A Local Genetic Correlation Analysis Provides Biological Insights Into the Shared Genetic Architecture of Psychiatric and Substance Use Phenotypes.

Background: Global genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation.

Methods: We used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus.

Results: We detected 80 significant (p < 2.08 × 10) bivariate local genetic correlations across 61 loci. The expression effect directions for risk genes within each bivariate locus were largely consistent with the local correlation coefficients, suggesting that genetically regulated gene expression may be used in the functional interpretation of local genetic correlations. Probabilistic fine-mapping identified several genes that may drive pleiotropic mechanisms for genetically correlated phenotypes. For example, we confirmed a local genetic correlation between schizophrenia and smoking behavior at 15q25 and prioritized PSMA4 as the most credible gene candidate underlying both phenotypes.

Conclusions: Our study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes.