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Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits

Nature Genetics volume 50, pages 1318–1326 (2018)Cite this article

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Abstract

We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.

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Fig. 1: Q-Q plots comparing observed distributions of association statistics against those expected under the fitted models for three representative traits.
Fig. 2: Estimated effect-size distributions for susceptibility SNPs based on the best fitted (M2 or M3) model for continuous (top) and binary traits (bottom).
Fig. 3: Projected number of discoveries (top) and corresponding percentage of GWAS heritability explained (bottom) based on the best-fitted (M2 or M3) model for effect-size distribution for continuous (left) and binary (right) traits.
Fig. 4: Expected area under the curve (AUC) for polygenic risk prediction models with SNPs included at the optimal significance (α) threshold (red solid line) and at the genome-wide significance level of 5 × 10–8 (black solid line).

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Acknowledgements

The authors thank H. Zhang for assistance with computing, P. Kundu for data management for the UK Biobank data, and M. Chatterjee for editing of manuscript. The research was supported by Bloomberg Distinguished Professorship endowment. Some of the simulation studies were conducted using genotype data from the UK Biobank Resource accessed under Application Number 17712.

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Authors and Affiliations

  1. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Yan Zhang, Guanghao Qi & Nilanjan Chatterjee

  2. Department of Statistics, Dongguk University, Seoul, Republic of Korea

    Ju-Hyun Park

  3. Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Nilanjan Chatterjee

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Contributions

Y.Z., G.Q., and N.C. conceived the methods. Y.Z., G.Q., and J.-H.P. carried out all analyses. Y.Z. and N.C. wrote the manuscript. All authors reviewed the manuscripts.

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Correspondence to Nilanjan Chatterjee.

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Zhang, Y., Qi, G., Park, JH. et al. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat Genet 50, 1318–1326 (2018). https://doi.org/10.1038/s41588-018-0193-x

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