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Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies

Nature Genetics volume 45, pages 400–405 (2013)Cite this article

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Abstract

We report a new method to estimate the predictive performance of polygenic models for risk prediction and assess predictive performance for ten complex traits or common diseases. Using estimates of effect-size distribution and heritability derived from current studies, we project that although 45% of the variance of height has been attributed to SNPs, a model trained on one million people may only explain 33.4% of variance of the trait. Models based on current studies allow for identification of 3.0%, 1.1% and 7.0% of the populations at twofold or higher than average risk for type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate these percentages to 18.8%, 6.1% and 12.2%, respectively. The utility of polygenic models for risk prediction will depend on achievable sample sizes for the training data set, the underlying genetic architecture and the inclusion of information on other risk factors, including family history.

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Figure 1: PCC for polygenic models and corresponding optimal significance level for SNP selection under three models for polygenic architectures for adult height.
Figure 2: Expected PCC for polygenic models at optimal significance level for SNP selection for four quantitative traits.
Figure 3: Expected AUC statistics at optimal significance level for SNP selection for five disease traits.

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Acknowledgements

This research was supported by the intramural program of the US National Cancer Institute.

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Authors and Affiliations

  1. Division of Cancer Epidemiology and Genetics, Department of Human and Human Services, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA

    Nilanjan Chatterjee, Joshua Sampson, Patricia Hartge, Stephen J Chanock & Ju-Hyun Park

  2. Information Management System, Rockville, Maryland, USA

    Bill Wheeler

  3. Department of Statistics, Dongguk University–Seoul, Seoul, South Korea

    Ju-Hyun Park

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  1. Nilanjan Chatterjee
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  2. Bill Wheeler
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  4. Patricia Hartge
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Contributions

N.C. led the development of the statistical methods and drafted the manuscript. J.-H.P. contributed to the development of the methods and performed the illustrative analyses. B.W. implemented simulation studies. J.S., P.H. and S.J.C. contributed to designs of various analyses and interpretation of results. N.C., B.W., J.S., P.H., S.J.C. and J.-H.P. reviewed and revised the manuscript.

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Correspondence to Nilanjan Chatterjee.

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The authors declare no competing financial interests.

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Supplementary Tables 1–4, Supplementary Figures 1–3, Supplementary Note (PDF 329 kb)

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Chatterjee, N., Wheeler, B., Sampson, J. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet 45, 400–405 (2013). https://doi.org/10.1038/ng.2579

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