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

Nature Genetics 47, 291–295 (2015) | Download Citation

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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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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.

Author information

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. Analytical and Translational Genetics Unit, Department of Medicine, 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. Schizophrenia Working Group of the Psychiatric Genomics Consortium

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

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Benjamin M Neale.

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    Supplementary Note, Supplementary Figures 1–9 and Supplementary Tables 1–10.

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DOI

https://doi.org/10.1038/ng.3211

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