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:

Epidemiology

A genome-wide association study in Japanese identified one variant associated with a preference for a Japanese dietary pattern

European Journal of Clinical Nutrition volume 75, pages 937–945 (2021)Cite this article

Subjects

Abstract

Background/Objectives

Individual eating habits may be influenced by genetic factors, in addition to environmental factors. Previous studies suggested that adherence to Japanese food patterns was associated with a decreased risk of all-cause and cardiovascular disease mortality. We conducted a genome-wide association study (GWAS) in a Japanese population to find genetic variations that affect adherence to a Japanese food pattern.

Subjects/Methods

We analyzed GWAS data using 14,079 participants from the Japan Multi-Institutional Collaborative Cohort study. We made a Japanese food score based on six food groups. Association of the imputed variants with the Japanese food score was performed by linear regression analysis with adjustments for age, sex, total energy intake, alcohol intake (g/day), and principal components 1–10 omitting variants in the major histocompatibility region.

Results

We found one SNP in the 14q11.2 locus that was significantly associated with the Japanese food score with P values <5 × 10−8. Functional annotation revealed that the expression levels of two genes (BCL2L2, SLC22A17) were significantly inversely associated with this SNP. These genes are known to be related to olfaction and obesity.

Conclusion

We found a new SNP that was associated with the Japanese food score in a Japanese population. This SNP is inversely associated with genes link to olfaction and obesity.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: A quantile-quantile plot (black) of genome-wide association studies.
Fig. 2: Genome-wide association signals.

Similar content being viewed by others

References

  1. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3–9.

    Article  CAS  Google Scholar 

  2. Martinez-Gonzalez MA, Bes-Rastrollo M, Serra-Majem L, Lairon D, Estruch R, Trichopoulou A. Mediterranean food pattern and the primary prevention of chronic disease: recent developments. Nutr Rev. 2009;67(Suppl 1):S111–6.

    Article  Google Scholar 

  3. Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am J Clin Nutr. 2013;97:505–16.

    Article  CAS  Google Scholar 

  4. Nakamura Y, Ueshima H, Okamura T, Kadowaki T, Hayakawa T, Kita Y, et al. A Japanese diet and 19-year mortality: National Integrated Project for Prospective Observation of Non-Communicable Diseases and its Trends in the Aged, 1980. Br J Nutr. 2008;101:1696–705.

    Article  Google Scholar 

  5. Okada E, Nakamura K, Ukawa S, Wakai K, Date C, Iso H, et al. The Japanese food score and risk of all-cause, CVD and cancer mortality: the Japan Collaborative Cohort Study. Br J Nutr. 2018;120:464–71.

    Article  CAS  Google Scholar 

  6. Pirastu N, Kooyman M, Robino A, van der Spek A, Navarini L, Amin N, et al. Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption. Sci Rep. 2016;6:31590.

    Article  CAS  Google Scholar 

  7. Nakagawa-Senda H, Hachiya T, Shimizu A, Hosono S, Oze I, Watanabe M, et al. A genome-wide association study in the Japanese population identifies the 12q24 locus for habitual coffee consumption: The J-MICC Study. Sci Rep. 2018;8:1493.

    Article  Google Scholar 

  8. Hasselbalch A, Heiman B, Kiviak K, Sørensen T. Studies of twins indicate that genetics influence dietary intake. J Nutr. 2008;138:2406–12.

    Article  CAS  Google Scholar 

  9. Mozaffarian D, Dashti HS, Wojczynski MK, Chu AY, Nettleton JA, Männistö S, et al. Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts. PLoS One 2017;12:e0186456.

    Article  Google Scholar 

  10. Guénard F, Bouchard-Mercier A, Rudkowska I, Lemieux S, Couture P, Vohl MC. Genome-wide association study of dietary pattern scores. Nutrients 2017;9:1–17.

    Article  Google Scholar 

  11. Igarashi M, Nogawa S, Kawafune K, Hachiya T, Takahashi S, Jia H, et al. Identification of the 12q24 Locus Associated With Fish Intake Frequency by Genome-Wide Meta-Analysis in Japanese Populations. Genes Nutr 2019;14:21.

    Article  Google Scholar 

  12. Hamajima N. The Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac J Cancer Prev 2007;8:317–23.

    PubMed  Google Scholar 

  13. Delongchamp R, Faramawi MF, Feingold E, Chung D, Abouelenein S. The association between SNPs and a quantitative trait: power calculation. Eur J Environ Public Health. 2018;2

  14. Tokudome S, Goto C, Imaeda N, Tokudome Y, Ikeda M, Maki S. Development of a data-based short food frequency questionnaire for assessing nutrient intake by middle-aged Japanese. Asian Pac J Cancer Prev 2004;5:40–3.

    PubMed  Google Scholar 

  15. Imaeda N, Fujiwara N, Tokudome Y, Ikeda M, Kuriki K, Nagaya T, et al. Reproducibility of a semi-quantitative food frequency questionnaire in Japanese female dietitians. J Epidemiol 2002;12:45–53.

    Article  Google Scholar 

  16. Goto C, Tokudome Y, Imaeda N, Takekuma K, Kuriki K, Igarashi F, et al. Validation study of fatty acid consumption assessed with a short food frequency questionnaire against plasma concentration in middle-aged Japanese. Scand J Nutr 2006;2:77–82.

    Article  Google Scholar 

  17. Tokudome Y, Goto C, Imaeda N, Hasegawa T, Kato R, Hirose K, et al. Relative validity of a short food frequency questionnaire for assessing nutrient intake versus three-day weighed diet records in middle-aged Japanese. J Epidemiol 2005;15:135–45.

    Article  Google Scholar 

  18. Wakai K. A Review of Food Frequency Questionnaires Developed and Validated in Japan. J Epidemiol 2009;19:1–11.

    Article  Google Scholar 

  19. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015;4:7.

    Article  Google Scholar 

  20. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38:904–9.

    Article  CAS  Google Scholar 

  21. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet 2006;2:e190.

    Article  Google Scholar 

  22. 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56–65.

    Article  Google Scholar 

  23. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature 2015;526:68–74.

    Article  Google Scholar 

  24. Yamaguchi-Kabata Y, Nakazono K, Takahashi A, Saito S, Hosono N, Kubo M, et al. Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups:effects on population-based association studies. Am J Hum Genet 2008;83:445–56.

    Article  CAS  Google Scholar 

  25. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019;28:166–74.

    Article  CAS  Google Scholar 

  26. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38:904–9.

    Article  CAS  Google Scholar 

  27. Delaneau O, Marchini J, Zagury J. A liear complexity phasing method for thousands of genomes. Nat Methods 2011;9:179–81.

    Article  Google Scholar 

  28. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet 2016;48:1284–7.

    Article  CAS  Google Scholar 

  29. Das S. DosageConvertor https://genome.sph.umich.edu/wiki/DosageConvertor

  30. Zhu X, Li S, Cooper RS, Elston RC. A unified association analysis approach for family and unrelated samples correcting for stratifications. Am J Hum Genet. 2008;82:352–65.

    Article  CAS  Google Scholar 

  31. Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics 2012;28:3329–31.

    Article  CAS  Google Scholar 

  32. Turner, SD qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. biorXiv. 2014. https://doi.org/10.1101/005165. CRAN: http://cran.r-project.org/web/packages/qqman/

  33. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 2010;26:2336–7.

    Article  CAS  Google Scholar 

  34. Tyner C, Barber GP, Casper J, Clawson H, Diekhans M, Eisenhart C, et al. The UCSC Genome Browser database: 2017 update. Nucleic Acids Res 2017;45:D626–34.

    CAS  PubMed  Google Scholar 

  35. Aken BL, Achuthan P, Akanni W, Amode MR, Bernsdorff F, Bhai J, et al. Ensembl 2017. Nucleic Acids Res 2017;45:D635–42.

    Article  CAS  Google Scholar 

  36. Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015;348:648–60.

    Article  Google Scholar 

  37. Day FR, Loh PR, Scott RA, Ong KK, Perry JR. A Robust Example of Collider Bias in a Genetic Association Study. Am J Hum Genet 2016;98:392–3.

    Article  CAS  Google Scholar 

  38. Matoba N, Akiyama M, Ishigaki K, Kanai M, Takahashi A, Momozawa Y, et al. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits. Nat Hum Behav. 2020. https://doi.org/10.1038/s41562-019-0805-1.

  39. Bennett KM, Liu J, Hoelting C, Stoll J. Expression and analysis of two novel rat organic cation transporter homologs, SLC22A17 and SLC22A23. Mol Cell Biochem 2011;352:143–54.

    Article  CAS  Google Scholar 

  40. Flower DR. The lipocalin protein family: structure and function. Biochem J 1996;318(Pt 1):1–14.

    Article  CAS  Google Scholar 

  41. Wang Y, Lam KS, Kraegen EW, Sweeney G, Zhang J, Tso AW, et al. Lipocalin-2 is an inflammatory marker closely associated with obesity, insulin resistance, and hyperglycemia in humans. Clin Chem 2007;53:34–41.

    Article  CAS  Google Scholar 

  42. Lee M, Kwon DY, Kim MS, Choi CR, Park MY, Kim AJ. Genome-wide association study for the interaction between BMR and BMI in obese Korean women including overweight. Nutr Res Pract 2016;10:115–24.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all the staff at the Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, the staff of the BioBank Japan project, and the members of J-MICC Research Group. We thank Drs. Nobuyuki Hamajima and Hideo Tanaka, the past principal investigators of the J-MICC, for their continuous support for our study. The full list of consortium (J-MICC Research Group) members is given in the Supplementary Information.

Funding

This study was supported by a Grants-in-Aid for Scientific Research for Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001), and by JSPS KAKENHI Grants (No. 16H06277) from the Japanese Ministry of Education, Culture, Sports, Science and Technology. This study was also supported in part by funding for the BioBank Japan Project from the Japan Agency for Medical Research and development from April 2015, and the Ministry of Education, Culture, Sports, Science and Technology from April 2003 to March 2015.

Author information

Authors and Affiliations

  1. Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Otsu, Japan

    Harumitsu Suzuki & Katsuyuki Miura

  2. Department of Hygiene, Wakayama Medical University, Wakayama, Japan

    Harumitsu Suzuki

  3. Yamashina Racto Clinic and Medical Examination Center, Kyoto, Japan

    Yasuyuki Nakamura

  4. Department of Public Health, Shiga University of Medical Science, Otsu, Japan

    Yasuyuki Nakamura, Naoyuki Takashima, Katsuyuki Miura & Yoshikuni Kita

  5. Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan

    Keitaro Matsuo & Isao Oze

  6. Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Keitaro Matsuo

  7. Department of Nutrition, Faculty of Wellness, Shigakkan University, Obu, Japan

    Nahomi Imaeda

  8. Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

    Nahomi Imaeda, Chiho Goto, Sadao Suzuki & Miki Watanabe

  9. Department of Health and Nutrition, School of Health and Human Life, Nagoya Bunri University, Inazawa, Japan

    Chiho Goto

  10. Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Akira Narita

  11. Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Shiwa-gun, Iwate, Japan

    Atsushi Shimizu

  12. Department of Public Health, Kindai University Faculty of Medicine, Osaka-Sayama, Japan

    Naoyuki Takashima

  13. Division of Bioethics and Healthcare Law, the National Cancer Center, Tokyo, Japan

    Kenji Matsui

  14. Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Masahiro Nakatochi

  15. Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Asahi Hishida, Takashi Tamura, Yuka Kadomatsu, Rieko Okada, Kenji Takeuchi & Kenji Wakai

  16. Department of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan

    Yuichiro Nishida

  17. Department Pharmacy, Saga University Hospital, Saga, Japan

    Chisato Shimanoe

  18. School of Health Sciences, Faculty of Medicine, Kagoshima University, Kagoshima, Japan

    Daisaku Nishimoto

  19. Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan

    Toshiro Takezaki

  20. Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya, Japan

    Hidemi Ito

  21. Department of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Hidemi Ito

  22. Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Japan

    Hiroaki Ikezaki & Masayuki Murata

  23. Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan

    Daisuke Matsui & Etsuko Ozaki

  24. Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Japan

    Haruo Mikami & Yohko Nakamura

  25. Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan

    Kokichi Arisawa & Hirokazu Uemura

  26. Department of Health and Welfare System, College of Nursing Art and Science, University of Hyogo, Akashi, Japan

    Hirokazu Uemura

  27. Laboratory of Public Health, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Japan

    Kiyonori Kuriki

  28. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Yukihide Momozawa & Michiaki Kubo

  29. Faculty of Nursing Science, Tsuruga Nursing University, Tsuruga, Japan

    Yoshikuni Kita

Authors
  1. Harumitsu Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasuyuki Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keitaro Matsuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nahomi Imaeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chiho Goto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Akira Narita
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Atsushi Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Naoyuki Takashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kenji Matsui
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katsuyuki Miura
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Masahiro Nakatochi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Asahi Hishida
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Takashi Tamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yuka Kadomatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Rieko Okada
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yuichiro Nishida
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Chisato Shimanoe
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Daisaku Nishimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Toshiro Takezaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Isao Oze
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Hidemi Ito
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Hiroaki Ikezaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Masayuki Murata
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Daisuke Matsui
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Etsuko Ozaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Haruo Mikami
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Yohko Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Sadao Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Miki Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Kokichi Arisawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Hirokazu Uemura
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Kiyonori Kuriki
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Yukihide Momozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Michiaki Kubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Yoshikuni Kita
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Kenji Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Kenji Wakai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YN, YoK, and KW: designed the research; HS, YN, NI, CG, NT, KenM, KMi, AH, TT, YuK, RR, YNi, CS, DN, ToT, IO, HI, HiK, MM, DM, EO, HM, YoN, SS, MW, KA, HU, KK, YM, MK, and KT: conducted the research; HS, YN, KeiM, AN, AS, and MN: analyzed data and performed statistical analyses; HS, YN, and KW: wrote the manuscript and had primary responsibility for final content. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Yasuyuki Nakamura.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suzuki, H., Nakamura, Y., Matsuo, K. et al. A genome-wide association study in Japanese identified one variant associated with a preference for a Japanese dietary pattern. Eur J Clin Nutr 75, 937–945 (2021). https://doi.org/10.1038/s41430-020-00823-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-020-00823-z

This article is cited by

Search

Advanced search

Quick links