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Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Molecular Psychiatry volume 27, pages 3095–3106 (2022)Cite this article

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Abstract

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology – namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

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Fig. 1: Enrichments of PsyOPS’s three features among the five nearest genes to each genome-wide-significant psychiatric GWAS locus.
Fig. 2: Enrichments of PsyOPS’s three features among genes nearest to genome-wide-significant psychiatric and non-psychiatric GWAS variants.
Fig. 3: PsyOPS achieves state-of-the-art performance at identifying candidate causal genes from GWAS.
Fig. 4: PsyOPS-prioritized genes are more likely to have convergent evidence from DNA looping and QTLs.
Fig. 5: In-depth visualization of two loci, one where PsyOPS prioritizes the nearest gene (GABBR2, top), and one where PsyOPS prioritizes the non-nearest gene (GABBR1, bottom).

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Code availability

Code to run PsyOPS is available at https://github.com/Wainberg/PsyOPS.

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Acknowledgements

MW and SJT acknowledge support from the Kavli Foundation, Krembil Foundation, CAMH Discovery Fund, the McLaughlin Foundation, NSERC (RGPIN-2020-05834 and DGECR-2020-00048) and CIHR (NGN-171423). The authors gratefully acknowledge Naomi Wray for helpful feedback on the manuscript.

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Authors and Affiliations

  1. Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada

    Michael Wainberg & Shreejoy J. Tripathy

  2. Deep Genomics Inc, Toronto, ON, Canada

    Daniele Merico

  3. The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada

    Daniele Merico

  4. Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA

    Matthew C. Keller

  5. Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA

    Matthew C. Keller

  6. Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA

    Eric B. Fauman

  7. Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada

    Shreejoy J. Tripathy

  8. Department of Psychiatry, University of Toronto, Toronto, ON, Canada

    Shreejoy J. Tripathy

  9. Department of Physiology, University of Toronto, Toronto, ON, Canada

    Shreejoy J. Tripathy

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  1. Michael Wainberg
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Contributions

MW and SJT designed the study; MW performed analyses; SJT supervised the study; MW wrote the manuscript with key input from DM, MCK, and EBF.

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Correspondence to Shreejoy J. Tripathy.

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DM is a full-time employee and stockholder of Deep Genomics. EBF is affiliated with Pfizer Worldwide Research, but contributed as an individual and the work was not part of a Pfizer collaboration nor was it funded by Pfizer; the author has no financial interests to declare. MW, SJT, and MCK declare no competing interests.

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Wainberg, M., Merico, D., Keller, M.C. et al. Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge. Mol Psychiatry 27, 3095–3106 (2022). https://doi.org/10.1038/s41380-022-01542-6

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