Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genome-wide association study identifies 112 new loci for body mass index in the Japanese population

Nature Genetics volume 49, pages 1458–1467 (2017)Cite this article

Subjects

Abstract

Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of body mass index (BMI) in Japanese people (n = 173,430), we found 85 loci significantly associated with obesity (P < 5.0 × 10−8), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19+ cells. We also found significant genetic correlations between BMI and lymphocyte count (P = 6.46 × 10−5, rg = 0.18) and between BMI and multiple complex diseases. These findings provide genetic evidence that lymphocytes are relevant to body weight regulation and offer insights into the pathogenesis of obesity.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Enrichment of identified variants in active enhancers.
Figure 2: Enrichment of identified variants in H3K4me3 peaks.
Figure 3: Variance explained by subsets of associated variants in Japanese cohorts.
Figure 4: Scatter plot of the effect sizes for BMI and T2D.
Figure 5: Genetic correlations between BMI and analyzed traits.

Similar content being viewed by others

References

  1. Haslam, D.W. & James, W.P.T. Obesity. Lancet 366, 1197–1209 (2005).

    Article  PubMed  Google Scholar 

  2. Wilson, P.W.F., D'Agostino, R.B., Sullivan, L., Parise, H. & Kannel, W.B. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch. Intern. Med. 162, 1867–1872 (2002).

    Article  PubMed  Google Scholar 

  3. Renehan, A.G., Tyson, M., Egger, M., Heller, R.F. & Zwahlen, M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 371, 569–578 (2008).

    Article  PubMed  Google Scholar 

  4. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Wen, W. et al. Meta-analysis of genome-wide association studies in East Asian–ancestry populations identifies four new loci for body mass index. Hum. Mol. Genet. 23, 5492–5504 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Winkler, T.W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11, e1005378 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yoon, K.H. et al. Epidemic obesity and type 2 diabetes in Asia. Lancet 368, 1681–1688 (2006).

    Article  PubMed  Google Scholar 

  10. Nakamura, Y. The BioBank Japan Project. Clin. Adv. Hematol. Oncol. 5, 696–697 (2007).

    PubMed  Google Scholar 

  11. Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).

  12. Hirata, M. et al. Cross-sectional analysis of BioBank Japan clinical data: a large cohort of 200,000 patients with 47 common diseases. J. Epidemiol. 27, S9–S21 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Abecasis, G.R. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    Article  PubMed  Google Scholar 

  14. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Trivellin, G. et al. Gigantism and acromegaly due to Xq26 microduplications and GPR101 mutation. N. Engl. J. Med. 371, 2363–2374 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Morris, A.P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wang, X. et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum. Mol. Genet. 22, 2303–2311 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  21. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  24. Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Segrè, A.V., Groop, L., Mootha, V.K., Daly, M.J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Okada, Y. et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat. Genet. 44, 302–306 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dimas, A.S. et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63, 2158–2171 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Julius, S., Egan, B.M., Kaciroti, N.A., Nesbitt, S.D. & Chen, A.K. In prehypertension leukocytosis is associated with body mass index but not with blood pressure or incident hypertension. J. Hypertens. 32, 251–259 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Ilavská, S. et al. Association between the human immune response and body mass index. Hum. Immunol. 73, 480–485 (2012).

    Article  PubMed  Google Scholar 

  34. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Liu, J.Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Oota, H. et al. The evolution and population genetics of the ALDH2 locus: random genetic drift, selection, and low levels of recombination. Ann. Hum. Genet. 68, 93–109 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. Han, Y. et al. Evidence of positive selection on a class I ADH locus. Am. J. Hum. Genet. 80, 441–456 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Johnson, A.M.F. & Olefsky, J.M. The origins and drivers of insulin resistance. Cell 152, 673–684 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Winer, D.A. et al. B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies. Nat. Med. 17, 610–617 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Winer, D.A., Winer, S., Chng, M.H., Shen, L. & Engleman, E.G. B lymphocytes in obesity-related adipose tissue inflammation and insulin resistance. Cell. Mol. Life Sci. 71, 1033–1043 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hjellvik, V., Tverdal, A. & Furu, K. Body mass index as predictor for asthma: a cohort study of 118,723 males and females. Eur. Respir. J. 35, 1235–1242 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Inamasu, J., Guiot, B.H. & Sachs, D.C. Ossification of the posterior longitudinal ligament: an update on its biology, epidemiology, and natural history. Neurosurgery 58, 1027–1039, discussion 1027–1039 (2006).

    Article  PubMed  Google Scholar 

  44. Tam, E.M. et al. Lower muscle mass and body fat in adolescent idiopathic scoliosis are associated with abnormal leptin bioavailability. Spine 41, 940–946 (2016).

    Article  PubMed  Google Scholar 

  45. Turesson, C., Bergström, U., Pikwer, M., Nilsson, J.-Å. & Jacobsson, L.T.H. A high body mass index is associated with reduced risk of rheumatoid arthritis in men, but not in women. Rheumatology 55, 307–314 (2016).

    Article  PubMed  Google Scholar 

  46. Qin, B. et al. Body mass index and the risk of rheumatoid arthritis: a systematic review and dose-response meta-analysis. Arthritis Res. Ther. 17, 86 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zammit, S. et al. Height and body mass index in young adulthood and risk of schizophrenia: a longitudinal study of 1 347 520 Swedish men. Acta Psychiatr. Scand. 116, 378–385 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Wyatt, R.J., Henter, I.D., Mojtabai, R. & Bartko, J.J. Height, weight and body mass index (BMI) in psychiatrically ill US Armed Forces personnel. Psychol. Med. 33, 363–368 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  PubMed  Google Scholar 

  50. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Delaneau, O., Marchini, J. & Zagury, J.-F.Alinear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

    Article  PubMed  Google Scholar 

  53. Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the staff of the TMM, the JPHC and the BBJ for collecting samples and clinical information. We are grateful to the staff of the RIKEN Center for Integrative Medical Sciences for genotyping and data management. We thank S.K. Low, K. Suzuki and M. Horikoshi for advice on statistical analyses, and A.P. Morris for providing us with the MANTRA software. This study was funded by the BioBank Japan project (M.A., Y.O., M. Kanai, A.T., Y.M., M.H., K.M., M. Kubo and Y.K.) and Tohoku Medical Megabank project (T.H., K.T., A.S., A.H., N.M. and M.Y.), which is supported by the Ministry of Education, Culture, Sports, Sciences and Technology of Japanese government and the Japan Agency for Medical Research and Development. The JPHC Study has been supported by the National Cancer Research and Development Fund (2010–present) and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan (1989–2010) (M. Iwasaki., T.Y., N.S. and S.T.). GWAS of psychiatric disorders were the results of the Strategic Research Program for Brain Sciences (SRPBS) from the Japan Agency for Medical Research and Development (A.T., M. Ikeda, N.I., M. Kubo and Y.K.).

Author information

Authors and Affiliations

  1. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Masato Akiyama, Yukinori Okada, Masahiro Kanai, Atsushi Takahashi & Yoichiro Kamatani

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

    Yukinori Okada

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

    Yukinori Okada

  4. Laboratory for Omics Informatics, Omics Research Center, National Cerebral and Cardiovascular Center, Osaka, Japan

    Atsushi Takahashi

  5. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yukihide Momozawa

  6. Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan

    Masashi Ikeda & Nakao Iwata

  7. Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan

    Shiro Ikegawa

  8. Institute of Medical Science, the University of Tokyo, Tokyo, Japan

    Makoto Hirata

  9. Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  10. Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan

    Motoki Iwasaki, Taiki Yamaji & Norie Sawada

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

    Tsuyoshi Hachiya, Kozo Tanno & Atsushi Shimizu

  12. Department of Hygiene and Preventive Medicine, School of Medicine, Iwate Medical University, Iwate, Japan

    Kozo Tanno

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

    Atsushi Hozawa, Naoko Minegishi & Masayuki Yamamoto

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

    Atsushi Hozawa, Naoko Minegishi & Masayuki Yamamoto

  15. Center for Public Health Sciences, National Cancer Center, Tokyo, Japan

    Shoichiro Tsugane

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

    Michiaki Kubo

  17. Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Yoichiro Kamatani

Authors
  1. Masato Akiyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukinori Okada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masahiro Kanai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atsushi Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yukihide Momozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Masashi Ikeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nakao Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shiro Ikegawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Makoto Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Koichi Matsuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Motoki Iwasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Taiki Yamaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Norie Sawada
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tsuyoshi Hachiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kozo Tanno
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Atsushi Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Atsushi Hozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Naoko Minegishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Shoichiro Tsugane
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Masayuki Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Michiaki Kubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yoichiro Kamatani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.A., Y.K. and M. Kubo conceived and designed the study. K.M., M.H. and M. Kubo collected and managed the BBJ sample. M. Iwasaki, T.Y., N.S. and S.T. collected and managed JPHC sample and information. T.H., K.T., A.S., A.H., N.M. and M.Y. collected and managed the TMM sample. Y.M. and M. Kubo performed genotyping. M.A., M. Kanai, Y.K. and A.T. performed statistical analysis. S.I., M. Ikeda and N.I. contributed to data acquisition. Y.O., A.T., Y.K. and M. Kubo supervised the study. M.A., Y.O., Y.K. and M. Kubo wrote the manuscript.

Corresponding author

Correspondence to Yoichiro Kamatani.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Note. (PDF 34958 kb)

Life Sciences Reporting Summary (PDF 159 kb)

Supplementary Data Set 1

Regional association plots of newly identified loci. (PDF 3582 kb)

Supplementary Data Set 2

Regional association plots of previously reported loci. (PDF 2696 kb)

Supplementary Data Set 3

Regional association plots of trans-ethnic meta-analysis. (PDF 9401 kb)

Supplementary Tables

Supplementary Tables 1–27 (excel) (XLSX 12426 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akiyama, M., Okada, Y., Kanai, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet 49, 1458–1467 (2017). https://doi.org/10.1038/ng.3951

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3951

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research