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A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits

Abstract

To identify genetic factors influencing quantitative traits of biomedical importance, we conducted a genome-wide association study in 8,842 samples from population-based cohorts recruited in Korea. For height and body mass index, most variants detected overlapped those reported in European samples. For the other traits examined, replication of promising GWAS signals in 7,861 independent Korean samples identified six previously unknown loci. For pulse rate, signals reaching genome-wide significance mapped to chromosomes 1q32 (rs12731740, P = 2.9 × 10−9) and 6q22 (rs12110693, P = 1.6 × 10−9), with the latter 400 kb from the coding sequence of GJA1. For systolic blood pressure, the most compelling association involved chromosome 12q21 and variants near the ATP2B1 gene (rs17249754, P = 1.3 × 10−7). For waist-hip ratio, variants on chromosome 12q24 (rs2074356, P = 7.8 × 10−12) showed convincing associations, although no regional transcript has strong biological candidacy. Finally, we identified two loci influencing bone mineral density at multiple sites. On chromosome 7q31, rs7776725 (within the FAM3C gene) was associated with bone density at the radius (P = 1.0 × 10−11), tibia (P = 1.6 × 10−6) and heel (P = 1.9 × 10−10). On chromosome 7p14, rs1721400 (mapping close to SFRP4, a frizzled protein gene) showed consistent associations at the same three sites (P = 2.2 × 10−3, P = 1.4 × 10−7 and P = 6.0 × 10−4, respectively). This large-scale GWA analysis of well-characterized Korean population-based samples highlights previously unknown biological pathways.

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Figure 1: Comparison of genetic background between subjects from Ansung and Ansan cohorts.
Figure 2: Quantile-quantile plots for the eight quantitative traits.
Figure 3: Six newly identified loci showing strong evidence of association.

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References

  1. Rabbee, N. & Speed, T. A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 22, 7–12 (2006).

    Article  CAS  Google Scholar 

  2. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  3. WTCCC. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  4. Weedon, M.N. et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat. Genet. 40, 575–583 (2008).

    Article  CAS  Google Scholar 

  5. Hui, L., DelMonte, T. & Ranade, K. Genotyping using the TaqMan assay. Curr. Protoc. Hum. Genet. Chapter 2 Unit 2.10 (2008).

  6. Gunderson, K.L. et al. Decoding randomly ordered DNA arrays. Genome Res. 14, 870–877 (2004).

    Article  CAS  Google Scholar 

  7. Frayling, T.M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).

    Article  CAS  Google Scholar 

  8. Loos, R.J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40, 768–775 (2008).

    Article  CAS  Google Scholar 

  9. Liu, Y.J. et al. Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum. Mol. Genet. 17, 1803–1813 (2008).

    Article  CAS  Google Scholar 

  10. Carafoli, E. The Ca2+ pump of the plasma membrane. J. Biol. Chem. 267, 2115–2118 (1992).

    CAS  PubMed  Google Scholar 

  11. Okunade, G.W. et al. Targeted ablation of plasma membrane Ca2+-ATPase (PMCA) 1 and 4 indicates a major housekeeping function for PMCA1 and a critical role in hyperactivated sperm motility and male fertility for PMCA4. J. Biol. Chem. 279, 33742–33750 (2004).

    Article  CAS  Google Scholar 

  12. Wang, G., Liszewski, M.K., Chan, A.C. & Atkinson, J.P. Membrane cofactor protein (MCP; CD46): isoform-specific tyrosine phosphorylation. J. Immunol. 164, 1839–1846 (2000).

    Article  CAS  Google Scholar 

  13. Dooley, D.C., Oppenlander, B.K. & Xiao, M. Analysis of primitive CD34− and CD34+ hematopoietic cells from adults: gain and loss of CD34 antigen by undifferentiated cells are closely linked to proliferative status in culture. Stem Cells 22, 556–569 (2004).

    Article  Google Scholar 

  14. Peters, N.S. New insights into myocardial arrhythmogenesis: distribution of gap-junctional coupling in normal, ischaemic and hypertrophied human hearts. Clin. Sci. (Lond.) 90, 447–452 (1996).

    Article  CAS  Google Scholar 

  15. Yang, B. et al. The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat. Med. 13, 486–491 (2007).

    Article  CAS  Google Scholar 

  16. Roell, W. et al. Engraftment of connexin 43-expressing cells prevents post-infarct arrhythmia. Nature 450, 819–824 (2007).

    Article  CAS  Google Scholar 

  17. Howarth, F.C., Nowotny, N., Zilahi, E., El Haj, M.A. & Lei, M. Altered expression of gap junction connexin proteins may partly underlie heart rhythm disturbances in the streptozotocin-induced diabetic rat heart. Mol. Cell. Biochem. 305, 145–151 (2007).

    Article  CAS  Google Scholar 

  18. Waerner, T. et al. ILEI: a cytokine essential for EMT, tumor formation, and late events in metastasis in epithelial cells. Cancer Cell 10, 227–239 (2006).

    Article  CAS  Google Scholar 

  19. Cho, S.W. et al. Differential effects of secreted frizzled-related proteins (sFRPs) on osteoblastic differentiation of mouse mesenchymal cells and apoptosis of osteoblasts. Biochem. Biophys. Res. Commun. 367, 399–405 (2008).

    Article  CAS  Google Scholar 

  20. Nakanishi, R. et al. Secreted frizzled-related protein 4 is a negative regulator of peak BMD in SAMP6 mice. J. Bone Miner. Res. 21, 1713–1721 (2006).

    Article  CAS  Google Scholar 

  21. Nakanishi, R. et al. Osteoblast-targeted expression of sfrp4 in mice results in low bone mass. J. Bone Miner. Res. 23, 271–277 (2008).

    Article  CAS  Google Scholar 

  22. Lettre, G. et al. Identification of ten loci associated with height highlights new biological pathways in human growth. Nat. Genet. 40, 584–591 (2008).

    Article  CAS  Google Scholar 

  23. Gudbjartsson, D.F. et al. Many sequence variants affecting diversity of adult human height. Nat. Genet. 40, 609–615 (2008).

    Article  CAS  Google Scholar 

  24. Pati, N., Schowinsky, V., Kokanovic, O., Magnuson, V. & Ghosh, S. A comparison between SNaPshot, pyrosequencing, and biplex invader SNP genotyping methods: accuracy, cost, and throughput. J. Biochem. Biophys. Methods 60, 1–12 (2004).

    Article  CAS  Google Scholar 

  25. Hyndman, R.J. & Fan, Y. Sample quantiles in statistical packages. Am. Stat. 50, 361–365 (1996).

    Google Scholar 

  26. Devlin, B., Roeder, K. & Wasserman, L. Genomic control, a new approach to genetic-based association studies. Theor. Popul. Biol. 60, 155–166 (2001).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  28. Ioannidis, J.P., Patsopoulos, N.A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS ONE 2, e841 (2007).

    Article  Google Scholar 

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Acknowledgements

This work was supported by a grant from the Ministry for Health, Welfare and Family Affairs, Republic of Korea (4845-301-430-260-00), and an intramural grant from the Korea National Institute of Health, Korea Center for Disease Control, Republic of Korea (4845-301-430-210-13).

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Contributions

The study was designed by H-.L.K., B.O., J-.K.L. and J-.Y.L. Genotyping experiments were performed by J-.E.L., J.H.O., D-.J.K., M.P., S-.H.C., H-.Y.J. and E.Y.C. DNA sample preparation was carried out by M.H.L., J-.W.K. and B-.G.H. Phenotype information was collected by H.M., Y.A., M.S.P., N.H.C. and C.S. Statistical analysis was performed by M.J.G., D.Y., H.R.H., T.P., G.C. and Y.S.C. Bioinformatic analysis was conducted by Y.J.K., J.Y.H., H-.J.B., L.C. and Y.S.C. The manuscript was written by Y.S.C., B.O., J.W.P., J-.K.L., M.I.M. and H-.L.K. All authors reviewed the manuscript.

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Correspondence to Bermseok Oh or Hyung-Lae Kim.

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Supplementary Figures 1–8, Supplementary Tables 1–9, Supplementary Methods (PDF 3008 kb)

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Cho, Y., Go, M., Kim, Y. et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41, 527–534 (2009). https://doi.org/10.1038/ng.357

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