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:

Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians

Nature Genetics volume 43, pages 531–538 (2011)Cite this article

Subjects

Abstract

We conducted a meta-analysis of genome-wide association studies of systolic (SBP) and diastolic (DBP) blood pressure in 19,608 subjects of east Asian ancestry from the AGEN-BP consortium followed up with de novo genotyping (n = 10,518) and further replication (n = 20,247) in east Asian samples. We identified genome-wide significant (P < 5 × 10−8) associations with SBP or DBP, which included variants at four new loci (ST7L-CAPZA1, FIGN-GRB14, ENPEP and NPR3) and a newly discovered variant near TBX3. Among the five newly discovered variants, we obtained significant replication in the independent samples for all of these loci except NPR3. We also confirmed seven loci previously identified in populations of European descent. Moreover, at 12q24.13 near ALDH2, we observed strong association signals (P = 7.9 × 10−31 and P = 1.3 × 10−35 for SBP and DBP, respectively) with ethnic specificity. These findings provide new insights into blood pressure regulation and potential targets for intervention.

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: Genome-wide association results for the AGEN-BP meta-analysis for blood pressure.
Figure 2: Regional association plots of six blood pressure loci.
Figure 3: Evidence for positive selection and pleiotropic effects at 12q24.13.
Figure 4: Plots of effect size (β) versus risk-allele frequency of 13 loci previously identified in GWAS meta-analysis of blood pressure in individuals of European descent.

Similar content being viewed by others

References

  1. Ezzati, M. et al. Selected major risk factors and global and regional burden of disease. Lancet 360, 1347–1360 (2002).

    Article  Google Scholar 

  2. Lopez, A.D. et al. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 367, 1747–1757 (2006).

    Article  Google Scholar 

  3. Lawes, C.M.M., Vander Hoorn, S. & Rodgers, A. International Society of Hypertension. Global burden of blood-pressure-related disease, 2001. Lancet 371, 1513–1518 (2008).

    Article  Google Scholar 

  4. Kearney, P.M. et al. Global burden of hypertension: analysis of worldwide data. Lancet 365, 217–223 (2005).

    Article  Google Scholar 

  5. He, J. et al. Premature deaths attributable to blood pressure in China: a prospective cohort study. Lancet 374, 1765–1772 (2009).

    Article  Google Scholar 

  6. Eastern Stroke and Coronary Heart Disease Collaborative Research Group. Blood pressure, cholesterol, and stroke in eastern Asia. Lancet 352, 1801–1807 (1998).

  7. Cowley, A.W. Jr. The genetic dissection of essential hypertension. Nat. Rev. Genet. 7, 829–840 (2006).

    Article  CAS  Google Scholar 

  8. Kurtz, T.W. Genome-wide association studies will unlock the genetic basis of hypertension: con side of the argument. Hypertension 56, 1021–1025 (2010).

    Article  CAS  Google Scholar 

  9. Newton-Cheh, C. et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676 (2009).

    Article  CAS  Google Scholar 

  10. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).

    Article  CAS  Google Scholar 

  11. Dominiczak, A.F. & Munroe, P.B. Genome-wide association studies will unlock the genetic basis of hypertension: pro side of the argument. Hypertension 56, 1017–1020 (2010).

    Article  CAS  Google Scholar 

  12. Anand-Srivastava, M.B. Natriuretic peptide receptor-C signaling and regulation. Peptides 26, 1044–1059 (2005).

    Article  CAS  Google Scholar 

  13. Takeuchi, F. et al. Blood pressure and hypertension are associated with 7 loci in the Japanese population. Circulation 121, 2302–2309 (2010).

    Article  Google Scholar 

  14. Chen, L., Davey Smith, G., Harbord, R.M. & Lewis, S.J. Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach. PLoS Med. 5, e52 (2008).

    Article  Google Scholar 

  15. Tsuchihashi-Makaya, M. et al. Gene-environmental interaction regarding alcohol-metabolizing enzymes in the Japanese general population. Hypertens. Res. 32, 207–213 (2009).

    Article  CAS  Google Scholar 

  16. Li, H. et al. Refined geographic distribution of the oriental ALDH2*504Lys (nee 487Lys) variant. Ann. Hum. Genet. 73, 335–345 (2009).

    Article  CAS  Google Scholar 

  17. Takeuchi, F. et al. Confirmation of ALDH2 as a major locus of drinking behavior and of its variants regulating multiple metabolic phenotypes in Japanese. Circ. J. 75, 911–918 (2011).

    Article  CAS  Google Scholar 

  18. Voight, B.F., Kudaravalli, S., Wen, X. & Pritchard, J.K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).

    Article  Google Scholar 

  19. Sabeti, P.C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007).

    Article  CAS  Google Scholar 

  20. Soranzo, N. et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat. Genet. 41, 1182–1190 (2009).

    Article  CAS  Google Scholar 

  21. Zollner, S. & Pritchard, J.K. Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am. J. Hum. Genet. 80, 605–615 (2007).

    Article  CAS  Google Scholar 

  22. Ong, R.T. & Teo, Y.Y. varLD: a program for quantifying variation in linkage disequilibrium patterns between populations. Bioinformatics 26, 1269–1270 (2010).

    Article  CAS  Google Scholar 

  23. Stranger, B.E. et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).

    Article  CAS  Google Scholar 

  24. Dixon, A.L. et al. A genome-wide association study of global gene expression. Nat. Genet. 39, 1202–1207 (2007).

    Article  CAS  Google Scholar 

  25. Mizutani, S. et al. New insights into the importance of aminopeptidase A in hypertension. Heart Fail. Rev. 13, 273–284 (2008).

    Article  CAS  Google Scholar 

  26. Cox, G.A., Mahaffey, C.L., Nystuen, A., Letts, V.A. & Frankel, W.N. The mouse fidgetin gene defines a new role for AAA family proteins in mammalian development. Nat. Genet. 26, 198–202 (2000).

    Article  CAS  Google Scholar 

  27. Goenaga, D. et al. Molecular determinants of Grb14-mediated inhibition of insulin signaling. Mol. Endocrinol. 23, 1043–1051 (2009).

    Article  CAS  Google Scholar 

  28. Heid, I.M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).

    Article  CAS  Google Scholar 

  29. Katoh, M. Molecular cloning and characterization of ST7R (ST7-like, ST7L) on human chromosome 1p13, a novel gene homologous to tumor suppressor gene ST7 on human chromosome 7q31. Int. J. Oncol. 20, 1247–1253 (2002).

    CAS  PubMed  Google Scholar 

  30. Brooks, P.J., Enoch, M.A., Goldman, D., Li, T.K. & Yokoyama, A. The alcohol flushing response: an unrecognized risk factor for esophageal cancer from alcohol consumption. PLoS Med. 6, e50 (2009).

    Article  Google Scholar 

  31. Harada, S., Misawa, S., Agarwal, D.P. & Goedde, H.W. Liver alcohol dehydrogenase and aldehyde dehydrogenase in the Japanese: isozyme variation and its possible role in alcohol intoxication. Am. J. Hum. Genet. 32, 8–15 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Barter, P.J. et al. Effects of torcetrapib in patients at high risk for coronary events. N. Engl. J. Med. 357, 2109–2122 (2007).

    Article  CAS  Google Scholar 

  33. Tabara, Y. et al. Common variants in the ATP2B1 gene are associated with susceptibility to hypertension: the Japanese Millennium Genome Project. Hypertension 56, 973–980 (2010).

    Article  CAS  Google Scholar 

  34. Hong, K.W. et al. Recapitulation of two genomewide association studies on blood pressure and essential hypertension in the Korean population. J. Hum. Genet. 55, 336–341 (2010).

    Article  CAS  Google Scholar 

  35. Johnson, A.D. et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24, 2938–2939 (2008).

    Article  CAS  Google Scholar 

  36. Pickrell, J.K. et al. Signals of recent positive selection in a worldwide sample of human populations. Genome Res. 19, 826–837 (2009).

    Article  CAS  Google Scholar 

  37. 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  Google Scholar 

  38. Marchini, J. et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  39. Browning, B.L. & Browning, S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210–223 (2009).

    Article  CAS  Google Scholar 

  40. Cui, J.S., Hopper, J.L. & Harrap, S.B. Antihypertensive treatments obscure familial contributions to blood pressure variation. Hypertension 41, 207–210 (2003).

    Article  CAS  Google Scholar 

  41. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  Google Scholar 

  42. Higgins, J.P., Thompson, S.G., Deeks, J.J. & Altman, D.G. Measuring inconsistency in meta-analyses. Br. Med. J. 327, 557–560 (2003).

    Article  Google Scholar 

  43. Ge, D. et al. WGAViewer: software for genomic annotation of whole genome association studies. Genome Res. 18, 640–643 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the essential role of the Asian Genetic Epidemiology Network (AGEN) in developing and supporting this manuscript. AGEN members include the Cardio-metabolic Genome Epidemiology (CAGE) Network, Genetic Epidemiology Network of Salt-Sensitivity (GenSalt), Korean Association Resource (KARE) Project, Shanghai Hypertension Study, Singapore Malay Eye Survey (SiMES), Singapore Prospective Study (SP2) Program, Suita Study and Taiwan Super Control Study.

CAGE: The CAGE Network Studies were supported by grants for the Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; the Program for Promotion of Fundamental Studies in Health Sciences, National Institute of Biomedical Innovation Organization (NIBIO); KAKENHI (Grant-in-Aid for Scientific Research) on Priority Areas 'Applied Genomics' from the Ministry of Education, Culture, Sports, Science and Technology of Japan; and the Grant of National Center for Global Health and Medicine (NCGM).

GenSalt: The Genetic Epidemiology Network of Salt Sensitivity is supported by research grants (U01HL072507, R01HL087263 and R01HL090682) from the National Heart, Lung, and Blood Institute, National Institutes of Health (Bethesda, Maryland, USA). T.N.K. is supported partially by Award Number K12HD043451 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (Bethesda, Maryland, USA).

KARE: KARE and HEXA-shared control studies were supported by grants from Korea Centers for Disease Control & Prevention, Republic of Korea (4845-301, 4851-302, 4851-307).

Shanghai: This work was supported by the Chinese National Key Program for Basic Research (grants 973:2004CB518603, 2006CB503804 and 2009CB521905) and Chinese National High Tech Program (grants 863:2009AA022703 and 2006AA02Z179) and the Ministry of Science and Technology, National Natural Science Foundation (30871361).

SiMES: The Singapore Malay Eye Study (SiMES) was funded by the National Medical Research Council (NMRC 0796/2003 and NMRC/STaR/0003/2008) and the Biomedical Research Council (BMRC, 09/1/35/19/616).

SP2: The Singapore Prospective Study Program (SP2) was funded through grants from the Biomedical Research Council of Singapore (BMRC 05/1/36/19/413 and 03/1/27/18/216) and the National Medical Research Council of Singapore (NMRC/1174/2008). Y.Y.T. acknowledges support from the Singapore National Research Foundation (NRF-RF-2010-05). E.S.T. also receives additional support from the National Medical Research Council through a clinician scientist award (NMRC/CSA/008/2009).

Suita/Ehime Study: The Ehime Study was supported by Grants for Scientific Research (Priority Areas 'Medical Genome Science (Millennium Genome Project)' and 'Applied Genomics') from the Ministry of Education, Culture, Sports, Science and Technology, Japan; a Grants-in-Aid (H15-longevity-005, H17-longevity-003, H16-kenko-001, H18-longevity (kokusai)) from the Ministry of Health, Labor and Welfare, Health and Labor Sciences Research Grants, Japan; a Science and Technology Incubation Program in Advanced Regions, Japan Science and Technology Agency; and the Japan Atherosclerosis Prevention Fund.

Taiwan Super Control Study: This study was supported by Academia Sinica Genomic Medicine Multicenter Study; National Research Program for Genomic Medicine, National Science Council, Taiwan (National Clinical Core, NSC97-3112-B-001-014; and National Genotyping Center, NSC97-3112-B-001-015).

Author information

Author notes

  1. Norihiro Kato, Fumihiko Takeuchi, Yasuharu Tabara, Tanika N Kelly, Min Jin Go, Xueling Sim, Wan Ting Tay, Chien-Hsiun Chen, Yi Zhang, Ken Yamamoto, Tomohiro Katsuya and Mitsuhiro Yokota: These authors contributed equally to this work.

  2. Toshio Ogihara, Dingliang Zhu, Naoharu Iwai, Jer-Yuarn Wu, Yik Ying Teo, E Shyong Tai, Yoon Shin Cho and Jiang He: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

    Norihiro Kato, Fumihiko Takeuchi & Masato Isono

  2. Department of Basic Medical Research and Education, Ehime University Graduate School of Medicine, Toon, Japan

    Yasuharu Tabara

  3. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA

    Tanika N Kelly & Jiang He

  4. Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Korea

    Min Jin Go, Young Jin Kim, Jong-Young Lee, Jae-Pil Jeon, Sung Soo Kim, Bok-Ghee Han & Yoon Shin Cho

  5. Centre for Molecular Epidemiology, National University of Singapore, Singapore

    Xueling Sim & Yik Ying Teo

  6. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore

    Wan Ting Tay, Tin Aung & Tien Yin Wong

  7. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

    Chien-Hsiun Chen, Li-ching Chang, Yuan-Tsong Chen & Jer-Yuarn Wu

  8. School of Chinese Medicine, China Medical University, Taichung, Taiwan

    Chien-Hsiun Chen & Jer-Yuarn Wu

  9. State Key Laboratory of Medical Genetics, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yi Zhang & Dingliang Zhu

  10. Shanghai Institute of Hypertension, Shanghai, China

    Yi Zhang & Dingliang Zhu

  11. Division of Genome Analysis, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan

    Ken Yamamoto

  12. Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan

    Tomohiro Katsuya

  13. Department of Genome Science, Aichi-Gakuin University, School of Dentistry, Nagoya, Japan

    Mitsuhiro Yokota

  14. National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore

    Rick Twee Hee Ong & Yik Ying Teo

  15. Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan

    Toru Nabika

  16. Cardiovascular Institute and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Dongfeng Gu

  17. Chinese National Center for Cardiovascular Diseases, Beijing, China

    Dongfeng Gu

  18. Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan

    Yoshihiro Kokubo

  19. Department of Genetics, Chinese National Human Genomic Center, Shanghai, China

    Wei Huang

  20. Department of Geriatric Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

    Keizo Ohnaka

  21. Mukogawa Women's University Institute for World Health Development, Nishinomiya, Japan

    Yukio Yamori

  22. Division of Endocrinology and Diabetes, Department of Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Eitaro Nakashima

  23. Department of Diabetes and Endocrinology, Chubu Rosai Hospital, Nagoya, Japan

    Eitaro Nakashima

  24. Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA

    Cashell E Jaquish

  25. Institute of Human Genetics, University of California, San Francisco, California, USA

    Mark Seielstad

  26. Department of Epidemiology, University of Texas School of Public Health, Houston, Texas, USA

    James E Hixson

  27. Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Japan

    Tetsuro Miki

  28. Department of Automation, Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China

    Xueya Zhou & Xuegong Zhang

  29. Institute for Adult Diseases, Asahi Life Foundation, Tokyo, Japan

    Takao Sugiyama & Yik Ying Teo

  30. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore

    Jian Jun Liu

  31. Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

    Ryoichi Takayanagi

  32. Department of Ophthalmology, National University of Singapore, Singapore

    Tin Aung & Tien Yin Wong

  33. Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA

    Yun Ju Sung

  34. Center for Eye Research Australia, University of Melbourne, Melbourne, Australia

    Tien Yin Wong

  35. Director, Shimane University Hospital, Izumo, Japan

    Shotai Kobayashi

  36. Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan

    Toshio Ogihara

  37. Department of Genomic Medicine, National Cerebral and Cardiovascular Center, Suita, Japan

    Naoharu Iwai

  38. Department of Statistics and Applied Probability, National University of Singapore, Singapore

    Yik Ying Teo

  39. Department of Epidemiology and Public Health, National University of Singapore, Singapore

    Yik Ying Teo & E Shyong Tai

  40. Department of Medicine, National University of Singapore, Singapore

    E Shyong Tai

  41. Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA

    Jiang He

Authors
  1. Norihiro Kato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fumihiko Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yasuharu Tabara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tanika N Kelly
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Jin Go
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueling Sim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wan Ting Tay
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chien-Hsiun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ken Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tomohiro Katsuya
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mitsuhiro Yokota
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Young Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Rick Twee Hee Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Toru Nabika
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Dongfeng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Li-ching Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yoshihiro Kokubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Keizo Ohnaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yukio Yamori
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Eitaro Nakashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Cashell E Jaquish
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jong-Young Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Mark Seielstad
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Masato Isono
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. James E Hixson
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Yuan-Tsong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Tetsuro Miki
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Xueya Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Takao Sugiyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Jae-Pil Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Jian Jun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Ryoichi Takayanagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Sung Soo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Tin Aung
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Yun Ju Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Xuegong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Tien Yin Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Bok-Ghee Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Shotai Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Toshio Ogihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Dingliang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Naoharu Iwai
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Jer-Yuarn Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Yik Ying Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. E Shyong Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Yoon Shin Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Jiang He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Principal investigators: N.K., J.H.

Project coordination leaders: N.K., J.H., Y.T.

Manuscript writing group: N.K., F.T., T.N.K., J.H., Y.Y.T., Y.S.C., E.S.T.

Project data management: T.N.K.

Genotyping and quality control: F.T., M.I., K.Y., Y.T., N.I., Y.K., X.S., W.T.T., Y.Y.T.

Phenotype collection, data management: CAGE: N.K., K.Y., T.K., T.N., M.Y., K.O., Y.Y., E.N., T.S., R.T., S.K., T.O.; GenSalt: T.N.K., D.G., J.H.; KARE: J.-P.J., S.S.K., Y.S.C.; Shanghai: Y.Z., X. Zhang, X. Zhou, D.Z.; SiMES/SP2: T.A., T.Y.W., E.S.T.; Suita: N.I., Y.K., Y.T., T.M.; Taiwan: C.-H.C., L.-c.C., Y.-T.C., J.-Y.W.

Genome-wide genotyping: CAGE: N.K., M.I.; GenSalt: J.E.H., Y.J.S.; KARE: J.-Y.L., B.-G.H., Y.S.C.; Shanghai: W.H.; SiMES/SP2: M.S., J.J.L.; Suita: N.I.; Taiwan: Y.-T.C., J.-Y.W.

Data analysis and data interpretation: N.K., F.T., T.N.K., Y.T., Y.Y.T., E.S.T., M.J.G., Y.J.K., X.S., W.T.T., R.T.H.O., C.-H.C., L.-c.C., C.E.J., J.H.

Corresponding author

Correspondence to Norihiro Kato.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–10 and Supplementary Tables 1–8. (PDF 2422 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kato, N., Takeuchi, F., Tabara, Y. et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet 43, 531–538 (2011). https://doi.org/10.1038/ng.834

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.834

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing