T21. COMMON AND RARE GENETIC ARCHITECTURE OF COMORBIDITY BETWEEN CARDIOVASCULAR DISEASES AND SEVERE MENTAL DISORDERS

• Published in: European neuropsychopharmacology 2024-10, Vol.87, p.167-167
• Main Authors: Jiang, Bixuan, Li, Xiangyi, Zhou, Wei, Huai, Cong, Shen, Lu, Qin, Shengying
• Format: Article
• Language: English
• ISSN: 0924-977X
• DOI: 10.1016/j.euroneuro.2024.08.331

Cardiovascular diseases (CVDs), a group of disorders of the heart and blood vessels, are the leading cause of death in the general population. Severe mental disorders (SMDs), such as schizophrenia (SCZ) and bipolar disorder (BD), have affected approximately 1% of the UK population. SCZ and BD are two typical types of SMDs and share a common genetic cause of mainly additive genetic effects. Several intriguing relationships have been observed between SMDs and CVDs. For example, the high mortality among patients with SMDs is attributable to heart disease. In a national retrospective longitudinal cohort of patients with schizophrenia, CVDs have the highest mortality rate of 403.2 per 100,000 person-years. Conversely, people suffering from CVDs seem to have an increased risk of SMDs. From a genetic perspective, pleiotropy occurs when one gene affects multiple phenotypes. Meta-analyses of GWASs can elucidate the regions of the genome that associate with a disease, quantitative trait, or biomarker by tagging and testing the association of single nucleotide polymorphisms (SNPs) in certain populations. In this study, taking SCZ and BD as the representative of SMDs and MI as the representative of CVDs, we investigated the potentially shared rare and common genetic architecture, including genes and pathways, that might contribute to the comorbidity relationship between SMDs and CVDs. We performed both meta-analysis GWAS and discovery WES approach to provide additional insights into the role of genes and pathways having pleiotropic effects on SCZ, BD and MI. Using gene-based and gene-set association analysis based on the results of genome-wide association studies (GWASs), we found the common genetic architecture of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES and SMG6) and nine pathways significantly shared between CVDs and SMDs. Through Mendelian randomization analysis, we found twenty-seven genes were the potential casual genes of SMDs and CVDs. Based on the exome sequencing data of CVDs and SMDs patients from the UK Biobank, we found MUC2 was exome-widely significant in the two diseases. Gene-set analyses of exome sequencing data indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in both SMDs and CVDs . We also found six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb. We hope our investig