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Conjuring SNPs to detect associations

Nature Genetics volume 39, pages 815–816 (2007)Cite this article

Human genome-wide association studies pose a challenge in identifying significant disease associations from nearly half a million statistical tests. A new report describes an especially promising approach, recently applied to the Wellcome Trust Case Control Consortium data sets, that uses the correlated structure of genomic variation to impute genotypes at missing sites and to test association with both observed and imputed SNPs.

Genetic mapping has always relied on statistical inference, but this enterprise has never been so utterly dependent on rigorous analytical methods as it is with genome-wide association studies (GWASs). For each of the nearly 500,000 SNPs in the human genome scored by widely used genotyping platforms for GWASs, it is possible to perform a simple statistical test of association with disease state. Even if the null hypothesis of no association were true for all SNPs, we would expect some of these tests to provide nominal P values on the order of 10−6. In order to avoid false-positive calls, we need to identify SNPs for which the P values are even lower. We could increase the power to appropriately reject the null hypothesis (that is, to correctly infer that a SNP is truly associated with disease) by elevating the sample size or restricting attention to intermediate-frequency SNPs and by being judicious in our choice of test. In this issue, Marchini et al.1 (page 906) show that thoughtful application of population genetic principles and use of HapMap data can provide an additional source of power for association tests. They have successfully applied these methods to the Wellcome Trust Case Control Consortium (WTCCC) data2 and have identified a collection of new genes associated with seven complex medical disorders (see pages 813–815 of this issue for discussion of the WTCCC studies).

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Figure 1: Inference of missing SNP genotypes (imputation) and the resulting improvement in the power of association tests.

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Authors and Affiliations

  1. Andrew G. Clark and Jian Li are in the Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA. ac347@cornell.edu,

    Andrew G Clark & Jian Li

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  1. Andrew G Clark
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Clark, A., Li, J. Conjuring SNPs to detect associations. Nat Genet 39, 815–816 (2007). https://doi.org/10.1038/ng0707-815

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