not for anticitrulline-negative RA, and the combination of smoking history and the presence of double copies of HLA-DR SE genes increased the risk for RA 21-fold compared with the risk among nonsmokers carrying no SE genes. Positive immunostaining for citrullinated proteins was recorded in bronchoalveolar lavage cells from smokers but not in those from nonsmokers.Conclusion. We identified an environmental factor, smoking, that in the context of HLA-DR SE genes may trigger RA-specific immune reactions to citrullinated proteins. These data thus suggest an etiology involving a specific genotype, an environmental provocation, and the induction of specific autoimmunity, all restricted to a distinct subset of RA.

An editorial in this issue explains that the degree of biological interaction between risk factors is measured as the deviation from additivity by the corresponding disease rates and not for example as deviation from multiplicativity. It is the purpose of this article to describe how a logistic regression model, or a Cox regression model, can be defined in order to produce the output that is needed for assessment of biological interaction. We will also demonstrate how common software can be programmed to deliver this output. Finally, we show how this output can be used as input in an Excel sheet that is set up to calculate the measures of biological interaction to be used for the assessment.

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

Gene-gene and gene-environment interactions are key features in the development of rheumatoid arthritis (RA) and other complex diseases. The aim of this study was to use and compare three different definitions of interaction between the two major genetic risk factors of RA--the HLA-DRB1 shared epitope (SE) alleles and the PTPN22 R620W allele--in three large case-control studies: the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) study, the North American RA Consortium (NARAC) study, and the Dutch Leiden Early Arthritis Clinic study (in total, 1,977 cases and 2,405 controls). The EIRA study was also used to analyze interactions between smoking and the two genes. "Interaction" was defined either as a departure from additivity, as interaction in a multiplicative model, or in terms of linkage disequilibrium--for example, deviation from independence of penetrance of two unlinked loci. Consistent interaction, defined as departure from additivity, between HLA-DRB1 SE alleles and the A allele of PTPN22 R620W was seen in all three studies regarding anti-CCP-positive RA. Testing for multiplicative interactions demonstrated an interaction between the two genes only when the three studies were pooled. The linkage disequilibrium approach indicated a gene-gene interaction in EIRA and NARAC, as well as in the pooled analysis. No interaction was seen between smoking and PTPN22 R620W. A new pattern of interactions is described between the two major known genetic risk factors and the major environmental risk factor concerning the risk of developing anti-CCP-positive RA. The data extend the basis for a pathogenetic hypothesis for RA involving genetic and environmental factors. The study also raises and illustrates principal questions concerning ways to define interactions in complex diseases.

Gene-environment associations are important in rheumatoid arthritis (RA) susceptibility, with an association existing between smoking, HLA- DRB1 'shared epitope' alleles, PTPN22 and antibodies to cyclic citrullinated peptides (CCP). Here, we test the hypothesis that a subset of the anti-CCP response, with specific autoimmunity to citrullinated alpha-enolase, accounts for an important portion of these associations. In 1,497 individuals from three RA cohorts, antibodies to the immunodominant citrullinated alpha-enolase CEP-1 epitope were detected in 43-63% of the anti-CCP-positive individuals, and this subset was preferentially linked to HLA-DRB1*04. In a case-control analysis of 1,000 affected individuals and 872 controls, the combined effect of shared epitope, PTPN22 and smoking showed the strongest association with the anti-CEP-1-positive subset (odds ratio (OR) of 37, compared to an OR of 2 for the corresponding anti-CEP-1-negative, anti-CCP-positive subset). We conclude that citrullinated alpha-enolase is a specific citrullinated autoantigen that links smoking to genetic risk factors in the development of RA.