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Updated: May 10, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Genome Wide Association Study (GWAS) Identifies Novel Genetic Loci for Second-Generation Antipsychotics (SGA)-Induced
Nihal El Rouby1,2,3, Aniwaa Owusu-Obeng4, Michael H Preuss4
1James L Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA.
None:
Second-generation antipsychotics (SGA) are widely used for treating psychiatric disorders; however, their use is associated with an increased risk of metabolic syndrome (MetS). To identify common genetic associations of SGA-induced metabolic syndrome (SGA-MetS), we conducted a genome-wide association study (GWAS) in a diverse patient population within the BioVU and BioMe electronic health records (EHRs)-linked biobanks. Additionally, we performed Mendelian Randomization (MR) analysis to investigate the association between the individual metabolic parameters comprising MetS (body mass index [BMI], fasting glucose, blood pressure, HDL, and triglycerides) and SGA-MetS. The meta-analysis of European ancestry GWAS from BioVU and BioMe (N = 9248) identified a genome-wide signal (rs61900075, β = -0.27, SE = 0.05, p = 1.6 × 10-8) on chromosome 11. Multiple associated variants met the suggestive level of association (p ≤ 10-5) in the PELO-ITGA1 locus on chromosome 5 and were associated among the Hispanic Ancestry within BioMe. The meta-analysis of the African Ancestry patients of BioVU and BioMe (N = 2018) identified multiple genome-wide signals that were functionally mapped to NPPC-DIS3L2 in chromosome 2. Finally, the inverse-variance weighted average MR (BMI: OR = 1.2, 95% CI: 1.1-1.4, p = 0.002) showed that genetically predicted, higher BMI was associated with an increased risk of SGA-MetS. Similar results were seen in the sensitivity analyses using the weighted median and Egger MR. This study identified novel variants for SGA-MetS and suggested a role of BMI in increasing the risk of SGA-MetS. The findings highlight the value of EHR biobanks for identifying the genetics underlying SGA-MetS. The associations in chromosome 2 and 5 will need further replication.
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