Abstract

Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a meta-analysis of genome-wide association studies for kidney function–related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function–related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 × 10−8). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of 110,347 individuals. We identify pleiotropic associations among these loci with kidney function–related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am. J. Kidney Dis. 39, S1–S266 (2002).

  2. 2.

    , , , & Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: accuracy and use for population estimates. Am. J. Kidney Dis. 56, 32–38 (2010).

  3. 3.

    et al. Genome-wide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study. J. Am. Soc. Nephrol. 15, 2457–2461 (2004).

  4. 4.

    et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. 41, 712–717 (2009).

  5. 5.

    et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat. Genet. 42, 210–215 (2010).

  6. 6.

    et al. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 42, 373–375 (2010).

  7. 7.

    et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376–384 (2010).

  8. 8.

    et al. Association of variants at UMOD with chronic kidney disease and kidney stones—role of age and comorbid diseases. PLoS Genet. 6, e1001039 (2010).

  9. 9.

    et al. Large-scale genome-wide association studies in east Asians identify new genetic loci influencing metabolic traits. Nat. Genet. 43, 990–995 (2011).

  10. 10.

    et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat. Genet. 40, 430–436 (2008).

  11. 11.

    et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout. Nat. Genet. 40, 437–442 (2008).

  12. 12.

    et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet. 5, e1000504 (2009).

  13. 13.

    et al. Multiple genetic loci influence serum urate levels and their relationship with gout and cardiovascular disease risk factors. Circ. Cardiovasc. Genet. 3, 523–530 (2010).

  14. 14.

    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).

  15. 15.

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

  16. 16.

    et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat. Genet. 44, 307–311 (2012).

  17. 17.

    Blood urea nitrogen and creatinine. Emerg. Med. Clin. North Am. 4, 223–233 (1986).

  18. 18.

    et al. Genome-wide association study for C-reactive protein levels identified pleiotropic associations in the IL6 locus. Hum. Mol. Genet. 20, 1224–1231 (2011).

  19. 19.

    et al. A novel alteration in metaxin 1, F202L, is associated with N370S in Gaucher disease. J. Hum. Genet. 49, 220–222 (2004).

  20. 20.

    et al. Expression of PAX8 in normal and neoplastic renal tissues: an immunohistochemical study. Mod. Pathol. 22, 1218–1227 (2009).

  21. 21.

    et al. Evi1 is essential for hematopoietic stem cell self-renewal, and its expression marks hematopoietic cells with long-term multilineage repopulating activity. J. Exp. Med. 208, 2403–2416 (2011).

  22. 22.

    MHC Sequencing Consortium. Complete sequence and gene map of a human major histocompatibility complex. Nature 401, 921–923 (1999).

  23. 23.

    et al. A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat. Genet. 38, 1166–1172 (2006).

  24. 24.

    et al. Paired-related murine homeobox gene expressed in the developing sclerotome, kidney, and nervous system. Dev. Dyn. 210, 53–65 (1997).

  25. 25.

    et al. The evolving doublecortin (DCX) superfamily. BMC Genomics 7, 188 (2006).

  26. 26.

    et al. Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six loci influencing serum magnesium levels. PLoS Genet. 6, e1001045 (2010).

  27. 27.

    et al. A novel approach to investigation of the pathogenesis of active minimal-change nephrotic syndrome using subtracted cDNA library screening. J. Am. Soc. Nephrol. 13, 1238–1247 (2002).

  28. 28.

    , , , & A novel mutation in the DNA-binding domain of MAF at 16q23.1 associated with autosomal dominant “cerulean cataract” in an Indian family. Am. J. Med. Genet. A 140, 558–566 (2006).

  29. 29.

    et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

  30. 30.

    et al. Identification of nine novel loci associated with white blood cell subtypes in a Japanese population. PLoS Genet. 7, e1002067 (2011).

  31. 31.

    et al. Meta-analysis identifies nine new loci associated with rheumatoid arthritis in the Japanese population. Nat. Genet. 44, 511–516 (2012).

  32. 32.

    et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).

  33. 33.

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

Download references

Acknowledgements

The authors acknowledge the essential roles of AGEN in developing the study. BBJ was supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT). SP2 was funded by 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). SiMES was funded by the National Medical Research Council of Singapore (NMRC 0796/2003, IRG07nov013 and NMRC/STaR/0003/2008) and the Biomedical Research Council of Singapore (BMRC 09/1/35/19/616). SINDI and SCES were funded by grants from the Biomedical Research Council of Singapore (BMRC 09/1/35/19/616 and BMRC 08/1/35/19/550) and the National Medical Research Council of Singapore (NMRC/STaR/0003/2008). Y.-Y.T. acknowledges support from the Singapore National Research Foundation (NRF-RF-2010-05). E.-S.T. receives support from the National Medical Research Council of Singapore through a Clinician Scientist Award. We thank the Singapore BioBank and the Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, for providing services for tissue archiving and genotyping, respectively. KARE was supported by grants from the Korea Centers for Disease Control and Prevention (4845-301, 4851-302 and 4851-307) and an intramural grant from the Korea National Institute of Health (2010-N73002-00). Y.S.C. acknowledges support from a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (2012R1A2A1A03006155). TWSC and TWT2D were supported by the Academia Sinica Genomic Medicine Multicenter Study (40-05-GMM). We acknowledge the National Center for Genome Medicine (NSC100-2319-B-001-001), the National Core Facility Program for Biotechnology of the National Science Council, Taiwan, for technical help in sample management and genotyping. GenSalt was supported by grants (U01HL072507, R01HL087263 and R01HL090682) from the National Heart, Lung, and Blood Institute, the US National Institutes of Health. CAGE was supported by grants for Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; the Program for Promotion of Fundamental Studies in Health Sciences, the National Institute of Biomedical Innovation Organization (NIBIO) and the grant of National Center for Global Health and Medicine (NCGM). We thank all the people who supported the Hospital-based Cohort Study at NCGM and the Amagasaki Study. We thank A. Taniguchi, H. Rakugi, K. Sugimoto, K. Kamide and C. Makibayashi for supporting the study.

Author information

Author notes

    • Yukinori Okada
    • , Xueling Sim
    • , Min Jin Go
    • , Jer-Yuarn Wu
    • , Dongfeng Gu
    •  & Fumihiko Takeuchi

    These authors contributed equally to this work.

    • Norihiro Kato
    • , Jiang He
    • , Yuan-Tsong Chen
    • , Yoon Shin Cho
    • , E-Shyong Tai
    •  & Toshihiro Tanaka

    These authors jointly directed this work.

Affiliations

  1. Laboratory for Statistical Analysis, Center for Genomic Medicine (CGM), RIKEN, Yokohama, Japan.

    • Yukinori Okada
    •  & Atsushi Takahashi
  2. Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

    • Yukinori Okada
    •  & Kazuhiko Yamamoto
  3. Centre for Molecular Epidemiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

    • Xueling Sim
    •  & Yik-Ying Teo
  4. Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.

    • Xueling Sim
  5. Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea.

    • Min Jin Go
    • , Young Jin Kim
    • , Jong-Young Lee
    • , Bok-Ghee Han
    •  & Yoon Shin Cho
  6. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.

    • Jer-Yuarn Wu
    • , Chien-Hsiun Chen
    • , Li-Ching Chang
    • , S-J Cathy Fann
    •  & Yuan-Tsong Chen
  7. School of Chinese Medicine, China Medical University, Taichung, Taiwan.

    • Jer-Yuarn Wu
    • , Chien-Hsiun Chen
    •  & Fuu-Jen Tsai
  8. State Key Laboratory of Cardiovascular Diseases, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Dongfeng Gu
    •  & Shufeng Chen
  9. Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.

    • Fumihiko Takeuchi
    • , Masato Isono
    •  & Norihiro Kato
  10. Laboratory for Endocrinology and Metabolism, CGM, RIKEN, Yokohama, Japan.

    • Shiro Maeda
  11. Laboratory for Medical Informatics, CGM, RIKEN, Yokohama, Japan.

    • Tatsuhiko Tsunoda
  12. Saw Swee Hock School of Public Health, National University of Singapore, Singapore.

    • Peng Chen
    • , Su-Chi Lim
    • , Yik-Ying Teo
    •  & E-Shyong Tai
  13. Diabetes Centre, Khoo Teck Puat Hospital, Singapore.

    • Su-Chi Lim
  14. Department of Medicine, Khoo Teck Puat Hospital, Singapore.

    • Su-Chi Lim
  15. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

    • Tien-Yin Wong
    •  & Tin Aung
  16. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

    • Tien-Yin Wong
    •  & Tin Aung
  17. Centre for Eye Research Australia, University of Melbourne, East Melbourne, Victoria, Australia.

    • Tien-Yin Wong
  18. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore.

    • Jianjun Liu
    •  & Yik-Ying Teo
  19. Center for Human Genetics, Duke University Medical Center, Durham, North Carolina, USA.

    • Terri L Young
  20. Institute of Human Genetics, University of California, San Francisco, California, USA.

    • Mark Seielstad
  21. Department of Statistics and Applied Probability, National University of Singapore, Singapore.

    • Yik-Ying Teo
  22. National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.

    • Yik-Ying Teo
  23. Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.

    • Daehee Kang
  24. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.

    • Hao Mei
    •  & Jiang He
  25. Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Dabeeru C Rao
  26. Human Genetics Center, University of Texas School of Public Health, Houston, Texas, USA.

    • James E Hixson
  27. Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan.

    • Tomohiro Katsuya
  28. Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan.

    • Tomohiro Katsuya
    •  & Toshio Ogihara
  29. Morinomiya University of Medical Sciences, Osaka, Japan.

    • Toshio Ogihara
  30. Department of Epidemiology and Biostatistics, Imperial College London, London, UK.

    • John C Chambers
    •  & Weihua Zhang
  31. National Heart and Lung Institute, Imperial College London, London, UK.

    • Jaspal S Kooner
  32. Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany.

    • Eva Albrecht
  33. Laboratory for Genotyping Development, CGM, RIKEN, Yokohama, Japan.

    • Michiaki Kubo
  34. Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.

    • Yusuke Nakamura
  35. Laboratory for International Alliance, CGM, RIKEN, Yokohama, Japan.

    • Naoyuki Kamatani
  36. Department of Biomedical Science, Hallym University, Gangwon-do, Republic of Korea.

    • Yoon Shin Cho
  37. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

    • E-Shyong Tai
  38. Duke–National University of Singapore Graduate Medical School, Singapore.

    • E-Shyong Tai
  39. Laboratory for Cardiovascular Diseases, CGM, RIKEN, Yokohama, Japan.

    • Toshihiro Tanaka

Consortia

  1. The KidneyGen Consortium

    A full list of contributing members and affiliations is provided in the Supplementary Note.

  2. The CKDGen Consortium

    A full list of contributing members and affiliations is provided in the Supplementary Note.

  3. The GUGC consortium

    A full list of contributing members and affiliations is provided in the Supplementary Note.

Authors

  1. Search for Yukinori Okada in:

  2. Search for Xueling Sim in:

  3. Search for Min Jin Go in:

  4. Search for Jer-Yuarn Wu in:

  5. Search for Dongfeng Gu in:

  6. Search for Fumihiko Takeuchi in:

  7. Search for Atsushi Takahashi in:

  8. Search for Shiro Maeda in:

  9. Search for Tatsuhiko Tsunoda in:

  10. Search for Peng Chen in:

  11. Search for Su-Chi Lim in:

  12. Search for Tien-Yin Wong in:

  13. Search for Jianjun Liu in:

  14. Search for Terri L Young in:

  15. Search for Tin Aung in:

  16. Search for Mark Seielstad in:

  17. Search for Yik-Ying Teo in:

  18. Search for Young Jin Kim in:

  19. Search for Jong-Young Lee in:

  20. Search for Bok-Ghee Han in:

  21. Search for Daehee Kang in:

  22. Search for Chien-Hsiun Chen in:

  23. Search for Fuu-Jen Tsai in:

  24. Search for Li-Ching Chang in:

  25. Search for S-J Cathy Fann in:

  26. Search for Hao Mei in:

  27. Search for Dabeeru C Rao in:

  28. Search for James E Hixson in:

  29. Search for Shufeng Chen in:

  30. Search for Tomohiro Katsuya in:

  31. Search for Masato Isono in:

  32. Search for Toshio Ogihara in:

  33. Search for John C Chambers in:

  34. Search for Weihua Zhang in:

  35. Search for Jaspal S Kooner in:

  36. Search for Eva Albrecht in:

  37. Search for Kazuhiko Yamamoto in:

  38. Search for Michiaki Kubo in:

  39. Search for Yusuke Nakamura in:

  40. Search for Naoyuki Kamatani in:

  41. Search for Norihiro Kato in:

  42. Search for Jiang He in:

  43. Search for Yuan-Tsong Chen in:

  44. Search for Yoon Shin Cho in:

  45. Search for E-Shyong Tai in:

  46. Search for Toshihiro Tanaka in:

Contributions

Y.O. and T. Tanaka designed the overall study. Y.O., X.S., M.J.G., C.-H.C., D.G., F.T. and P.C. analyzed GWAS data. Y.O. performed meta-analysis and other statistical analysis. Y.O., A.T., S.M., T. Tsunoda, K.Y., M.K., Y.N., N. Kamatani and T. Tanaka managed GWAS data of BBJ. X.S., P.C., S.-C.L., T.-Y.W., J.L., T.L.Y., T.A., M.S., Y.-Y.T. and E.-S.T. managed the GWAS data from SP2, SiMES, SINDI and SCES. M.J.G., Y.J.K., J.-Y.L., B.-G.H., D.K. and Y.S.C. managed the GWAS data from KARE and HEXA. C.-H.C., F.-J.T., L.-C.C., S.-J.C.F., Y.-T.C. and J.-Y.W. managed the GWAS data from TWSC and TWT2D. D.G., H.M., D.C.R., J.E.H., S.C. and J.H. managed the GWAS data from GenSalt. F.T., T.K., M.I., T.O. and N. Kato managed the GWAS data from CAGE. J.C.C., W.Z. and J.S.K. managed the data from the KidneyGen Consortium. E.A. managed the data from the GUGC consortium. Y.O., T. Tanaka, E.-S.T., Y.S.C., J.-Y.W., J.H. and N. Kato directed the study and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Yukinori Okada.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Tables 1–8, Supplementary Figures 1 and 2 and Supplementary Note

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.2352

Further reading