Schizophrenia is a highly heritable psychiatric disorder with a complex genetic risk architecture that reflects the additive impact of hundreds of risk variants. While many schizophrenia-associated risk variants are thought to regulate the expression of target genes in a cell-type-specific manner, the mechanisms by which the effect of these myriad variants combine to contribute to risk remain unclear. Here we apply a CRISPR-based approach to evaluate in parallel twelve schizophrenia eGenes (that encompass common variation) in human glutamatergic neurons. Querying the shared neuronal impacts across risk genes uncovers a convergent effect concentrated on pathways of brain development and synaptic signaling. Our analyses reveal shared and divergent downstream effects of these twelve genes, independent of their previously annotated biological roles. General convergence of gene expression increases with increasing polygenicity, while the specificity of convergence increases between functionally similar genes. Convergent networks show brain-region and developmental period-specific enrichments, as well as disorder-specific enrichments for both rare and common variant target genes across schizophrenia, bipolar disorder, autism spectrum disorder, and intellectual disability. These gene targets are drug-able and potentially represent novel points of therapeutic intervention. Convergent signatures are also resolved in the post-mortem brain. Overall, convergence suggests a model to explain how non-additive interactions arise between risk genes and may explain cross-disorder pleiotropy of genetic risk for psychiatric disorders.

Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ('eGenes', whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ('convergence'), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ('sub-additive effects'). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.

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