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A cross-population atlas of genetic associations for 220 human phenotypes

Nature Genetics volume 53, pages 1415–1424 (2021)Cite this article

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Abstract

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

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Fig. 1: Overview of the identified loci in the cross-population meta-analyses of 220 deep-phenotype GWASs.
Fig. 2: Number of significant associations per variant.
Fig. 3: HLA and ABO association PheWAS.
Fig. 4: The deconvolution analysis of a matrix of summary statistics of 159 diseases across populations.
Fig. 5: Examples of disease-component correspondence and biological interpretation of the components by projection and enrichment analysis using GREAT.

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Data availability

The genotype data of BBJ used in this study are available from the Japanese Genotype-phenotype Archive (JGA) with accession codes JGAS000114/JGAD000123 and JGAS000114/JGAD000220, which can be accessed through application at https://humandbs.biosciencedbc.jp/en/hum0014-latest. The UKB analysis was conducted via application number 47821. The genotype and phenotype data can be accessed through application at https://www.ukbiobank.ac.uk. This study used the FinnGen release 3 data. Summary results can be accessed through application at https://www.finngen.fi/en/access_results. We provide downloadable full GWAS summary statistics with an interactive visualization of Manhattan, LocusZoom and PheWAS plots at our PheWeb.jp website (https://pheweb.jp/). The summary statistics of GWASs in this study (BioBank Japan, European and cross-population meta-analyses) are also deposited at the National Bioscience Database Center (NBDC) Human Database (https://humandbs.biosciencedbc.jp/en/) with the accession code hum0197, and the GWAS Catalog (https://www.ebi.ac.uk/gwas/) with the study accession IDs from GCST90018563 (https://www.ebi.ac.uk/gwas/studies/GCST90018563) to GCST90019002 (https://www.ebi.ac.uk/gwas/studies/GCST90019002) (full IDs are described in the Supplementary Notes). The summary statistics of metabolite GWASs in the Japanese population (Tohoku Medical Megabank Organization) which we used for decomposition–projection analysis are available at https://jmorp.megabank.tohoku.ac.jp/202102/gwas/TGA000005. We used gnomAD database (https://gnomad.broadinstitute.org/) to refer to the allele frequencies.

Code availability

We used publicly available software for the analyses. The software used is listed and described in the Methods section of our manuscript.

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Acknowledgements

We thank all the participants of BioBank Japan, UK Biobank and FinnGen. We thank K. Watanabe for her input in the analysis of phenotypic correlations and pleiotropy. This research was supported by the Tailor-Made Medical Treatment program (the BioBank Japan Project) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), the Japan Agency for Medical Research and Development (AMED). The FinnGen project is funded by two grants from Business Finland (grant nos. HUS 4685/31/2016 and UH 4386/31/2016) and nine industry partners (AbbVie, AstraZeneca, Biogen, Celgene, Genentech, GSK, MSD, Pfizer and Sanofi). The following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/), THL Biobank (https://thl.fi/fi/web/thl-biopank), Helsinki Biobank (https://www.terveyskyla.fi/helsinginbiopankki/), Northern Finland Biobank Borealis (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki), Finnish Clinical Biobank Tampere (https://www.tays.fi/biopankki), Biobank of Eastern Finland (https://ita-suomenbiopankki.fi), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.bloodservice.fi/Research%20Projects/biobanking) and Terveystalo Biobank Finland (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). 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 and the JCR Grant for Promoting Basic Rheumatology. M. Kanai was supported by a Nakajima Foundation Fellowship and the Masason Foundation. Y. Tanigawa is in part supported by a Funai Overseas Scholarship from the Funai Foundation for Information Technology and the Stanford University School of Medicine. M.A.R. is in part supported by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) under award no. R01HG010140, and an NIH Center for Multi- and Cross-population Mapping of Mendelian and Complex Diseases grant (no. 5U01 HG009080). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Y.O. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant nos. 19H01021, 20K21834) and AMED (grant nos. JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006 and JP21km0405217), JST Moonshot R&D Grant Number JPMJMS2021, Takeda Science Foundation and the Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University.

Author information

Author notes

  1. These authors contributed equally: Saori Sakaue, Masahiro Kanai.

Authors and Affiliations

  1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Saori Sakaue, Masahiro Kanai, Takahiro Konuma, Kenichi Yamamoto, Ken Suzuki & Yukinori Okada

  2. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Saori Sakaue, Masato Akiyama, Kazuyoshi Ishigaki, Chikashi Terao, Yoichiro Kamatani & Yukinori Okada

  3. Center for Data Sciences, Harvard Medical School, Boston, MA, USA

    Saori Sakaue & Kazuyoshi Ishigaki

  4. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saori Sakaue & Kazuyoshi Ishigaki

  5. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Saori Sakaue, Masahiro Kanai, Juha Karjalainen, Mitja Kurki, Kazuyoshi Ishigaki, Aarno Palotie & Mark J. Daly

  6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Masahiro Kanai, Juha Karjalainen, Mitja Kurki & Mark J. Daly

  7. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Masahiro Kanai, Juha Karjalainen, Mitja Kurki & Mark J. Daly

  8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Masahiro Kanai

  9. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

    Masahiro Kanai, Juha Karjalainen, Mitja Kurki, Aarno Palotie & Mark J. Daly

  10. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA

    Yosuke Tanigawa & Manuel A. Rivas

  11. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Seizo Koshiba, Akira Narita, Gen Tamiya & Masayuki Yamamoto

  12. Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Sendai, Japan

    Seizo Koshiba, Gen Tamiya & Masayuki Yamamoto

  13. Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan

    Kenichi Yamamoto

  14. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan

    Kenichi Yamamoto & Yukinori Okada

  15. Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

    Masato Akiyama

  16. Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Akari Suzuki & Kazuhiko Yamamoto

  17. Department of Urology, Iwate Medical University, Iwate, Japan

    Wataru Obara

  18. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Yamaji

  19. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Kazuhisa Takahashi

  20. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai

  21. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai & Yasuo Takahashi

  22. Tokushukai Group, Tokyo, Japan

    Takao Suzuki & Nobuaki Shinozaki

  23. Department of Hematology, Nippon Medical School, Tokyo, Japan

    Hiroki Yamaguchi

  24. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan

    Shiro Minami

  25. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan

    Shigeo Murayama

  26. Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan

    Kozo Yoshimori

  27. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan

    Satoshi Nagayama

  28. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Otsu, Japan

    Daisuke Obata

  29. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan

    Masahiko Higashiyama

  30. Aso Iizuka Hospital, Fukuoka, Japan

    Akihide Masumoto

  31. National Hospital Organization Osaka National Hospital, Osaka, Japan

    Yukihiro Koretsune

  32. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Kaoru Ito

  33. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Toshimasa Yamauchi & Takashi Kadowaki

  34. Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Issei Komuro

  35. Toranomon Hospital, Tokyo, Japan

    Takashi Kadowaki

  36. Graduate School of Medicine, Tohoku University, Sendai, Japan

    Gen Tamiya & Masayuki Yamamoto

  37. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

    Gen Tamiya

  38. Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yusuke Nakamura

  39. Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan

    Yusuke Nakamura

  40. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Michiaki Kubo

  41. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  42. Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani

  43. Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

    Aarno Palotie & Yukinori Okada

  44. Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  45. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan

    Yukinori Okada

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  1. Saori Sakaue
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Consortia

FinnGen

  • Juha Karjalainen
  • , Mitja Kurki
  • , Aarno Palotie
  •  & Mark J. Daly

Contributions

S.S., M. Kanai and Y.O. conceived the study. S.S., M. Kanai, Y. Tanigawa., M.A.R. and Y.O. wrote the manuscript. S.S., M. Kanai, J.K., M. Kurki, T. Konuma, Kenichi Yamamoto, M.A., K. Ishigaki, Kazuhiko Yamamoto, Y. Kamatani, A.P., M.J.D. and Y.O. conducted GWAS data analysis. S.S., Y. Tanigawa and M.A.R. conducted statistical decomposition analysis. S.S., S.K., A.N., G.T. and Y.O. conducted metabolome analysis. A.S., K.S., W.O., K. Yamaji, K.T., S.A., Y. Takahashi, T.S., N.S., H.Y., S. Minami, S. Murayama, K. Yoshimori, S.N., D.O., M.H., A.M., Y. Koretsune, K. Ito, C.T., T.Y., I.K., T. Kadowaki, M.Y., Y.N., M. Kubo, Y.M., Kazuhiko Yamamoto and K.M. collected and managed samples and data. A.P. and M.J.D. coordinated collaboration with FinnGen.

Corresponding authors

Correspondence to Saori Sakaue, Koichi Matsuda or Yukinori Okada.

Ethics declarations

Competing interests

M.A.R. is on the SAB of 54Gene and the Computational Advisory Board for Goldfinch Bio and has advised BioMarin, Third Rock Ventures, MazeTx and Related Sciences. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Caroline Hayward, Marylyn Ritchie, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Overview of this study.

We performed 220 deep-phenotype GWASs in BioBank Japan, including 108 novel GWASs ever conducted in East Asian population. We performed trans-biobank meta-analyses with UK Biobank and FinnGen (ntotal = 628,000), resulting in discovery of 5,343 novel loci. All summary statistics are openly shared through pheweb.jp web portal. As downstream analyses, we performed (i) cross-population comparison of pleiotropy and genetic correlation, (ii) comprehensive HLA fine-mapping, and (iii) statistical decomposition of a matrix of summary statistics to gain insights into biology underlying current disease classifications, by incorporating functional genomics, metabolomics, and biomarker data.

Extended Data Fig. 2 Locus plots for representative loci.

(a) Regional association plots for Pulmonary Tuberculosis (PTB) in BBJ are shown. The lead variant (rs140780894) is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant. (b) Regional association plots for cholelithiasis in BBJ are shown. The lead variant (rs715) is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant. (c) Regional association plots for gastric diseases in BBJ at the PSCA locus in gastric ulcer, gastric cancer, and gastric polyp are shown. Rs2976397, which was a lead variant in gastric ulcer, is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant. (d) Regional association plots at the FUT3 locus in gall bladder polyp and cholelithiasis in BBJ are shown. Rs28362459, which was a lead variant in gall bladder polyp, is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant. (e) Regional association plots for urticaria in BBJ are shown. The lead variant (rs56043070) is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant. (f) Regional association plots for salicylic acids prescription in BBJ are shown. The lead variant (rs151193009) is colored in pink, and colors of other dots indicate linkage disequilibrium measure r2 with the lead variant.

Extended Data Fig. 3 The effect size correlation between BBJ GWAS and European GWAS.

The marginal effect sizes of genome-wide significant variants across traits in BBJ are compared with those in European GWAS. Each plot represents a variant, and is colored based on the significance in European GWAS as shown in the left top legend. Pearson’s correlation r and P value (two-sided) between BBJ GWAS and European GWAS are also shown in the legend.

Extended Data Fig. 4 Phenotypic correlation across 220 phenotypes in BBJ.

a. Heatmap of pair-wise phenotypic correlation matrix. The color of the cells indicates the value of correlation r as shown in a color scale at the bottom. The traits (rows and columns) were hierarchically clustered by hclust library in R. b. Silhouette score for clustering of closely related phenotypes with different number of clusters (Supplementary Notes).

Extended Data Fig. 5 The degree of pleiotropy in BBJ after accounting for phenotypic or genetic correlations.

The Manhattan-like plots show the number of significant associations (P < 5.0×10−8) at each tested genetic variant in Japanese. a. For all traits (ntrait = 220; as shown in Fig. 2a). b. After accounting for phenotypic correlations. c. After accounting for genetic correlations.

Extended Data Fig. 6 Genetic correlation matrices across populations.

The matrices describe pairwise genetic correlation rg in Japanese GWAS (a; n = 5,565) and in European GWAS (b; n = 10,878), which was estimated by bivariate LD score regression. A color of the cells indicates the value of rg as shown in a color scale at the bottom. The traits (rows and columns) were hierarchically clustered by hclust library in R, and trait domains are displayed as colored boxes (see Methods).

Extended Data Fig. 7 Network representation of the TSVD analysis.

Two-dimensional illustration of interconnection among 159 diseases and 40 latent components. Plots in blue indicate each trait’s statistics, and plots in pink indicate the latent components derived by TSVD. White lines represent the contribution of each phenotype in each component. The width of the lines indicates the strength of the contribution based on the squared cosine score.

Extended Data Fig. 8 Heatmap representation of squared cosine scores of diseases to components.

The components (rows) are shown from 1 (top) to 40 (bottom), and the diseases (columns) are sorted based on the contribution of each component to the disease based on the squared cosine score (from component 1 to 40). Each cell is colored based on the squared cosine score of a given trait to a given component, as shown in a color scale at the bottom right.

Extended Data Fig. 9 Enrichment analyses of genes explaining each component with tissue specificity.

A heatmap representation of the enrichment analyses of genes explaining each component with tissue-specific genes defined by GTEx expression profile (a) and regulatory vocabulary from ENCODE3 data (b). Each cell is colored based on Penrichment from Fisher’s exact tests to assess the enrichment of the genes comprising each component within each tissue-specific gene set as shown in a color scale at the bottom right.

Extended Data Fig. 10 Genetic variants analyzed in the three cohorts.

The Venn diagram showing the number of genetic variants analyzed in this study in each of the three cohorts (BBJ, UKB, and FinnGen) and overlapping variants across the cohorts.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Supplementary Notes and Supplementary references

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–12

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Sakaue, S., Kanai, M., Tanigawa, Y. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415–1424 (2021). https://doi.org/10.1038/s41588-021-00931-x

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