Abstract

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome through pilot WGS projects will be critical to determining which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The Whole Genome Sequencing for Psychiatric Disorders Consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

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Change history

  • Correction 16 March 2018

    In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.

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Acknowledgements

The authors acknowledge and thank the study participants and their families. The WGSPD is a public–private partnership between the NIMH, the Stanley Center for Psychiatric Research, and researchers at 11 academic institutions across the USA. This work was supported by grants from the NIMH, specifically U01 MH105653 (M.B.), U01 MH105641 (S.A.M.), U01 MH105573 (C.N.P.), U01 MH105670 (D.B.G.), U01 MH105575 (M.W.S., A.J.W.), U01 MH105669 (M.J.D., K.E.), U01 MH105575 (N.B.F., D.H.G., R.A.O.), U01 MH105666 (A.P.), U01 MH105630 (D.C.G.), U01 MH105632 (J.B.), U01 MH105634 (R.E.G.), U01 MH100239-03S1 (M.W.S., S.J.S., A.J.W.), R01 MH095454 (N.B.F.); by grants from the Simons Foundation (SFARI #385110, M.W.S., S.J.S., A.J.W., D.B.G., SFARI #401457 (D.H.G.)); and by a gift from the Stanley Foundation (S.E.H.).

Author information

Author notes

  1. A full of list of members appears in the Supplementary Note.

  2. Stephan J. Sanders and Benjamin M. Neale contributed equally to this work.

Affiliations

  1. Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    • Stephan J. Sanders
    • , Donna M. Werling
    • , Joon-Yong An
    • , Shan Dong
    • , Matthew W. State
    •  & A. Jeremy Willsey
  2. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Benjamin M. Neale
    • , Hailiang Huang
    •  & Mark J. Daly
  3. Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Benjamin M. Neale
    • , Hailiang Huang
    • , Mark J. Daly
    • , Kevin Eggan
    • , Robert E. Handsaker
    • , Steven A. McCarroll
    • , Aarno Palotie
    •  & Steven E. Hyman
  4. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    • Goncalo Abecasis
    • , Michael Boehnke
    • , Anjene M. Addington
    •  & Thomas Lehner
  5. National Institute of Mental Health, Bethesda, MD, USA

    • P. Alexander Arguello
  6. South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, School of Medicine, Brownsville, TX, USA

    • John Blangero
  7. Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    • Daniel H. Geschwind
  8. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Daniel H. Geschwind
  9. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    • David C. Glahn
  10. Institute for Genomic Medicine, Columbia University Medical Center, Hammer Health Sciences, New York, NY, USA

    • David B. Goldstein
  11. Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Raquel E. Gur
  12. Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands

    • Roel A. Ophoff
    •  & Nelson B. Freimer
  13. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA

    • Roel A. Ophoff
  14. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    • Roel A. Ophoff
  15. Department of Psychiatry, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • Carlos N. Pato
  16. Department of Health Research and Policy, Division of Biostatistics, Stanford University, Stanford, CA, USA

    • Chiara Sabatti

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Consortia

  1. Whole Genome Sequencing for Psychiatric Disorders (WGSPD)

    Contributions

    S.J.S., B.M.N., H.H., and D.M.W. contributed to the power calculation. All authors contributed to the text.

    Competing interests

    The authors declare no competing financial interests.

    Corresponding authors

    Correspondence to Steven E. Hyman or Anjene M. Addington or Thomas Lehner or Nelson B. Freimer.

    Integrated supplementary information

    1. Supplementary Figure 1 Statistical power in the noncoding genome by cohort size, related to Figure 1 in the main manuscript.

      We estimated the power at a significance threshold (alpha) of 5 × 10−5, accounting for 1,000 categories of noncoding variants, to detect an excess of noncoding variants at 122,500 risk loci in cases vs. controls as we varied the sample size and risk:non-risk ratio, which represents annotation quality (Supplementary Tables 1 and 3). In a) we assessed the power for detecting an excess of de novo mutations at a relative risk of 5 as sample size increases. With a risk:non-risk ratio of 1:20, approximately equivalent to assessing protein truncating variants in the coding genome, we achieve >80% power with a sample size of 5,000. In b) the power to detect an excess burden of rare variants (allele frequency ≤0.1%) is assessed at a relative risk of 1.2. In c) we assessed the power to identify an excess of de novo mutations at a specific genomic locus, e.g. the noncoding region regulating a single gene. Consequently, we set the significance threshold (alpha) at 2.5 × 10−6 to account for 20,000 genes. In d) we assessed the power to identify an excess of rare variants (allele frequency ≤0.1%) at a specific nucleotide (alpha = 1.7 × 10−11), since this yielded better power than testing for burden at a locus (alpha = 2.5 × 10−6).

    Electronic supplementary material

    1. Supplementary Text and Figures

      Supplementary Figure 1, Supplementary Tables 1–4, and Supplementary Experimental Procedures

    2. Supplementary Methods

    3. Supplementary Tables

    4. Supplementary Note