Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritisopen access
- Authors
- Kwon, Young-Chang; Lim, Jiwoo; Bang, So-Young; Ha, Eunji; Hwang, Mi Yeong; Yoon, Kyungheon; Choe, Jung-Yoon; Yoo, Dae-Hyun; Lee, Shin-Seok; Lee, Jisoo; Chung, Won Tae; Kim, Tae-Hwan; Sung, Yoon-Kyoung; Shim, Seung-Cheol; Choi, Chan-Bum; Jun, Jae-Bum; Kang, Young Mo; Shin, Jung-Min; Lee, Yeon-Kyung; Cho, Soo-Kyung; Kim, Bong-Jo; Lee, Hye-Soon; Kim, Kwangwoo; Bae, Sang-Cheol
- Issue Date
- Nov-2020
- Publisher
- BMJ PUBLISHING GROUP
- Keywords
- arthritis; autoimmune diseases; genetic; polymorphism; rheumatoid
- Citation
- ANNALS OF THE RHEUMATIC DISEASES, v.79, no.11, pp.1438 - 1445
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANNALS OF THE RHEUMATIC DISEASES
- Volume
- 79
- Number
- 11
- Start Page
- 1438
- End Page
- 1445
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/8825
- DOI
- 10.1136/annrheumdis-2020-217663
- ISSN
- 0003-4967
- Abstract
- Objective
Genome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case–control population.
Methods
We newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.
Results
We identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10−8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.
Conclusion
This study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.
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