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Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis | Nature Genetics
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Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Nature Genetics volume 47, pages 1385–1392 (2015)Cite this article

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Abstract

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

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Figure 1: Computational performance of the BOLT-REML and GCTA heritability analysis algorithms.
Figure 2: Extreme polygenicity of schizophrenia in comparison to other complex diseases.
Figure 3: SNP heritability of disease liabilities partitioned by GC content.
Figure 4: Inferred heritability of schizophrenia liability due to SNPs of various allele frequencies.
Figure 5: Genetic correlations and total correlations of GERA disease liabilities.

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Acknowledgements

We are grateful to K. Galinsky, T. Hayeck, P. Palamara, J. Listgarten, V. Anttila, S. Sunyaev, D. Howrigan, R. Walters, P. Sullivan, M. Keller, M. Goddard, P. Visscher, J. Yang, S. Ripke, D. Golan and S. Rosset for helpful discussions. This research was supported by US National Institutes of Health grants R01 HG006399 and R01 MH101244 and US National Institutes of Health fellowship F32 HG007805. H.K.F. was supported by the Fannie and John Hertz Foundation. Members of the Schizophrenia Working Group of the Psychiatric Genomics Consortium are listed in the Supplementary Note. Statistical analyses of PGC2 data were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org/) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (NWO 480-05-003, principal investigator D. Posthuma) along with a supplement from the Dutch Brain Foundation and VU University Amsterdam. Analyses of GERA data were conducted on the Orchestra High-Performance Compute Cluster at Harvard Medical School, which is partially supported by US National Center for Research Resources grant 1S10RR028832-01.

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Authors and Affiliations

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

    Po-Ru Loh, Gaurav Bhatia, Alexander Gusev, Samuela J Pollack & Alkes L Price

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

    Po-Ru Loh, Gaurav Bhatia, Alexander Gusev, Brendan K Bulik-Sullivan, Samuela J Pollack, Benjamin M Neale, Nick Patterson & Alkes L Price

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

    Hilary K Finucane

  4. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Brendan K Bulik-Sullivan & Benjamin M Neale

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

    Samuela J Pollack & Alkes L Price

  6. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, USA

    Teresa R de Candia

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

    Sang Hong Lee & Naomi R Wray

  8. School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia

    Sang Hong Lee

  9. Department of Psychiatry and Human Genetics, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA

    Kenneth S Kendler

  10. Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK

    Michael C O'Donovan

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  1. Po-Ru Loh
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Schizophrenia Working Group of the Psychiatric Genomics Consortium

Contributions

P.-R.L., N.P. and A.L.P. designed experiments. P.-R.L. performed experiments. P.-R.L., G.B., A.G., H.K.F., B.K.B.-S., S.J.P. and A.L.P. analyzed data. All authors wrote the manuscript.

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Correspondence to Po-Ru Loh or Alkes L Price.

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A full list of members is provided in the Supplementary Note

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Supplementary Figures 1–20, Supplementary Tables 1–19 and Supplementary Note. (PDF 2803 kb)

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Loh, PR., Bhatia, G., Gusev, A. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet 47, 1385–1392 (2015). https://doi.org/10.1038/ng.3431

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