SYSTEMATIC INVESTIGATION OF ALLELIC REGULATORY ACTIVITY OF GENETIC VARIANTS ASSOCIATED WITH PSYCHIATRIC DISORDERS

Genome-wide association studies (GWAS) have identified numerous genomic regions implicated in the risk of psychiatric disorders. However, discerning the causal variant(s) remains challenging due to linkage disequilibrium, necessitating further experimental validation. To address this challenge, a massively parallel reporter assay (MPRA) has been developed to functionally validate the allelic regulatory effects of thousands of variants in a single experiment by utilizing next-generation sequencing technology. In the first part of this study, we performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia-associated common variants in primary human neural progenitors (HNPs), identifying 439 variants with allelic regulatory effects (MPRA-allelic variants). Existing strategies for prioritizing causal variants, such as GWAS summary statistics, fine-map posterior inclusion probability, and enhancer overlap, proved inadequate in predicting MPRA-allelic variants. Notably, 64% of MPRA-allelic variants did not exhibit expression quantitative trait loci (eQTL) signatures, indicating that MPRA can uncover previously unexplored variants with regulatory potential. Since many psychiatric disorders (e.g., schizophrenia) show comorbidity among other psychiatric disorders, scientists have hypothesized that there is underlying pleiotropy at play. To understand the genetic basis of pleiotropy, the Psychiatric Genomic Consortium (PGC) has conducted a meta- genome-wide association study (meta-GWAS) across eight psychiatric disorders and identified 136 loci, of which 109 were associated with multiple psychiatric disorders, underscoring the need to investigate the mechanisms underlying pleiotropic effects. In the second part of this study, we conducted MPRA on 15,902 variants associated with single or multiple psychiatric disorders to elucidate the regulatory logic of variants with disorder-specific and pleiotropic effects in HNPs. We identified 683 expression-modulating variants (emVars) that exhibit allelic regulatory effects in enhancers. Subsequent bioinformatic analyses revealed that disease-specific and pleiotropic emVars alter binding motifs for transcription factors (TFs) with minimal overlap, and that pleiotropic TFs exhibit higher connectivity in protein-protein interaction (PPI) networks. Lastly, linking our emVars with eQTL resources showed that 62% of our emVars overlapped with eQTLs. This overlap was higher for emVars than the MPRA-allelic variants identified by the same study, highlighting the higher biological relevance of emVars compared to MPRA-allelic variants.