ABSTRACT: We predicted that eGenes linked to schizophrenia would share substantial downstream transcriptomic changes with a common direction of effect (termed “convergence”). Although convergence has been described in the context of loss-of-function autism spectrum disorder risk genes, these rare mutations almost never co-occur in the same individual. The convergent impact of common variants which are frequently inherited together, and the impacts of which are apparent only in aggregate remain unknown. We targeted twenty-one schizophrenia eGenes in iGLUTs using pooled and arrayed CRISPR-based approaches, significantly perturbing seventeen (CALN1, CLCN3, DOC2A, FES, FURIN, GATAD2A, NAGA, PCCB, PLCL1, THOC7, TMEM219, SF3B1, SNAP91, SNCA, UBE2Q2L, ZNF823, ZNF804A), and resolving convergent impacts robust to experimental and donor effects. To test if convergence influenced the outcome when eGenes were inherited in combination (i.e. if eGene effects sum linearly according to the additive model), we compared manipulation of eGenes one at a time and in groups defined by annotated functions at the synapse (“synaptic”: SNAP91, CLCN3, PLCL1, DOC2A, SNCA), or regulating transcription (“regulatory”: ZNF823, INO80E, SF3B1, THOC7, GATAD2A), or with un-related non-synaptic, non-regulatory biology (“multi-function”: CALN1, CUL9, TMEM219, PCCB, FURIN), and random combinations thereof. Altogether, with broad relevance across complex polygenic disease our work begins to experimentally determine answers to the long-standing question of how risk variants interact in human neurons.
