Session Title: Genetics and Genomics of Rheumatic Diseases
Session Type: Abstract Submissions (ACR)
Background/Purpose: Rheumatoid arthritis (RA) is believed to have a multifactorial etiology, involving both genetic and environmental components, and can be divided into two major subsets according to 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. The aim of current study was to investigate gene-environment interaction between smoking 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 Swedish EIRA case-control study was analyzed by means of logistic regression models. Smoking history was collected through questionnaires. Heavy smoking was defined as more than 10 pack-years. Genetic information was obtained from an Immunochip scan. Interaction between smoking intensity 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.
Results: For ACPA positive RA, 390 SNPs were found to significantly interact with heavy smoking after Bonferroni correction, with a majority located in the HLA region (328 out of 390, 84.10%), all of which displayed high linkage disequilibrium (LD); for ACPA negative RA, 56 SNPs passed threshold for significance, most located outside the HLA region (51 out of 56, 91.07%). After adjusting for HLA-DRB1 shared epitope (SE), 37 SNPs remained significant for ACPA positive RA, with 17 (45.95%) confined to HLA region and the rest spread across 9 other chromosomes; for ACPA negative RA, 19 SNPs stood out, all of them outside the HLA region. Through functional prediction and pathway annotation, 24 candidate genes/regions were identified for ACPA positive RA, several of them (C6orf10, GRB10, HCG9, TAP2, PPT2, HLA-E, SMAD3) together with HLA-DR presented a network of antigen presentation pathways; for ACPA negative RA, 13 genes were demonstrated, 6 of them (GP1BA, AFF3, ICOSLG, NOTCH2, TGS1, LYN) constitute T helper cell differentiation pathways. For ACPA positive RA, besides those SNPs in LD with known susceptibility variant at HLA-DRB1, none of the others have previously been identified.
Conclusion: Our study presents the most explicit picture to date, with regard to the patterns of gene-smoking interaction in ACPA positive/negative RA, suggesting fairly contrasting etiology of the two subsets. Our findings support the, by far, greatest influence from HLA-region on ACPA positive RA; while for ACPA negative RA, more genes outside HLA-region contribute to the etiology. Noticeably, for both RA subsets, new SNPs that are not significant in association analyses stand out in interaction analyses, indicating that genetic factors should be considered together with environmental factors in studies of RA etiology.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/a-genome-wide-interaction-study-with-smoking-suggests-new-risk-loci-for-two-different-subsets-of-rheumatoid-arthritis-results-from-swedish-epidemiological-investigation-of-rheumatoid-arthritis-study/