Eric Otto Johnson

Introduction: Smoking cessation substantially reduces tobacco-related morbidity and mortality, with earlier cessation conferring greater health benefits. Polygenic scores (PGSs) for smoking persistence may provide insight into cessation timing across diverse populations. This study examined the association between a multi-ancestry PGS for smoking persistence and smoking cessation timing in a genetically diverse population. Methods: This study included 139,129 current and former smokers with genetic data from the All of Us Research Program. A multi-ancestry PGS for smoking persistence was constructed using genome-wide association study summary statistics from the GWAS & Sequencing Consortium of Alcohol and Nicotine Use Phase 2 (GSCAN2). Smoking status and cessation timing were examined across the PGS distribution, with a focus on differences between the highest and lowest deciles. Median cessation ages and cumulative cessation probabilities were estimated, while Cox proportional hazards models compared cessation hazards across PGS groups overall and by genetic ancestry. Results: Among 139,129 participants, the proportion of current smokers increased across the PGS distribution, from 35.5% in the lowest decile to 51.9% in the highest decile. Unadjusted median cessation ages were 38 and 50 years in the lowest and highest deciles, respectively (N = 125,047). After adjusting for sex, educational attainment, household income, cigarettes smoked per day, and genetic population structure, estimated median cessation ages were 40 years (95% CI: 40-40) and 48 years (95% CI: 47-50), respectively, representing an 8-year difference. Participants in the highest decile also had a lower cessation hazard than those in the lowest decile (HR = 0.67, 95% CI: 0.65-0.70), with associations observed across genetic ancestry groups, although estimates varied in precision. Conclusion: A multi-ancestry PGS for smoking persistence was associated with substantial differences in cessation timing across genetically diverse populations. These findings suggest that genetic liability to smoking persistence may help characterize risk of delayed cessation and inform earlier, more targeted smoking cessation support.

Despite extensive research on DNA methylation signatures associated with alcohol use disorder (AUD), findings are often inconsistent. Here we conducted a large-scale meta-analysis of epigenome-wide association studies (EWASs) to identify reliable, reproducible epigenetic markers of AUD. Seven cohorts, comprising 3,775 individuals (1,325 with AUD), contributed to this meta-analysis. Downstream analyses included differentially methylated regions, overrepresentation analyses and the construction of a methylation risk score. We identified 118 significant CpG sites associated with AUD, with the strongest association found at cg24889777 ( P = 5.12 × 10 ⁻¹⁷ ). CpG sites were enriched for pathways related to GTPase signaling, transmembrane transporter activity and EWAS signals of alcohol consumption. The methylation risk score explained 2.06% of the variance in alcohol consumption and predicted heavy drinking in an independent cohort ( N = 2,534, area under the curve = 0.657). This analysis offers key insights into the epigenetic mechanisms of AUD and lays the groundwork for future research on methylation risk scores for the diagnosis, prognosis and treatment of AUD.

Only recently have human postmortem brain studies of differential gene expression (DGE) associated with opioid overdose death (OOD) been published; sample sizes from these studies have been modest (N = 40–153). To increase statistical power to identify OOD-associated genes, we leveraged human prefrontal cortex RNA-seq data from four independent OOD studies and conducted a transcriptome-wide DGE meta-analysis (N = 272). Using a unified gene expression data processing and analysis framework across studies, we meta-analyzed 20, 098 genes and found 335 significant differentially expressed genes (DEGs) by OOD status (false discovery rate < 0.05). Of these, 66 DEGs were among the list of 303 genes reported as OOD-associated in prior prefrontal cortex molecular studies (e.g., genes/gene families OPRK1, NPAS4, DUSP, EGR). The remaining 269 DEGs were not previously reported (e.g., NR4A2, SYT1, HCRTR2, BDNF). There was little evidence of genetic drivers for the observed differences in gene expression between opioid addiction cases and controls. Enrichment analyses for the DEGs across molecular pathway and biological process databases highlight an interconnected set of genes and pathways linked to orexin and tyrosine kinase receptors through MEK/ERK/MAPK signaling to affect neuronal plasticity.

As cannabis use continues to rise among people with HIV (PWH), understanding its impact on immune function in this population is becoming increasingly important. To provide new insights on how cannabis modulates immune function, we analyzed single-nucleus multi-omic profiles of peripheral blood mononuclear cells (PBMCs) from PWH to characterize the changes in gene expression and chromatin accessibility associated with chronic cannabis exposure. We identified numerous differentially expressed genes (DEGs) between cannabis users and non-users in each cell type, approximately half of which were unique to individual cell types. Changes in pro- and anti-inflammatory gene expression associated with cannabis use are dependent on cell lineage and type. We identified hundreds of differential chromatin accessibility regions in each cell type, including cis -regulatory elements correlated with cell-type-dependent DEGs (e.g. NFKBIA in CD4+ T cells and CCL3L1 in classical monocytes). Multiple cannabis-associated transcription factors (e.g., NFKB1, FOS , and TCF7 ) emerge as regulators of the differentially expressed inflammatory genes. Furthermore, cannabis altered the communication between classical monocytes and lymphocytes. These findings indicate that cannabis-induced immunomodulatory effects are profound, dynamic and complex among cell types and that transcriptional changes are regulated at least in part by epigenetic mechanisms.

Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome‐wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure (‘generic’) versus minimally opioid‐exposed control (‘exposed’). First, at the phenotypic level, we conducted phenome‐wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD‐related comorbidities were more pronounced when using the generic group, 83% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 ( p _generic = 8.83E‐03 vs. p _exposed = 1.83E‐02) and genetic correlations with prior OUD GWAS ( r _g‐generic = 0.83 ± 0.26 vs. r _g‐exposed = 0.78 ± 0.27). Although GWASs were limited by sample size ( N _generic = 6269, N _exposed = 6365), compared to an independent OUD GWAS ( N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.

Disease heterogeneity presents a major challenge for genetic and epigenetic dissection of complex traits. Neuropsychiatric traits, such as opioid use disorder (OUD), arise from diverse genetic and environmental factors that uniquely combine in individuals, complicating efforts to identify causal genes and enhancers. We identified epigenetic variation linked to opioid overdose by profiling H3K27ac in the nucleus accumbens (NAc) of 91 cases and controls. While standard approaches identified only 156 loci with differential acetylation, machine learning models built on combinations of putative regulatory regions distinguished cases from controls at high accuracy (>0.95) and were significantly enriched for OUD genetic risk. To leverage disease heterogeneity, we defined individual-specific variation in H3K27ac, termed Variant Enhancer Loci (VEL). VELs converged on shared target genes at a five-fold higher rate than prior prefrontal cortex studies. RNA splicing was the top-enriched ontology, including the splicing regulator CELF5. CELF5 knockdown in iPSC-derived medium spiny neurons was linked to collagen proteins and extracellular matrix organization. Collectively, this study provides new insights into opioid addiction and a framework for personalized epigenetic analysis of heterogeneous diseases.

Genes influencing opioid use disorder (OUD) biology have been identified via genome-wide association studies (GWAS), gene expression, and network analyses. These discoveries provide opportunities to identifying existing compounds targeting these genes for drug repurposing studies. However, systematically integrating discovery results and identifying relevant available pharmacotherapies for OUD repurposing studies is challenging. To address this, we’ve constructed a framework that uses existing results and drug databases to identify candidate pharmacotherapies. For this study, two independent OUD related meta-analyses were used including a GWAS and a differential gene expression (DGE) study of post-mortem human brain. Protein-Protein Interaction (PPI) sub-networks enriched for GWAS risk loci were identified via network analyses. Drug databases Pharos, Open Targets, Therapeutic Target Database (TTD), and DrugBank were queried for clinical status and target selectivity. Cross-omic and drug query results were then integrated to identify candidate compounds. GWAS and DGE analyses revealed 3 and 335 target genes (FDR q < 0.05), respectively, while network analysis detected 70 genes in 22 enriched PPI networks. Four selection strategies were implemented, which yielded between 72 and 676 genes with statistically significant support and 110 to 683 drugs targeting these genes, respectively. After filtering out less specific compounds or those targeting well-established psychiatric-related receptors (OPRM1 and DRD2), between 2 and 329 approved drugs remained across the four strategies. By leveraging multiple lines of biological evidence and resources, we identified many FDA approved drugs that target genes associated with OUD. This approach a) allows high-throughput querying of OUD-related genes, b) detects OUD-related genes and compounds not identified using a single domain or resource, and c) produces a succinct summary of FDA approved compounds eligible for efficient expert review. Identifying larger pools of candidate pharmacotherapies and summarizing the supporting evidence bridges the gap between discovery and drug repurposing studies.

Despite extensive research on DNA methylation (DNAm) signatures associated with alcohol use disorder (AUD), findings are often inconsistent and not replicated. We conducted a large-scale meta-analysis of epigenome-wide association studies (EWAS) to identify reliable, reproducible epigenetic markers of AUD. Seven cohorts, comprising 3,775 individuals (1,325 with AUD), contributed to this meta-analysis within the framework of the Psychiatric Genomics Consortium Substance Use Disorders Epigenetics Working Group. Downstream analyses included the identification of differentially methylated regions, overrepresentation analyses, and the construction of a methylation risk score (MRS). We identified 118 significant CpG sites associated with AUD, with the strongest association found at cg24889777 ( p =5.12×10 ^-17 ) in the long non-coding RNA LOC100505942. CpG sites were enriched for pathways related to GTPase signaling and transmembrane transporter activity, as well as EWAS signals of alcohol consumption. The MRS explained 10.44% of variance in heavy drinking in an independent cohort (N=2,534, AUC=0.657). This large-scale meta-analysis offers key insights into the epigenetic mechanisms of AUD and lays the groundwork for future research on methylation risk scores for the diagnosis, prognosis, and treatment in AUD.