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A new multipoint method for genome-wide association studies by imputation of genotypes

Nature Genetics volume 39, pages 906–913 (2007)Cite this article

Abstract

Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.

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Figure 1: Accuracy and calibration of imputed genotypes.
Figure 2: Power versus region-wide type I error for the mapping methods described in the main text, based on simulating case-control data sets conditional upon the haplotype data in the ten ENCODE regions.
Figure 3: Power versus region-wide type I error for mapping methods described in the main text, based on simulating case-control data sets conditional upon the haplotype data in the ten ENCODE regions.
Figure 4: Results of imputing SNPs in the region of the TCF7L2 gene from the WTCCC data.
Figure 5: Imputing missing data at genotyped SNPs.

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Acknowledgements

We thank M. McCarthy, E. Zeggini, A. Hattersley and the WTCCC for allowing us to use the TCF7L2 data. We thank M. Stephens, B. Servin, P. De Bakker, P. Fearnhead, J. Barrett, Z. Su, C. Spencer, D. Vukcevic and N. Cardin for discussions. We acknowledge support from The Wellcome Trust, the US National Institutes of Health, the SNP Consortium, the Wolfson Foundation, the Nuffield Trust and the Engineering and Physical Sciences Research Council. B.H. is supported by a National Science Foundation Graduate Research Fellowship and the Overseas Research Students Award Scheme.

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Author notes

  1. Jonathan Marchini and Bryan Howie: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK

    Jonathan Marchini, Bryan Howie, Simon Myers, Gil McVean & Peter Donnelly

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  1. Jonathan Marchini
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  2. Bryan Howie
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Correspondence to Peter Donnelly.

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Supplementary information

Supplementary Fig. 1

Overall power for methods using maximum Bayes factors. (PDF 4 kb)

Supplementary Fig. 2

Overall power for methods using region Bayes factors. (PDF 4 kb)

Supplementary Fig. 3

Bayes factor plot for TCF7L2. (PDF 177 kb)

Supplementary Methods (PDF 148 kb)

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Marchini, J., Howie, B., Myers, S. et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39, 906–913 (2007). https://doi.org/10.1038/ng2088

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