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Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders

Molecular Psychiatry volume 26, pages 2070–2081 (2021)Cite this article

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Abstract

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium—schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

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Fig. 1: Forest plots for the four most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes.
Fig. 2: Results from MAGMA brain cell-type enrichment analyses of raw and conditional GWAS analyses.

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Scripts used to generate the results are available on request from the corresponding author.

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Acknowledgements

This work is supported by grants from the National Health and Medical Research Council of Australia (1087889, 1145645, 1113400, 1078901 and 1078037), and the Sylvia & Charles Viertel Charitable Foundation. The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320). We thank the research participants and employees of 23andMe, Inc. for contributing to this study. This paper would not have been possible without the generosity of participants in the many studies that comprise the final meta-analyses and the dedication of many clinicians and research staff who have collected the data and made them publically available. Acknowledgments for specific data sets are provided in the Supplementary Material.

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Authors and Affiliations

  1. Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia

    Enda M. Byrne, Zhihong Zhu, Ting Qi, Peter M. Visscher, Jian Yang & Naomi R. Wray

  2. Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Nathan G. Skene & Jens Hjerling-Leffler

  3. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Julien Bryois & Patrick F. Sullivan

  4. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

    Antonio F. Pardinas, Michael J. Owen, James T. R. Walters & Michael C. O’Donovan

  5. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eli Stahl

  6. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Jordan W. Smoller

  7. Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA

    Jordan W. Smoller

  8. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jordan W. Smoller

  9. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Jordan W. Smoller

  10. Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany

    Marcella Rietschel

  11. Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia

    John G. McGrath, Peter M. Visscher, Jian Yang & Naomi R. Wray

  12. Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, Brisbane, QLD, Australia

    John G. McGrath

  13. National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark

    John G. McGrath

  14. Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599-7264, USA

    Patrick F. Sullivan

  15. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599-7264, USA

    Patrick F. Sullivan

  16. Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Vic, Australia

    Michael E. Goddard

  17. Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Vic, 3083, Australia

    Michael E. Goddard

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  1. Enda M. Byrne
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Consortia

Bipolar Working Group of the Psychiatric Genomics Consortium

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Corresponding author

Correspondence to Enda M. Byrne.

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Conflict of interest

PFS is on the advisory committee at Lundbeck, is a Scientific Advisory Board member at Pfizer and has received speaker reimbursement and grant funding from Roche. JH-L. is a Scientific Advisor at Cartana and has received grant funding from Roche.

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Members of the Bipolar Working Group of the Psychiatric Genomics Consortium and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium are listed in Supplementary Information file.

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Byrne, E.M., Zhu, Z., Qi, T. et al. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders. Mol Psychiatry 26, 2070–2081 (2021). https://doi.org/10.1038/s41380-020-0705-9

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