Inflammatory bowel diseases (IBDs) are chronic disorders of the gastrointestinal tract with the following two subtypes: Crohn’s disease (CD) and ulcerative colitis (UC). To date, most IBD genetic associations were derived from individuals of European (EUR) ancestries. Here we report the largest IBD study of individuals of East Asian (EAS) ancestries, including 14,393 cases and 15,456 controls. We found 80 IBD loci in EAS alone and 320 when meta-analyzed with ~370,000 EUR individuals (~30,000 cases), among which 81 are new. EAS-enriched coding variants implicate many new IBD genes, including ADAP1 and GIT2. Although IBD genetic effects are generally consistent across ancestries, genetics underlying CD appears more ancestry dependent than UC, driven by allele frequency (NOD2) and effect (TNFSF15). We extended the IBD polygenic risk score (PRS) by incorporating both ancestries, greatly improving its accuracy and highlighting the importance of diversity for the equitable deployment of PRS.
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CaVEMaN and DAP-G GTEx v8 fine-mapping cis-eQTL data were retrieved from https://gtexportal.org/home/datasets#filesetFilesDiv15. 1000 Genomes Project Phase 3 is available from https://www.internationalgenome.org/category/phase-3/. TOPMed reference panel R2 is available from https://imputation.biodatacatalyst.nhlbi.nih.gov/#!. Human Genome Diversity Project is available from https://www.internationalgenome.org/data-portal/data-collection/hgdp. Simons Genome Diversity Project is available from https://www.simonsfoundation.org/simons-genome-diversity-project/. Korean Personal Genome Diversity Project is available from http://opengenome.net/Main_Page. NBDC human database (accession ID: JGAS000114) is available from https://humandbs.biosciencedbc.jp/en/. STRING functional protein association networks are available from https://string-db.org/. NFE summary statistics are from ftp://ftp.sanger.ac.uk/pub/project/humgen/summary_statistics/human/2016-11-07/. FIN summary statistics are from FinnGen R7, https://www.finngen.fi/en/access_results. PRS weights and genome-wide summary statistics for the meta-analyzed EAS samples and across all study samples (EAS and EUR) can be downloaded from https://www.ibdgenetics.org. Individual-level genotype data for EAS samples are available upon request: SHA1, Z.L. (firstname.lastname@example.org); KOR1, K.S. (email@example.com); JPN1, Y. Kakuta (firstname.lastname@example.org) and ICH1, IIBDGC (email@example.com). Access to individual-level genotypes from samples recruited within mainland China is subject to the policies and approvals from the Human Genetic Resource Administration, Ministry of Science and Technology of the People’s Republic of China.
Computer code relating to this study includes:
RICOPILI v2019_Jun_25.001: https://sites.google.com/a/broadinstitute.org/ricopili
EIGENSTRAT v6.1.4: PCA, https://github.com/DReichLab/EIG/tree/master/EIGENSTRAT
bcftools v1.11: http://samtools.github.io/bcftools/bcftools.html
LDSC v1.0.1: https://github.com/bulik/ldsc
S-LDXR v0.3-beta: https://huwenboshi.github.io/s-ldxr
HRC-1000G-check-bim v4.3.0: https://www.well.ox.ac.uk/~wrayner/tools/HRC-1000G-check-bim-v4.3.0.zip
VcfCooker v1.1.1: https://genome.sph.umich.edu/wiki/VcfCooker
Eagle2 v2.4.1: https://alkesgroup.broadinstitute.org/Eagle/
Minimac4 v1.0.0: https://genome.sph.umich.edu/wiki/Minimac4
apt software v220.127.116.11: https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/microarray-analysis-partners-programs/affymetrix-developers-network/affymetrix-power-tools.html
SNPolisher v3.0: https://downloads.thermofisher.com/SNPolisher_3.0.zip
BEAGLE v5.1: https://faculty.washington.edu/browning/beagle/b5_1.html
PLINK2 v2.00a3.6: https://www.cog-genomics.org/plink/2.0
METAL v2011-03-25: https://genome.sph.umich.edu/wiki/METAL
MANTRA v1: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3460225/
COJO v1.92.2beta: https://yanglab.westlake.edu.cn/software/gcta/#COJO
PRS-CS v1.0.0: https://github.com/getian107/PRScs
PRS-CSx v1.0.0: https://github.com/getian107/PRScsx
Python implementation for SuSiE: https://github.com/getian107/SuSiEx
FinnGen QC and Association analysis: https://finngen.gitbook.io/documentation/methods/genotype-imputation/genotype-data
FinnGen GWAS: https://finngen.gitbook.io/documentation/methods/phewas
IEU open GWAS project: https://gwas.mrcieu.ac.uk/phewas/
VEP v104.3: https://useast.ensembl.org/info/docs/tools/vep/index.html
Cytoscape v3.9.1: https://cytoscape.org/
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Z.L. acknowledges support from the National Natural Science Foundation of China (91942312, 81630017). H.H. acknowledges support from NIDDK K01DK114379, NIDDK R01DK129364 and the Stanley Center for Psychiatric Research. M.L. acknowledges support from the National Natural Science Foundation of China (81870389, 82070565). Y. Kakuta and Y. Kinouchi acknowledge support from JSPS KAKENHI (21K07884, 21K07955), the Japan Agency for Medical Research and Development (AMED) (JP18kk0305002) and Labour Sciences Research Grants for Research on Intractable Diseases from the Ministry of Health, Labour and Welfare of Japan. Y. Kakuta, Y. Kawai, K.T. and M.N. acknowledge support from AMED (JP19km0405501). K.S. acknowledges support from the National Research Foundation of Korea (2017R1A2A1A05001119, 2020R1A2C2003275). J.C. acknowledges support from NIDDK U24DK062429 and NIDDK U01DK062422. D.P.B.M. acknowledges the Leona M. and Harry B. Helmsley Charitable Trust and NIDDK U01DK062413. K.T. and M.N. acknowledge support from AMED (JP19km0405205). Part of the computations on JPN1 was performed on the NIG supercomputer at the ROIS National Institute of Genetics. Computations on SHA1 were performed in a supercomputing environment at the Digital Health China Technologies Corp. Ltd. We want to acknowledge the participants and investigators of the FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd., Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., Merck Sharp & Dohme Corp, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc., Novartis AG and Boehringer Ingelheim. The following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank (www.auria.fi/biopankki), THL Biobank (www.thl.fi/biobank), Helsinki Biobank (www.helsinginbiopankki.fi), Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www.ita-suomenbiopankki.fi/en), Central Finland Biobank (www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www.veripalvelu.fi/verenluovutus/biopankkitoiminta) and Terveystalo Biobank (www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish Biobanks are members of BBMRI.fi infrastructure (www.bbmri.fi). Finnish Biobank Cooperative-FINBB (https://finbb.fi/) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious services (https://site.fingenious.fi/en/) managed by FINBB.
W.S. and C.S. are employees of Digital Health China Technologies Corp. Ltd. M.J.D. is a founder of Maze Therapeutics. D.P.B.M. has received consultancy fees from Prometheus Biosciences, Prometheus Laboratories, Takeda, Gilead, Pfizer. Stock—Prometheus Biosciences. B.D.Y. has served on advisory boards for AbbVie Korea, Celltrion, Daewoong Pharma, Ferring Korea, Janssen Korea, Pfizer Korea and Takeda Korea; has received research grants from Celltrion and Pfizer Korea; has received consulting fees from Chong Kun Dang Pharm., CJ Red BIO, Cornerstones Health, Daewoong Pharma, IQVIA, Kangstem Biotech, Korea United Pharm. Inc., Medtronic Korea, NanoEntek and Takeda; and has received speaking fees from AbbVie Korea, Celltrion, Ferring Korea, IQVIA, Janssen Korea, Pfizer Korea, Takeda and Takeda Korea. H.H. received consultancy fees from Ono Pharmaceutical and an honorarium from Xian Janssen Pharmaceutical. The remaining authors declare no competing interests.
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Extended Data Fig. 1 Quantile-Quantile plots for IBD genetic associations.
λ, genomic inflation factor; λ1000, scaled inflation factor for an equivalent study of 1,000 cases and 1,000 controls. The dots indicate variants. Shaded area indicates the 95% confidence interval under the null distribution. a-c, SHA1. d-f, ICH1 (only the designated null variants in ImmunoChip were used). g-i, KOR1. j-l, JPN1. m-o, Meta-analysis including all EAS samples (SHA1, ICH1, KOR1, and JPN1). p-r, FIN. a, d, g, j, m, and p are for CD. b, e, h, k, n, and q are for UC. c, f, i, l, o, and r are for IBD.
Extended Data Fig. 2 Index variants in the 16 new IBD loci in EAS.
a, Minor allele frequency (MAF) taken from 1000 Genomes EAS and EUR reference panels, respectively. b, P-value in respective studies.
Extended Data Fig. 3 Comparison between the fixed-effect (FE) meta-analysis and MANTRA.
Index variants in loci identified by either FE or MANTRA were plotted. For FE, we used genome-wide significance threshold of 5 × 10−8, and for MANTRA, we used the Bayes Factor threshold of 106, plotted as the vertical and horizontal lines respectively. P, P-value from FE. BF, Bayes factor from MANTRA.
Extended Data Fig. 4 IBD gene network.
IBD gene network was created using the STRING functional protein association networks and clustered using Community Clustering Glay (Methods). For clusters with more than two genes or with new IBD genes, the top three significantly enriched pathways were shown if false-discovery rate (FDR) < 0.05. New, nearest genes to the index variants in new IBD loci or new genes in Table 2 (boldfaced). Known, nearest genes to the index variants in known IBD loci. Index, nearest genes to the index variants in IBD loci except for those in Table 2. Tier, genes in Table 2.
Extended Data Fig. 5 Comparative genetic architecture within EAS.
a, SNP-based heritability in the liability scale with the prevalence in its respective population or the European population. b, Genetic correlation (rg). For a and b, the sample size used to derive SHA1, KOR1 and JPN1 h2 and their rg were 8,831, 6,038 and 2,624 for CD, and 8,679, 5,988 and 2,803 for UC, respectively. Results are plotted as mean value ± 95% confidence interval (error bar).
Extended Data Fig. 6 Quantile-Quantile plots for the heterogeneity test within EAS.
a, b, CD. c, d, UC. e, f, IBD. a, c, e, Genome-wide variants including the MHC locus. b, d, f, Genome-wide variants excluding the MHC locus. Cochran’s Q-test, two-sided, was used for the heterogeneity test. The dots indicate variants. Shaded area indicates the 95% confidence interval under the null distribution.
Extended Data Fig. 7 Enrichment of squared genetic correlation stratified across genomic annotations.
No significant enrichment or depletion (deviation from 1) was observed after Bonferroni corrections. Results are plotted as mean value ± 95% confidence interval before multiple testing corrections (error bar).
Extended Data Fig. 8 Variance explained for IBD associations across EUR and EAS.
We included all loci from Supplementary Table 8. For loci with fine-mapping analyses performed, we used the conditional OR (using COJO, Methods) for variants with the highest PIP in each credible set to account for multiple independent associations. We took fine-mapping results from ref. 12 for EUR and from this study for EAS. For loci with no fine-mapping results, we used the index variant (variant with the most significant P-value) as the proxy for the loci. We only plotted associations that have variance explained > 0.3% in either EAS or EUR. Different MAF is defined as Fst > 0.01, and different OR is defined as heterogeneity test P-value < 0.05 after Bonferroni correction. Because the heterogeneity test was corrected using a higher multiple testing burden, the significance for a handful of loci, for example, RNF186, can be different from Fig. 3c. Nearest genes to the associations were used as labels for associations when the text space is available.
Extended Data Fig. 9 Difference between variance explained for CD and UC across EUR and EAS.
Index variants from Supplementary Table 8 were plotted. Difference between variance explained was calculated as variance explained of CD − variance explained of UC.
Extended Data Fig. 10 Polygenic risk prediction on Chinese, Korean and Japanese study participants.
a, Leave-one-country-out strategy was performed to test the performance of PRS on SHA1 (Chinese), KOR1 (Korean), and JPN1 (Japanese) individuals, respectively. The prediction accuracy was measured as R2 on the liability scale using the population prevalence (Methods). For the testing cohort, we randomly split study participants into validation and testing 100 times (Methods). All other EAS cohorts were used as discovery. Results are plotted as mean value ± 95% confidence interval of R2 across the 100 replicates (error bar). b, Effective sample size of training datasets calculated as 4/(1/ncase + 1/ncontrol).
Supplementary Tables 1–13.
Supplementary Data 1
Manhattan plot for IBD genetic associations.
Supplementary Data 2
Regional association plot for IBD-associated loci in EAS.
Supplementary Data 3
Forest plot for index variants in IBD-associated loci.
Supplementary Data 4
Forest plot for putative causal variants identified in EAS and in EUR.
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Liu, Z., Liu, R., Gao, H. et al. Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries. Nat Genet 55, 796–806 (2023). https://doi.org/10.1038/s41588-023-01384-0