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