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Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

Nature Genetics volume 44, pages 369–375 (2012)Cite this article

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Abstract

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

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Acknowledgements

The authors thank the staff and participants of the ARIC study for their important contributions. We thank all referees for many constructive comments and suggestions. We acknowledge funding from the Australian National Health and Medical Research Council (NHMRC; 241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485, 613672, 613601 and 1011506), the US National Institutes of Health (AA07535, AA10248, AA014041, AA13320, AA13321, AA13326 and DA12854) and the Australian Research Council (ARC; DP0770096 and DP1093502). A.P.M. acknowledges financial support from the Wellcome Trust (WT081682/Z/06/Z). M.I.M. acknowledges financial support from the Wellcome Trust (WT083270). The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by contracts with the National Heart, Lung, and Blood Institute (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), the XXX (R01HL087641, R01HL59367 and R01HL086694), the US National Human Genome Research Institute (U01HG004402) and the US National Institutes of Health (HHSN268200625226C). Infrastructure was partly supported by a component of the US National Institutes of Health and NIH Roadmap for Medical Research (UL1RR025005).

Author information

Authors and Affiliations

  1. Queensland Institute of Medical Research, Brisbane, Queensland, Australia

    Jian Yang, Sarah E Medland, Nicholas G Martin, Grant W Montgomery & Peter M Visscher

  2. University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia

    Jian Yang & Peter M Visscher

  3. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Teresa Ferreira, Andrew P Morris & Mark I McCarthy

  4. Department of Psychiatry, Washington University, Saint Louis, Missouri, USA

    Pamela A F Madden & Andrew C Heath

  5. Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK

    Michael N Weedon & Timothy M Frayling

  6. Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK

    Ruth J Loos

  7. Oxford Centre for Diabetes, Endocrinology and Metabolism, Headington, Oxford, UK

    Mark I McCarthy

  8. Program in Genomics, Children's Hospital, Boston, Massachusetts, USA

    Joel N Hirschhorn

  9. Division of Genetics, Children's Hospital, Boston, Massachusetts, USA

    Joel N Hirschhorn

  10. Division of Endocrinology, Children's Hospital, Boston, Massachusetts, USA

    Joel N Hirschhorn

  11. Broad Institute, Cambridge, Massachusetts, USA

    Joel N Hirschhorn

  12. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Joel N Hirschhorn

  13. Department of Food and Agricultural Systems, The University of Melbourne, Parkville, Victoria, Australia

    Michael E Goddard

  14. Biosciences Research Division, Department of Primary Industries, Bundoora, Victoria, Australia

    Michael E Goddard

  15. The Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia

    Peter M Visscher

Authors
  1. Jian Yang
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  2. Teresa Ferreira
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  3. Andrew P Morris
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Consortia

Genetic Investigation of ANthropometric Traits (GIANT) Consortium

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

Contributions

P.M.V. and M.E.G. designed the study. M.E.G., J.Y. and P.M.V. derived the analytical results. J.Y. performed all statistical analyses. J.Y. and P.M.V. wrote the first draft of the paper. M.N.W., R.J.L., T.M.F., M.I.M. and J.N.H. contributed the summary data of the height and BMI meta-analyses on behalf of the GIANT Consortium and provided comments that improved earlier versions of the manuscript. T.F., A.P.M. and M.I.M. contributed the summary data of the T2D meta-analysis on behalf of the DIAGRAM Consortium. S.E.M., P.A.F.M., A.C.H., N.G.M. and G.W.M. contributed the individual-level and imputed genotypes and phenotype data of the QIMR cohort.

Corresponding author

Correspondence to Peter M Visscher.

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Competing interests

The authors declare no competing financial interests.

Additional information

A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 1–5 and Supplementary Note (PDF 5267 kb)

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Yang, J., Ferreira, T., Morris, A. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44, 369–375 (2012). https://doi.org/10.1038/ng.2213

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