Estimation of effect size distribution from genome-wide association studies and implications for future discoveries

Journal name:
Nature Genetics
Volume:
42,
Pages:
570–575
Year published:
DOI:
doi:10.1038/ng.610
Received
Accepted
Published online

Abstract

We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn's disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15–20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.

At a glance

Figures

  1. Nonparametric estimates for distributions of effect sizes for susceptibility loci.
    Figure 1: Nonparametric estimates for distributions of effect sizes for susceptibility loci.

    (a) Curves based only on observed susceptibility loci; these curves are distorted because loci with larger effect sizes are more likely to have been detected. (b) Curves based on estimated susceptibility loci, representative of the population of all susceptibility loci. (c) Estimated nonparametric distributions after normalization over the common observed range for the three traits.

  2. Receiver operating characteristic curves for genetic risk models.
    Figure 2: Receiver operating characteristic curves for genetic risk models.

    (a,b) Curves for Crohn's disease (a) and BPC cancers (b). AUC is a measure of the discriminatory power of the risk model. Blue, a theoretical genetic risk model that explains all of the known familial risk of the trait. Green, a risk model that includes all of the susceptibility loci (142 for Crohn's disease and 67 on average for BPC cancers) estimated to exist within the range of effect sizes seen in the current GWASs. Red, a risk model that includes only known susceptibility loci (~30 for Crohn's disease and ~7 on average for each of the BPC cancers), which we used to estimate the distribution of effect sizes of these traits. Black, reference line corresponding to a model without discriminatory power in which cases have the same distribution of risk as controls.

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Author information

Affiliations

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

    • Ju-Hyun Park,
    • Sholom Wacholder,
    • Mitchell H Gail,
    • Stephen J Chanock &
    • Nilanjan Chatterjee
  2. Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

    • Ulrike Peters
  3. Core Genotyping Facility, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Gaithersburg, Maryland, USA.

    • Kevin B Jacobs &
    • Stephen J Chanock

Contributions

J.-H.P. and N.C. developed the statistical methods and designed the analyses. J.-H.P. implemented the methods and carried out all analyses. N.C. and S.J.C. drafted the manuscript. S.W., M.H.G., K.B.J. and U.P. made important suggestions for presentation and interpretation of the results. All the authors participated in critically reviewing the paper and approved the final version of the manuscript.

Competing financial interests

The authors declare no competing financial interests.

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