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Estimating coverage and power for genetic association studies using near-complete variation data

Abstract

Although studies suggest that SNPs derived from HapMap provide promising coverage and power for association studies, the lack of alternative variation datasets limits independent analysis. Using near-complete variation data for 76 genes resequenced in HapMap samples, we find that coverage of common variation by commercial genotyping arrays is substantially lower compared to the HapMap-based estimates. We quantify the power offered by these arrays for a range of disease models.

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Figure 1: Coverage of the near-complete variation by HapMap and SNP sets derived from HapMap.
Figure 2: Estimates of power for different disease models.

References

  1. The International HapMap Consortium. Nature 437, 1299–1320 (2005).

    Article  Google Scholar 

  2. Ke, X. et al. Hum. Mol. Genet. 14, 2757–2767 (2005).

    Article  CAS  Google Scholar 

  3. Clark, A.G. et al. Genome Res. 15, 1496–1502 (2005).

    Article  CAS  Google Scholar 

  4. Pe'er, I. et al. Nat. Genet. 38, 663–667 (2006).

    Article  CAS  Google Scholar 

  5. Barrett, J.C. & Cardon, L.R. Nat. Genet. 38, 659–662 (2006).

    Article  CAS  Google Scholar 

  6. de Bakker, P.I. et al. Nat. Genet. 38, 1298–1303 (2006).

    Article  CAS  Google Scholar 

  7. Marchini, J. et al. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  8. Servin, B. & Stephens, M. PLoS Genet. 3, e114 (2007).

    Article  Google Scholar 

  9. Schork, N.J. Am. J. Hum. Genet. 70, 1480–1489 (2002).

    Article  CAS  Google Scholar 

  10. de Bakker, P.I. et al. Nat. Genet. 37, 1217–1223 (2005).

    Article  CAS  Google Scholar 

  11. Zeggini, E. et al. Science 316, 1336–1341 (2007).

    Article  CAS  Google Scholar 

  12. Scott, L.J. et al. Science 316, 1341–1345 (2007).

    Article  CAS  Google Scholar 

  13. Saxena, R. et al. Science 316, 1331–1336 (2007).

    Article  CAS  Google Scholar 

  14. Kaiser, J. Science 319, 395 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank the past and present members of the SeattleSNPs team for their efforts in variation identification. This work was supported by grants from the US National Institute of Health (HL66682 and HL66642 to D.A.N. and M.J.R.).

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Correspondence to Tushar R Bhangale or Deborah A Nickerson.

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Supplementary Methods, Supplementary Note, Supplementary Figures 1–3 and Supplementary Tables 1–3 (PDF 1120 kb)

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Bhangale, T., Rieder, M. & Nickerson, D. Estimating coverage and power for genetic association studies using near-complete variation data. Nat Genet 40, 841–843 (2008). https://doi.org/10.1038/ng.180

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