Genomic and transcriptomic insights into antipsychotic-induced changes in total cholesterol and body mass index in a multi-ancestry cohort of the US veterans

Objective: Antipsychotic medications are a cornerstone in the treatment of many psychiatric disorders, but they are associated with adverse metabolic effects including weight gain and hypercholesterolemia. To investigate the underlying genetic architecture of these effects, we conducted the largest-by-far and most ethnically diverse genome-wide association study (GWAS) of longitudinal changes in total cholesterol (TC) and body mass index (BMI) using an antipsychotic-treated cohort from the Million Veteran Program (MVP). Methods: The study included 59,372 participants for TC and 39,112 for BMI across European (EUR), African American (AFR), and Hispanic (HIS) ancestries. GWAS, trans-ancestry meta-analysis, functional annotation, and summary-data-based Mendelian randomization (SMR) analyses were performed to identify associated loci, enriched biological pathways, and gene expression signals. Results: We identified genome-wide significant and suggestive loci for both traits, with stronger associations for TC and clinically significant weight gain (BMI > 1.5 kg/m2) than for overall BMI change. Significant loci included genes involved in cholesterol metabolism and lipid homeostasis for TC and genes previously implicated in BMI-related traits for BMI > 1.5 kg/m2. Trans-ancestry meta-analysis highlighted suggestive loci shared across ancestries, and functional annotation demonstrated significant enrichment of protein homeostasis and synaptic function gene sets for BMI > 1.5 kg/m2. SMR analyses further identified suggestive gene expression associations in whole blood and liver. Conclusions: These findings implicate lipid metabolism, body weight regulation, and central nervous system mechanisms in antipsychotic-associated metabolic changes. This work advances understanding of genetic susceptibility to metabolic adverse effects of antipsychotic treatment and may inform future precision medicine approaches to risk prediction and treatment selection.