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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

Nature Genetics volume 47, pages 291–295 (2015)Cite this article

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Abstract

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

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Figure 1: Results from selected simulations.
Figure 2: LD Score regression plot for the most recent schizophrenia meta-analysis.

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Acknowledgements

We would like to thank P. Sullivan for helpful discussion. This work was supported by US National Institutes of Health grants F32 HG007805 (P.-R.L.), R01 HG006399 (A.L.P.), R03 CA173785 (H.K.F.) and R01 MH094421 (PGC) and by the Fannie and John Hertz Foundation (H.K.F.). Data on coronary artery disease and myocardial infarction were contributed by CARDIoGRAMplusC4D investigators and were downloaded from Psychiatric Genomics Consortium.

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Authors and Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Brendan K Bulik-Sullivan, Po-Ru Loh, Nick Patterson, Mark J Daly, Alkes L Price & Benjamin M Neale

  2. Department of Medicine, Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Brendan K Bulik-Sullivan, Stephan Ripke, Mark J Daly & Benjamin M Neale

  3. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Brendan K Bulik-Sullivan, Stephan Ripke, Mark J Daly & Benjamin M Neale

  4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Po-Ru Loh, Hilary K Finucane & Alkes L Price

  5. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Hilary K Finucane

  6. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Jian Yang

  7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Alkes L Price

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  1. Brendan K Bulik-Sullivan
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  9. Benjamin M Neale
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Schizophrenia Working Group of the Psychiatric Genomics Consortium

Contributions

B.K.B.-S. conceived the idea, analyzed the data, performed the analyses and drafted the manuscript. B.M.N. conceived the idea and drafted the manuscript. M.J.D. conceived the idea and supplied reagents. N.P. conceived the idea and supplied reagents. A.L.P. conceived the idea and supplied reagents. P.-R.L. analyzed the data and performed the analyses. H.K.F. analyzed the data and performed the analyses. S.R. analyzed the data and performed the analyses. J.Y. provided software. All authors provided input and revisions for the final manuscript.

Corresponding author

Correspondence to Benjamin M Neale.

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The authors declare no competing financial interests.

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A full list of members and affiliations appears in the Supplementary Note.

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Supplementary Note, Supplementary Figures 1–9 and Supplementary Tables 1–10. (PDF 1264 kb)

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Bulik-Sullivan, B., Loh, PR., Finucane, H. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291–295 (2015). https://doi.org/10.1038/ng.3211

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