Background Rheumatoid arthritis (RA) is believed to have a multifactorial etiology, involving both genetic and environmental components, and can be divided into two major subsets based on the presence/absence of anti-citrullinated protein/peptide antibodies (ACPA). Smoking is the most established environmental risk factor. Despite progress from genome-wide association studies (GWAS), identified genetic variants only explain a small proportion of RA occurrence. Hypothetically, gene-environment interaction could add etiologic understanding of the disease.
Objectives The aim of current study was to investigate gene-environment interaction between smoking status and SNPs from an Immunochip, with selected SNPs of interest from an inflammatory point of view, for each of the two major RA subsets.
Methods Data from the Swedish EIRA case-control study was analyzed by means of logistic regression models. Information on smoking history was collected through questionnaires. An ever smoker was defined as a person who had ever smoked cigarettes before the index year, while a never smoker was defined as a person who had never smoked cigarettes. Genetic information was obtained from an Immunochip scan. Interaction between smoking and 133648 genetic markers that passed quality control were examined for the two RA subsets (1590 ACPA positive cases, 891 ACPA negative cases; compared with 1856 matched controls). Attributable proportion due to interaction together with 95% confidence interval was evaluated for each smoking-SNP pair. In order to replicate the findings, all the results were analyzed in a separate dataset from northern Sweden, Umeå.
Results For ACPA positive RA, 102 SNPs were found to significantly interact with smoking after Bonferroni correction, all located in the HLA region, and displayed high linkage disequilibrium (LD); 51 of them were replicated under a P-value of 0.05. After adjusting for HLA-DRB1 shared epitope, 15 SNPs remained significant for ACPA positive RA, with 13 of them being replicated. For ACPA negative RA, no smoking-SNP pair passed the threshold for significance. Through functional prediction and pathway annotation, 10 candidate genes/regions were identified for ACPA positive RA, several of them (HLA-DOB, HLA-DQA1, HLA-DQA2, HLA-DRA, HLA-DRB1, HLA-DRB5, TAP2) presented a network of antigen presentation pathways.
Conclusions Our study presents the most explicit picture to date, with regard to the patterns of gene-smoking interaction in ACPA positive/negative RA, and suggests fairly contrasting etiology of the two subsets. Our findings support the, by far, greatest influence from HLA-region on ACPA positive RA; Except for HLA-DR, the study additionally linked RA risk to the class I HLA, implicating involvement of cytotoxic T cells in RA pathogenesis.
Disclosure of Interest None Declared