GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits (original) (raw)

Nature Human Behaviour volume 4, pages 308–316 (2020)Cite this article

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Abstract

Dietary habits are important factors in our lifestyle, and confer both susceptibility to and protection from a variety of human diseases. We performed genome-wide association studies for 13 dietary habits including consumption of alcohol (ever versus never drinkers and drinks per week), beverages (coffee, green tea and milk) and foods (yoghurt, cheese, natto, tofu, fish, small whole fish, vegetables and meat) in Japanese individuals (n = 58,610–165,084) collected by BioBank Japan, the nationwide hospital-based genome cohort. Significant associations were found in nine genetic loci (MCL1-ENSA, GCKR, AGR3-AHR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, CYP1A2-CSK and ADORA2A-AS1) for 13 dietary traits (P < 3.8 × 10−9). Of these, ten associations between five loci and eight traits were new findings. Furthermore, a phenome-wide association study revealed that five of the dietary trait-associated loci have pleiotropic effects on multiple human complex diseases and clinical measurements. Our findings provide new insight into the genetics of habitual consumption.

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Data availability

GWAS summary statistics of the 13 dietary habits investigated are publicly available at the National Bioscience Database Centre (NBDC) Human Database (Research ID: hum0014) as open data with no access restrictions. GWAS genotype data were deposited at the NBDC Human Database (Research ID: hum0014).

References

  1. Johnson, K. E. & Voight, B. F. Patterns of shared signatures of recent positive selection across human populations. Nat. Ecol. Evol. 2, 713–720 (2018).
    Article PubMed PubMed Central Google Scholar
  2. Okada, Y. et al. Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese. Nat. Commun. 9, 1631 (2018).
    Article PubMed PubMed Central CAS Google Scholar
  3. Okada, Y. eLD: entropy-based linkage disequilibrium index between multiallelic sites. Hum. Genome Var. 5, 29 (2018).
    Article PubMed PubMed Central CAS Google Scholar
  4. Baik, I., Cho, N. H., Kim, S. H., Han, B.-G. & Shin, C. Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am. J. Clin. Nutr. 93, 809–816 (2011).
    Article CAS PubMed Google Scholar
  5. Takeuchi, F. et al. Confirmation of ALDH2 as a major locus of drinking behavior and of its variants regulating multiple metabolic phenotypes in a Japanese population. Circ. J. 75, 911–918 (2011).
    Article CAS PubMed Google Scholar
  6. Schumann, G. et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc. Natl Acad. Sci. USA 113, 14372–14377 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  7. Yang, X. et al. Common variants at 12q24 are associated with drinking behavior in Han Chinese. Am. J. Clin. Nutr. 97, 545–551 (2013).
    Article CAS PubMed Google Scholar
  8. Jorgenson, E. et al. Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study. Mol. Psychiatry 22, 1359–1367 (2017).
    Article CAS PubMed PubMed Central Google Scholar
  9. Clarke, T. K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112117). Mol. Psychiatry 22, 1376–1384 (2017).
    Article CAS PubMed PubMed Central Google Scholar
  10. Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).
    Article CAS PubMed PubMed Central Google Scholar
  11. Zhong, V. W. et al. A genome-wide association study of bitter and sweet beverage consumption. Hum. Mol. Genet. 28, 2449–2457 (2019).
    Article CAS PubMed PubMed Central Google Scholar
  12. Sulem, P. et al. Sequence variants at _CYP1A1_-CYP1A2 and AHR associate with coffee consumption. Hum. Mol. Genet 20, 2071–2077 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  13. Amin, N. et al. Genome-wide association analysis of coffee drinking suggests association with CYP1A1/CYP1A2 and NRCAM. Mol. Psychiatry 17, 1116–1129 (2012).
    Article CAS PubMed Google Scholar
  14. Coffee and Caffeine Genetics Consortiumet al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol. Psychiatry 20, 647–656 (2015).
    Article CAS Google Scholar
  15. Pirastu, N. et al. Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption. Sci. Rep. 6, 31590 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  16. Nakagawa-Senda, H. 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. 8, 1493 (2018).
    Article PubMed PubMed Central CAS Google Scholar
  17. Mozaffarian, D. et al. Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts. PLoS One 12, e0186456 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  18. Jiang, L., Penney, K. L., Giovannucci, E., Kraft, P. & Wilson, K. M. A genome-wide association study of energy intake and expenditure. PLoS One 13, e0201555 (2018).
    Article PubMed PubMed Central CAS Google Scholar
  19. Nagai, A. et al. Overview of the BioBank Japan project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).
    Article PubMed PubMed Central Google Scholar
  20. Nakachi, K., Matsuyama, S., Miyake, S., Suganuma, M. & Imai, K. Preventive effects of drinking green tea on cancer and cardiovascular disease: epidemiological evidence for multiple targeting prevention. BioFactors 13, 49–54 (2000).
    Article CAS PubMed Google Scholar
  21. Uemura, H. et al. Inverse association between soy food consumption, especially fermented soy products intake and soy isoflavone, and arterial stiffness in Japanese men. Sci. Rep. 8, 9667 (2018).
    Article PubMed PubMed Central CAS Google Scholar
  22. Tsugane, S. & Sawada, N. The JPHC study: design and some findings on the typical Japanese diet. Jpn. J. Clin. Oncol. 44, 777–782 (2014).
    Article PubMed Google Scholar
  23. Akiyama, 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).
    Article CAS PubMed Google Scholar
  24. Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).
    Article CAS PubMed Google Scholar
  25. Bulik-Sullivan, B. 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
  26. Cornelis, M. C. et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS Genet. 7, e1002033 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  27. Cornelis, M. C. et al. Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum. Mol. Genet. 25, ddw334 (2016).
    Article CAS Google Scholar
  28. Yin, G. et al. ALDH2 polymorphism is associated with fasting blood glucose through alcohol consumption in Japanese men. Nagoya J. Med. Sci. 78, 183–193 (2016).
    CAS PubMed PubMed Central Google Scholar
  29. Gelernter, J. et al. Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol. Psychiatry 19, 41–49 (2014).
    Article CAS PubMed Google Scholar
  30. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).
    Article CAS PubMed PubMed Central Google Scholar
  31. Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
    Article CAS Google Scholar
  32. Kim, Y. K. et al. Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population. Cardiovasc. Diabetol. 15, 20 (2016).
    Article PubMed PubMed Central CAS Google Scholar
  33. Sakaue, S. et al. Functional variants in ADH1B and ALDH2 are non-additively associated with all-cause mortality in Japanese population. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-019-0518-y (2019).
  34. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Researchet al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).
    Article CAS Google Scholar
  35. 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
  36. Hirata, J. et al. Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population. Nat. Genet. 51, 470–480 (2019).
    Article CAS PubMed Google Scholar
  37. Lamparter, D. et al. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).
    Article PubMed PubMed Central CAS Google Scholar
  38. 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
  39. Kennedy, O. J. et al. Systematic review with meta-analysis: coffee consumption and the risk of cirrhosis. Aliment. Pharmacol. Ther. 47, 562–5 (2016).
    Article Google Scholar
  40. Inoue, M., Yoshimi, I., Sobue, T. & Tsugane, S. Influence of coffee drinking on subsequent risk of hepatocellular carcinoma: a prospective study in Japan. J. Natl Cancer Inst. 97, 293–300 (2005).
    Article CAS PubMed Google Scholar
  41. Panahi, S., Fernandez, M. A., Marette, A. & Tremblay, A. Yogurt, diet quality and lifestyle factors. Eur. J. Clin. Nutr. 71, 573–579 (2017).
    Article CAS PubMed Google Scholar
  42. D’Addezio, L., Mistura, L., Sette, S. & Turrini, A. Sociodemographic and lifestyle characteristics of yogurt consumers in Italy: results from the INRAN-SCAI 2005-06 survey. Med. J. Nutr. Metab. 8, 119–129 (2015).
    Article Google Scholar
  43. Kot, M. & Daniel, W. A. Effect of cytochrome P450 (CYP) inducers on caffeine metabolism in the rat. Pharmacol. Rep. 59, 296–305 (2007).
  44. Zanger, U. M. & Schwab, M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther. 138, 103–141 (2013).
    Article CAS PubMed Google Scholar
  45. Berthou, F. et al. Evidence for the involvement of several cytochromes P-450 in the first steps of caffeine metabolism by human liver microsomes. Drug Metab. Dispos. 19, 561–567 (1991).
    CAS PubMed Google Scholar
  46. Wang, L.-X., Wen, S., Wang, C.-C., Zhou, B. & Li, H. Molecular adaption of alcohol metabolism to agriculture in East Asia. Quat. Int. 426, 187–194 (2016).
    Article Google Scholar
  47. Way, M. J., Ali, M. A., McQuillin, A. & Morgan, M. Y. Genetic variants in ALDH1B1 and alcohol dependence risk in a British and Irish population: a bioinformatic and genetic study. PLoS One 12, e0177009 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  48. Linneberg, A. et al. Genetic determinants of both ethanol and acetaldehyde metabolism influence alcohol hypersensitivity and drinking behaviour among Scandinavians. Clin. Exp. Allergy 40, 123–130 (2009).
    Article CAS Google Scholar
  49. Husemoen, L. L. N. et al. The association of ADH and ALDH gene variants with alcohol drinking habits and cardiovascular disease risk factors. Alcohol. Clin. Exp. Res. 32, 1984–1991 (2008).
    CAS PubMed Google Scholar
  50. Cornelis, M. C., El-Sohemy, A. & Campos, H. Genetic polymorphism of the adenosine A2A receptor is associated with habitual caffeine consumption. Am. J. Clin. Nutr. 86, 240–244 (2007).
    Article CAS PubMed Google Scholar
  51. Matoba, N. et al. GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture. Nat. Hum. Behav. 3, 471–477 (2019).
    Article PubMed Google Scholar

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Acknowledgements

We are grateful to all participants enrolled in BBJ. We thank all the clinicians and organizations that contributed to the collection of samples and clinical information. This research was supported by the Tailor-Made Medical Treatment Programme (BBJ) of the Ministry of Education, Culture, Sports, Science and Technology and the Japan Agency for Medical Research and Development (AMED; grant nos. JP17km0305002, JP19km0405201 and JP19km045208), and by the Strategic Research Programme for Brain Sciences of AMED (no. JP19dm0107097). Y.O. was supported by the Japan Society for the Promotion of Science, KAKENHI (nos. 15H05911 and 19H01021), AMED (nos. JP19gm6010001, JP19ek0410041, JP19ek0109413 and JP19km0405211), Takeda Science Foundation and the Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University and Osaka University Centre of Medical Data Science, Advanced Clinical Epidemiology Investigator’s Research Project. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
    Nana Matoba, Masato Akiyama, Kazuyoshi Ishigaki, Masahiro Kanai, Atsushi Takahashi & Yoichiro Kamatani
  2. Department of Genetics, UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
    Nana Matoba
  3. Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
    Masato Akiyama
  4. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
    Masahiro Kanai
  5. Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Suita, Japan
    Atsushi Takahashi
  6. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
    Yukihide Momozawa
  7. Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
    Shiro Ikegawa
  8. Department of Psychiatry, Fujita Health University School of Medicine, Toyotake, Japan
    Masashi Ikeda & Nakao Iwata
  9. Institute of Medical Science, The University of Tokyo, Tokyo, Japan
    Makoto Hirata
  10. Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
    Koichi Matsuda
  11. Division of Molecular Pathology, the Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan
    Yoshinori Murakami
  12. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
    Michiaki Kubo
  13. 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
  14. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
    Yukinori Okada
  15. Laboratory of Statistical Immunology, Immunology Frontier Research Center, Osaka University, Suita, Japan
    Yukinori Okada
  16. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
    Yukinori Okada

Authors

  1. Nana Matoba
  2. Masato Akiyama
  3. Kazuyoshi Ishigaki
  4. Masahiro Kanai
  5. Atsushi Takahashi
  6. Yukihide Momozawa
  7. Shiro Ikegawa
  8. Masashi Ikeda
  9. Nakao Iwata
  10. Makoto Hirata
  11. Koichi Matsuda
  12. Yoshinori Murakami
  13. Michiaki Kubo
  14. Yoichiro Kamatani
  15. Yukinori Okada

Contributions

N.M., M.A., Y.K. and Y.O. contributed to study concept and design. M.H., K.M., Y. Murakami and M. Kubo collected and managed BBJ samples. Y. Momozawa and M. Kubo performed genotyping. N.M., M.A., K.I., M. Kanai and A.T. performed statistical analysis. S.I., M.I. and N.I. contributed to data acquisition. N.M., Y.K. and Y.O. wrote the manuscript. All authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence toYoichiro Kamatani or Yukinori Okada.

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The authors declare no competing interests.

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Editor recognition statement Primary handling editor: Stavroula Kousta

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Supplementary Tables 1, 5, 8 and 9.

Reporting Summary

Supplementary Table

Supplementary Table 2. Summary of current studies.

Supplementary Table 3. Covariates used in each association test.

Supplementary Table 4. Associations of previously reported loci.

Supplementary Table 6. Look-up results in UK BioBank.

Supplementary Table 7. Effect of taste sensitivity-related SNPs.

Supplementary Table 10. Full results of cross-trait LDSC analysis across dietary habits.

Supplementary Table 11. Description of complex diseases and laboratory measurements used in PheWAS.

Supplementary Table 12. PheWAS results.

Supplementary Table 13. Full results of heritability enrichment in ten cell type groups.

Supplementary Table 14. Full results of pathway enrichment analysis.

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Matoba, N., Akiyama, M., Ishigaki, K. et al. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits.Nat Hum Behav 4, 308–316 (2020). https://doi.org/10.1038/s41562-019-0805-1

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