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.
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Data availability
We deposited six sets of summary statistics (multi-ancestry, EUR-GWAS and EAS-GWAS for all RA and seropositive RA) to the GWAS Catalog under accession IDs GCST90132222, GCST90132223, GCST90132224, GCST90132225, GCST90132226 and GCST90132227. We deposited the PRS model (multi-ancestry PRS with CD4+ T cell T-bet IMPACT annotation) to the Polygenic Score (PGS) Catalog; the publication ID is PGP000357, and the score ID is PGS002745. Summary statistics and the PRS model are also available at https://data.cyverse.org/dav-anon/iplant/home/kazuyoshiishigaki/ra_gwas/ra_gwas-10-28-2021.tar. The UK Biobank analysis was conducted via application no. 47821.
Code availability
The codes are available at our website (https://github.com/immunogenomics/RA_GWAS) and archived in Zenodo (https://doi.org/10.5281/zenodo.6999289).
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Acknowledgements
We thank the Director of Health Malaysia for supporting the work described in the South Asian (SAS) population: the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) study. The MyEIRA study was funded by grants from Ministry of Health Malaysia (NMRR-08-820-1975) and the Swedish National Research Council (DNR-348-2009-6468). The GENRA study and the CARDERA genetics cohort genotyping were funded by Versus Arthritis (grant reference 19739 to I.C.S.). The Nurses’ Health Study (NHS cohort) is funded by the National Institutes of Health (NIH) (R01 AR049880, UM1 CA186107, R01 CA49449, U01 CA176726 and R01 CA67262). The Swedish EIRA study was supported by the Swedish Research Council (to L.K., L.P. and L.A.). S.S. was in part supported by the Mochida Memorial Foundation for Medical and Pharmaceutical Research, Kanae Foundation for the Promotion of Medical Science, Astellas Foundation for Research on Metabolic Disorders, JCR Grant for Promoting Basic Rheumatology, and Manabe Scholarship Grant for Allergic and Rheumatic Diseases. I.C.S. is funded by the National Institute for Health and Care Research (NIHR) Advanced Research Fellowship (grant reference NIHR300826). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. K.A.S. is supported by the Sherman Family Chair in Genomic Medicine and by a Canadian Institutes for Health Research Foundation Grant (FDN 148457) and grants from the Ontario Research Fund (RE-09-090) and Canadian Foundation for Innovation (33374). S.-C.B. is supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2021R1A6A1A03038899). R.P.K. and J.C.E. are funded by NIH (UL1 TR003096). C.M.L. is partly funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. T. Arayssi was partially supported by the National Priorities Research Program (grant 4-344-3-105 from the Qatar National Research Fund, a member of Qatar Foundation). M. Kerick and J.M. are funded by Rheumatology Cooperative Research Thematic Network program RD16/0012/0013 from the Instituto de Salud Carlos III (Spanish Ministry of Science and Innovation). Y.O. is funded by JSPS KAKENHI (19H01021 and 20K21834), AMED (JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006 and JP21km0405217), JST Moonshot R&D (JPMJMS2021 and JPMJMS2024), Takeda Science Foundation, and the Bioinformatics Initiative of Osaka University Graduate School of Medicine. Y. Kochi is funded by grants from Nanken-Kyoten, TMDU and Medical Research Center Initiative for High Depth Omics. S.R. is supported by UH2AR067677, U01HG009379, R01AR063759 and U01HG012009.
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K. Ishigaki, S.S., C.T., Y.O. and S.R. conceived and designed the study. K. Ishigaki wrote the manuscript with critical input from S.S., C.T., Y.L., Y.O. and S.R. K. Ishigaki conducted meta-analysis and all GWAS downstream analyses with the help of S.S., C.T., T. Amariuta, Y.L., Y.O. and S.R. K. Yamaguchi and Y. Kochi conducted PADI4 long-read sequencing and PADI4 sQTL analysis. M. Koido, K.T., Y. Kamatani and C.T. contributed to construction of the population-specific reference panel. K. Ishigaki, S.S., C.T., K.S., V.A.L., I.C.S., S.V., D.P., J.B., G.X., J.Z., C.I.A., E.K., R.J.C., K.A.S., M. Kerick, F.M., M. Traylor, C.M.L., H.X., R.S., T. Arayssi, J.M., L.K., Y.O. and S.R. conducted GWAS analyses. C.T., C.L.T., V.A.L., S.V., M. Takahashi, X.W., L.L., T.L., D.P., A.B., G.O., J.B., S.M., K.P.L., R.J.C., E.W.K., K. Matsuo, F.M., S.E., H.X., K. Ikari, P.K.G., L.P., Y.O. and S.R. contributed to genotyping experiments. C.T., K.S., C.L.T., V.A.L., I.C.S., S.V., K.O., A.M., M.H., H.I., M. Hammoudeh, S.A.E., B.K.M., H.H., H.B., I.W.U., X.W., L.L., T.L., D.P., A.B., G.O., S.M.M.V., A.J.M., S.H., H.T., E.T., A.S., Y.M., Kenichi Yamamoto, S.M., G.X., J.Z., C.I.A., E.K., G.W., I.v.d.H.-B., J.C., K.P.L., R.J.C., H.-S.L., S.-Y.B., K.A.S., N.d.V., L.A., S.R.-D., E.W.K., S.-C.B., R.P.K., J.C.E., X.M., T.H., P.D., M.S., M. Kerick, J.C.D., The BioBank Japan Project, K. Matsuda, K. Matsuo, T.M., F.M., K.F., Y.T., A.K., C.M.L., S.E., H.X., R.S., T. Arayssi, K. Ikari, M. Harigai, P.K.G., Kazuhiko Yamamoto, S.L.B., L.P., J.M., L.K., Y.O. and S.R. contributed to the collection of samples and management of genotype data and clinical information. J.C.D.’s involvement in this project was primarily as faculty at Vanderbilt University Medical Center prior to joining the NIH.
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Extended data
Extended Data Fig. 1 PCA and UMAP plot of 1KG Phase 3.
a, We projected each individual’s imputed genotype into a PC space, which was calculated using all individuals in 1KG Phase 3. b, We further conducted UMAP analysis using the top 20 PC scores of all GWAS samples and all individuals in 1KG Phase 3. We plotted all samples in 1KG Phase 3 or plotted samples in each ancestry separately. UMAP plot of all GWAS samples is provided in Fig. 1c.
Extended Data Fig. 2 Effect size heterogeneity between seropositive and seronegative RA.
a, Odds ratio and its 95% confidence interval of seropositive and seronegative RA are plotted. Among the lead variants at 122 significant autosomal loci, we plotted 118 variants common in EUR of 1KG Phase 3 (MAF > 0.01). We present the results from multi-ancestry GWAS and EUR-GWAS. Effect size heterogeneity was assessed in the multi-ancestry GWAS results by Cochran’s Q test (Phet). Pearson’s correlation data are provided. b, Differences (delta) of effect size magnitude between seropositive and seronegative RA. A positive delta indicates a larger magnitude of effect size in seropositive RA than seronegative RA. We defined the standard error (s.e.) of delta by the following formula: \(\sqrt {S.E._{seropositive}^2 + S.E._{seronegative}^2}\). We provided 2 × s.e. of delta as error bars. In seropositive RA GWAS, we had 27,448 cases and 240,149 controls; in seronegative RA GWAS, we had 4,515 cases and 240,149 controls.
Extended Data Fig. 3 Enrichment of high PIP variants within open chromatin regions of 18 blood cell populations.
Enrichment of high PIP variants within open chromatin regions of 18 blood cell populations was analyzed by gchromVAR software. The horizontal dashed line indicates Bonferroni corrected P value threshold (0.05/18 = 0.0027). P values were estimated by the enrichment test implemented in gchromVAR.
Extended Data Fig. 4 Conditional analysis results at the IL2RA and TNFAIP3 loci.
a,b, Conditional analysis was conducted in each cohort, and the results were meta-analyzed using the inverse-variance weighted fixed effect model (a, IL2RA locus; b, TNFAIP3 locus). We used multi-ancestry GWAS results. Results at the TYK2 locus are provided in Extended Data Fig. 6. Variants in LD with the lead variant (r2 > 0.6 both in EUR and EAS ancestries) in each round of conditional analysis are highlighted by different colors.
Extended Data Fig. 5 Haplotype-level association test at the IL6R locus.
Haplotype-level association results at the IL6R locus. We defined haplotypes as shown in the table, and we estimated the dosage of four haplotypes using phased imputed genotype data. We conducted multivariate logistic regression tests in each of EUR cohorts (n = 97,173 in total), and the results were meta-analyzed using the inverse-variance weighted fixed effect model. The effect size estimate and 2 x s.e. are shown in the right panel.
Extended Data Fig. 6 Three ancestry-specific signals at the TYK2 locus.
a,b, Conditional analysis was conducted in each cohort, and the results were meta-analyzed using the inverse-variance weighted fixed effect model (a, EUR-GWAS; b, EAS-GWAS). Variants in LD with the lead variant (r2 > 0.6 in EUR or EAS ancestries) in each round of conditional analysis are highlighted by different colors.
Extended Data Fig. 7 S-LDSC analysis using histone mark annotations.
a, The estimate and its 95% confidence interval of the heritability proportion explained by 396 histone mark annotations are provided. Confidence intervals and the P values indicating non-negative tau were estimated via block-jackknife implemented in the LDSC software (one-sided test). When a heritability enrichment is significant (P < 0.05/396 = 1.3 ×10−4), that annotation is colored by the type of GWAS. b, The best histone mark annotation (H3K4me1 in PMA-I stimulated primary CD4+ T cells) and the best IMPACT annotation (CD4+ T cell T-bet) were jointly modeled in S-LDSC analysis. Tau estimate (per variant heritability in each annotation) normalized by total per variant heritability are provided as the centre, and its 95% confidence interval are provided as error bars. EUR-GWAS results were used. c, P values were calculated by block-jackknife implemented in the LDSC software and indicate the significance of non-negative tau (per variant heritability) of each annotation (one-sided test). Each histone mark annotation is colored by its cell type category. Horizontal dashed line indicates Bonferroni-corrected P-value threshold (0.05/396 = 1.3 ×10−4). Top panel shows the results without controlling the effect of the best IMPACT annotation (CD4+ T cell T-bet), and the bottom panel shows the results with controlling for this effect.
Extended Data Fig. 8 PRS performance comparison between the previous RA GWAS and this GWAS.
The liability scale R2 in the 15 cohorts not included in the previous RA GWAS. We used the multi-ancestry GWAS results reported in Okada et al. and this GWAS to develop PRS models. We used the LOCO approach to evaluate the PRS model based on this GWAS. The differences were assessed by two-sided paired Wilcoxon text (n = 15). Within each boxplot, the horizontal lines reflect the median, the top and bottom of each box reflect the interquartile range (IQR), and the whiskers reflect the maximum and minimum values within each grouping no further than 1.5 × IQR from the hinge.
Extended Data Fig. 9 PRS performances in different ancestral groups.
PRS performances (liability scale R2) are shown for each combination of PRS models and cohort groups for which the PRS was applied (n = 25, 8, and 4, respectively, from the left). The differences between the group with the best performance and the second best were analyzed by two-sided paired Wilcoxon test. Within each boxplot, the horizontal lines reflect the median, the top and bottom of each box reflect the interquartile range (IQR), and the whiskers reflect the maximum and minimum values within each grouping no further than 1.5 × IQR from the hinge. We used the LOCO approach where applicable.
Extended Data Fig. 10 PRS distribution differences between cases and controls.
PRS distribution differences between cases and controls. Multi-ancestry PRS with CD4+ T cell T-bet IMPACT annotation was used. We used the LOCO approach. In each cohort, PRS was scaled using mean and s.d. of the control samples, and individual level data were merged across cohorts in an ancestry group.
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Ishigaki, K., Sakaue, S., Terao, C. et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat Genet 54, 1640–1651 (2022). https://doi.org/10.1038/s41588-022-01213-w
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DOI: https://doi.org/10.1038/s41588-022-01213-w
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