Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases

Abstract

The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (P < 9.58 × 10−9). East Asian–specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease) and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.

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Fig. 1: Disease-associated loci detected in this GWAS.
Fig. 2: Novel associations that can be explained by East Asian–specific missense variants.
Fig. 3: A novel suggestive association of cerebral aneurysm can be explained by artery-specific eQTL signals for ATP2B1.
Fig. 4: TFs whose binding sites were enriched for heritability of diseases.

Data availability

GWAS summary statistics of the 42 diseases are publicly available from our website (JENGER; http://jenger.riken.jp/en/) and the National Bioscience Database Center Human Database (https://humandbs.biosciencedbc.jp/en/) (research ID: hum0014) without any access restrictions. GWAS genotype data for case samples were deposited at the National Bioscience Database Center Human Database (research ID: hum0014).

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Acknowledgements

We acknowledge the staff of the BBJ for their outstanding assistance. We express our heartfelt gratitude to the Tohoku Medical Megabank Organization (Tohoku University), Iwate Tohoku Medical Megabank Organization (Iwate Medical University), Japan Public Health Center-based Prospective Study, Japan Multi-Institutional Collaborative Cohort Study and NCGG Biobank for their invaluable contributions to collecting control samples. We also express our gratitude to the OACIS for contributing to the replication study of CAD, and the National Cancer Center Hospital for contributing to the replication study of lung cancer. We also express our gratitude to E.K. and H.S. for kindly sharing their results of chromatin immunoprecipitation sequencing data analysis. We extend our appreciation to Y. Yukawa, Y. Yokoyama and other members of the Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences for great support. This research was supported by the Tailor-Made Medical Treatment Program (BBJ) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Agency for Medical Research and Development (AMED) under grant numbers JP17km0305002 (to M.Kubo) and JP17km0305001 (to M.M., S.Nagayama, Y.D., Y.Miki, T.Katagiri, O.O., W.O., H.I., T.Yoshida, I.I., T.Takahashi, J.I. and K.M.), JST KAKENHI grants (18H02932 to S.I.) and the Research Program on Hepatitis from AMED (JP19fk0310109 and JP19fk0210020 to K.C.). The Tohoku Medical Megabank Organization (Tohoku University) was supported in part by MEXT-JST and AMED, the most recent grant numbers being JP19km0105001 and JP19km0105002 (to M.Y.). The Iwate Tohoku Medical Megabank Organization (Iwate Medical University) was supported in part by MEXT-JST and AMED, the most recent grant numbers being JP19km0105003 and JP19km0105004. The Japan Public Health Center Prospective Study has been supported by the National Cancer Center Research and Development Fund since 2011 (latest grant number: 29-A-4, to S.T.) and was supported by a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan from 1989–2010. The Japan Multi-Institutional Collaborative Cohort Study was supported by Grants-in-Aid for Scientific Research for Priority Areas of Cancer (17015018 to Nobuyuki Hamajima) and Innovative Areas (221S0001 to Hideo Tanaka) and by Japan Society for the Promotion of Science KAKENHI grants (CoBiA and 16H06277) from MEXT (to Hideo Tanaka and K.W.). The NCGG study was partly supported by AMED under grant number JP18kk0205009 (S.Niida) and JP20dk0207045 (K.O.). The OACIS was supported by AMED (JP19ek0210081 to Yasuhiko Sakata). The lung cancer study at the National Cancer Center Hospital was supported by the National Cancer Center Research and Development Fund (NCC Biobank), AMED (JP16ck0106096 to T.Kohno) and the Ministry of Health, Labour and Welfare program (H29-Gantaisaku-Ippann-025 to T.Kohno). The study at Fujita Health University was supported by AMED under grant numbers JP20dm0107097 (M.Ikeda and N.I.), JP20km0405201 (N.I.) and JP20km0405208 (M.Ikeda).

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Contributions

K. Ishigaki wrote the manuscript with critical input from S.R. and Y. Kamatani. K. Ishigaki conducted all of the bioinformatics analyses with the help of M.A., M. Kanai, A.T., S.S., N. Matoba, S.-K.L., Y.O., C. Terao, T.A., S.G., S.R. and Y. Kamatani. Y. Momozawa and M. Kubo performed the genotyping. H.S. and E.K. analyzed the chromatin immunoprecipitation sequencing data. K. Ito, S.K., K.O., S. Niida, Yasushi Sakata, Yasuhiko Sakata, T. Kohno and K. Shiraishi contributed to the replication studies in a Japanese population. S.-K.L., Y. Kochi, M. Horikoshi, Ken Suzuki, K. Ito, M. Hirata, K.M., S.I., I.K., T. Tanaka, H.N., A. Suzuki, T.H., M.T., K.C., D.M., M.M., S. Nagayama, Y.D., Y. Miki, T. Katagiri, O.O., W.O., H.I., T. Yoshida, I.I., T. Takahashi, C. Tanikawa, T.S., N. Sinozaki, S. Minami, H. Yamaguchi, S.A., Y.T., K. Yamaji, K. Takahashi, T.F., R.T., H. Yanai, A.M., Y. Koretsune, H.K., M. Higashiyama, S. Murayama, K. Yamamoto, Y. Murakami, Y.N., J.I., T. Yamauchi, T. Kadowaki, M. Kubo and Y. Kamatani contributed to management of the BBJ data. M. Ikeda and N.I. managed the other GWAS data. N. Minegishi, Kichiya Suzuki, K. Tanno, A.Shimizu, T. Yamaji, M. Iwasaki, N. Sawada, H.U., K. Tanaka, M.N., M.S., K.W., S.T. and M.Y. contributed to management of the cohort control data.

Corresponding authors

Correspondence to Soumya Raychaudhuri or Johji Inazawa or Toshimasa Yamauchi or Takashi Kadowaki or Michiaki Kubo or Yoichiro Kamatani.

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Extended data

Extended Data Fig. 1 Study design of this GWAS.

a, Study designs in this GWAS. Study design 1 (top) was used in the main analysis. An example of study design 1 is provided; in GWAS of disease 3, we included all other patients (except those have related diseases) into control group. The definition of related diseases is provided in Supplementary Table 1. Study design 2 (bottom) was used to discuss the appropriateness of study design selection. b, Effect size estimates and S.E. at the 309 autosomal disease-associated variants detected in sex-combined analysis (P < 5 × 10−8). We compared the effect size estimates in study design 1 with those in study design 2. Heterogeneity between two studies was tested using Cochran’s Q test. The identity line is shown in blue. The red dot (rs373205748 associated with arrhythmia) indicates a variant with significant heterogeneity in effect size estimates between two study designs (P = 0.00012 < 0.05/309).

Extended Data Fig. 2 Replication analysis of previous GWAS findings using this GWAS results.

We compared effect sizes reported in the previous GWAS with those in this GWAS. Effect size and S.E. are shown. The identity line is shown in blue. The sample size of GWAS is provided in Table 1. We utilized a generalized linear mixed model in our GWAS.

Extended Data Fig. 3 Low allele frequency might contribute to replication failure.

We first compared effect sizes reported in the previous GWAS with those in our GWAS (Supplementary Table 3 and Extended Data Fig. 2); 1,219 out of 1,396 previously reported risk alleles were replicated with the same effect direction (177 alleles were not replicated). We compared MAF of replicated variants (n = 1,219) and MAF of not replicated variants (n = 177). Mann-Whitney U test P value is provided (two-sided test).

Extended Data Fig. 4 Permutation test to estimate appropriate P value threshold to control type I errors.

Using 1,000 simulated binary phenotypes with down-sampled samples (n = 10,000), we conducted GWAS utilizing the same strategy as used in the main analysis. a, The distribution of minimum P values in each phenotype (Pmin). The 95-th percentile of Pmin was 2.87 × 10−8. The 95% confidence interval was estimated by 1,000 bootstraps. b, The distributions of Pmin using all samples (n = 198,137) and those using 10,000 samples. To increase computational efficiency, we restricted this analysis to imputed genotype data in chromosome 22. For this analysis in b, we utilized Plink2.

Extended Data Fig. 5 Allele frequency comparison between novel and known disease-associated variants.

MAF comparison at disease-associated variants at novel (n = 41) and known loci (n = 153) with suggestive significance (P < 5 × 10−8) (a, East Asian populations; b, European populations in 1KG phase3). For known loci, we restricted this analysis to loci where the closest reported variants were discovered by GWAS in European populations. Mann-Whitney U test P value is provided (two-sided test).

Extended Data Fig. 6 A novel association which can be explained by an East Asian-specific missense variant.

A regional association plot for keloid (812 cases vs 211,641 controls) at the PHLDA3 region is provided. We utilized a generalized linear mixed model in our GWAS.

Extended Data Fig. 7 The association of p.V326A of POT1 for all diseases in this GWAS.

Effect size and S.E. are provided for neoplastic diseases (a) and non-neoplastic diseases (b). The sample size of GWAS is provided in Table 1. We utilized a generalized linear mixed model in our GWAS.

Extended Data Fig. 8 Comparison of allelic directions between this GWAS and previous European GWAS at known loci.

a, Schematic explanations how we compared statistics between BBJ-GWAS and GWAS conducted in European populations (EUR-GWAS). We utilized two inclusion criteria of known loci: (i) EUR-GWAS has significant associations (P < 5 × 10−8) within 1Mb from the BBJ-lead variants and (ii) the BBJ-lead variant is in LD with the lead variant in the European-GWAS (r2 > 0.4 in European samples in 1KG phase3). The first criterion was added to exclude loci where EUR-GWAS has insufficient power (112 known loci remained after applying the first criterion). The second criterion was added because EUR-GWAS statistics at the BBJ-lead variant is not representing those at the EUR-lead variant when they are not in LD. b, effect sizes of BBJ- and EUR-GWAS at the BBJ-lead variants. All variants which passed the first criterion were used (n = 112). Variants which passed the second criterion are shown in red (n = 65). Since two variants have extremely large effect size, we provided two plots in different scales. The three variants with the opposite effect directions are marked by large dots, and their details are also provided. c, Regional association of T2D around rs12031188. Variants in LD (r2 > 0.4) with BBJ-lead variant (rs12031188) but not with EUR-lead variant are shown in red; Variants in LD (r2 > 0.4) with both lead variants are shown in blue. East Asians and Europeans in 1KG phase3 were used for LD calculation of the BBJ- and the EUR-lead variant, respectively.

Extended Data Fig. 9 Genetic correlations between male- and female-specific GWAS.

a. Genetic correlations between male- and female-specific GWAS. Estimates of genetic correlation and standard errors are provided. *: genetic correlation was significantly different from one (two-sided t test P = 2.2 × 10−3 < 0.05/20). b. The results of S-LDSC analysis based on sex-specific GWAS of asthma using 220 cell-type specific annotations. Significant annotations in either male or female asthma were shown (P < 0.05/220). Heterogeneity was tested by Cochran’s Q test, and its P values (Phet) were also provided. Black dashed line indicates P value = 0.05/220; grey dashed line indicates P value = 0.05.

Extended Data Fig. 10 S-LDSC results of four diseases in our GWAS.

The results of S-LDSC were plotted on the UMAP space. The significant results (FDR<0.05) were highlighted by cluster-specific colors (the same colors as used in Fig. 4). The names of the top five most significant TFs were also shown on the plot. The results of diseases with less than five significant TF binding site tracks were shown.

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Supplementary Tables 1–15

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Ishigaki, K., Akiyama, M., Kanai, M. et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet 52, 669–679 (2020). https://doi.org/10.1038/s41588-020-0640-3

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