A GWAS Meta-meta-analysis and In-depth Silico Pharmacogenomic Investigations in Identification of APOE and Other Genes Associated with Pain, Anti-inflammatory, and Immunomodulating Agents in Opioid Use Disorder (OUD) Derived from 14.91 M Subjects

Abstract

This study aimed to integrate genome-wide association studies (GWAS) with pharmacogenomics data to develop personalized pain and inflammatory therapeutics. Despite recent developments in the clinical utilities of pharmacogenomics, it needs more investigations for uncovering the complicated mechanisms of drugs from a genetic standpoint. The research addresses the increasing misuse of opioids during recovery, emphasizing personalized interventions for opioid use disorder (OUD). Key pain-related pathways were analyzed to uncover their interactions. Five GWAS traits, including pain, inflammatory biomarkers, immune system abnormalities, and opioid-related traits, were examined. Candidate genes extracted from GWAS datasets were refined through in silico analyses, including protein-protein interactions (PPIs), TF-miRNA coregulatory interactions, enrichment analysis (EA), and clustering enrichment analysis (CEA). A network of 50 highly connected genes was identified, with APOE emerging as a top candidate due to its role in cholesterol metabolism and opioid-induced lipid effects. Pharmacogenomics analysis highlighted significant gene annotations, including OPRM1, DRD2, APOE, GRIN2B, and GPR98, linking them to opioid dependence, neurological disorders, and lipid traits. Protein interaction analyses further validated these connections, with implications for epigenetic repair. Our findings reveal a strong association between APOE, opioid use, and Alzheimer's disease, suggesting potential for novel recovery strategies. Combining HDL-boosting drugs with pro-dopaminergic regulators like KB220 may help prevent relapse. This study underscores the importance of integrating genetic and pharmacogenomic data to advance personalized therapies.

Keywords: APOE; GWAS; Lipids; Meta-meta-analysis; Opioids; Pain.

Fig. 1

Flowchart of the analysis strategy applied in the current study. Abbreviations are as follows: GWAS: genome-wide association studies; CMA: comprehensive meta-analysis; and PPIs: protein–protein interactions

Fig. 2

Forest plot of GWAS meta-meta-analysis based on 5 separate meta-groups including Meta1 (pain), Meta2 (inflammatory biomarker measurement), Meta3 (abnormality of the immune system), Meta4 (opioid dependence), and Meta5 (opioid use dependence). Effect size [partial correlation coefficients (PCC) using Fisher’s z transformation] was set on Random status with 2-tailed and positive effect direction. Obviously, all included meta-data are in 0 range between the Favors A and B range. The relative weight of Meta1, Meta2, Meta3, and Meta5 is so close together and just Meta4 represented a different and low relative weight which comes from its low sample size in comparison with the other meta-data

Fig. 3

Funnel plot of GWAS meta-meta-analysis visualized by CMA3 showing the possibility of publication bias based on the standard error by Fisher’s Z. Clearly, there is no publication bias confirming the validity of this meta-meta-analysis

Fig. 4

STRING-MODEL of 50 top scored genes stand for pain, inflammation, immunity, opioid dependence, and OUD to find a reliable protein–protein interactions (PPIs) network

Fig. 5

Fruchterman–Reingold model of TF-miRNAs coregulatory interactions among the candidate genes. The most interacted proteins are illustrated larger than others; also, the purple and blue proteins refer to transcription factors (TFs) and miRNAs, respectively

Fig. 6

Bar graph of enriched terms across input gene list which are differentially colored by p-values

Fig. 7

Network of enriched terms is colored by cluster ID, where nodes sharing the same cluster ID are typically close to each other. The main colors refer to these: Red: regulation of lipid localization (squared); blue: behavioral response to ethanol; green: positive regulation of membrane protein ectodomain; violet: regulation of neural synaptic plasticity; orange: multicellular organismal-level homeostasis; brown: inflammatory response; pink: amino sugar metabolic process; yellow: visual phototransduction