Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis

Kazuyoshi Ishigaki, Saori Sakaue, Chikashi Terao, Yang Luo, Kyuto Sonehara, Kensuke Yamaguchi, Tiffany Amariuta, Chun Lai Too, Vincent A. Laufer, Ian Scott, Sebastien Viatte, Meiko Takahashi, Koichiro Ohmura, Akira Murasawa, Motomu Hashimoto, Hiromu Ito, Mohammed Hammoudeh, Samar Al Emadi, Basel K. Masri, Hussein HalabiHumeira Badsha, Imad W. Uthman, Xin Wu, Li Lin, Ting Li, Darren Plant, Anne Barton, Gisela Orozco, Suzanne M. M. Verstappen, John Bowes, Alexander J. MacGregor, Suguru Honda, Masaru Koido, Kohei Tomizuka, Yoichiro Kamatani, Hiroaki Tanaka, Eiichi Tanaka, Akari Suzuki, Yuichi Maeda, Kenichi Yamamoto, Satoru Miyawaki, Gang Xie, Jinyi Zhang, Christopher Amos, Edward Keystone, Gertjan Wolbink, Irene van der Horst-Bruinsma, Jing Cui, Katherine Liao, Robert J. Carroll, Hye-Soon Lee

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49 Citations (Scopus)

Abstract

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10 −8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.

Original languageEnglish
Pages (from-to)1640-1651
Number of pages12
JournalNature Genetics
Volume54
Issue number11
DOIs
Publication statusPublished - 4 Nov 2022

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