Keerthy Sunder

Background/Objectives: Post-Traumatic Stress Disorder (PTSD) is a complex psychiatric condition with a strong polygenic and stress-related biological basis. Although Genome-Wide Association Studies (GWAS) have acknowledged abundant risk variants, translating these findings into biologically meaningful candidates remains challenging. This study introduces an integrative computational approach from raw file preparation by python-coded application into downstream in-depth silico analyses designed to systematically refine GWAS signals for PTSD using fine-mapping, linkage disequilibrium (LD), and haplotype analyses. Methods: GWAS source file for PTSD was obtained from the GWAS Catalog (EFO_0001358) and analyzed using a custom Python pipeline integrating data harmonization, genome-wide visualization, LD estimation via 1000 Genomes reference panels, approximate Bayesian fine-mapping, and Haploview-inspired haplotype inference. SNPs were filtered based on statistical significance, LD structure, and posterior inclusion probability. Downstream systems’ biology analyses included protein–protein interaction modeling and pharmacogenomics (PGx) annotations. Results: From the primary GWAS dataset, 100 top-ranked SNPs were selected, leading to the identification of 58 significant loci. LD and haplotype analyses refined these signals to 93 candidate SNPs (66 genes). Following the exclusion of non-protein-coding genes, 45 genes remained, and network-based prioritization generated a final list of 20 biologically connected and pharmacogenetically relevant genes associated with PTSD. Conclusions: This integrative approach provided a robust and reproducible framework for GWAS fine-mapping and variant prioritization, effectively reducing large-scale GWAS outputs to biologically interpretable PTSD risk loci and genes.

Background: GLP‑1 receptor agonists (GLP‑1RAs) are effective for type 2 diabetes and obesity and engage central stress and reward pathways (HPA axis; mesolimbic dopamine). Psychiatric signals have been reported in some settings. Objective :To narratively synthesize evidence on neuropsychiatric effects of GLP‑1RAs—mood, anxiety, suicidality, reward processing, substance use, and migraine—and to consider genetic/epigenetic moderators (e.g., hypodopaminergia/reward deficiency syndrome).Evidence: Mechanistic and imaging studies demonstrate GLP‑1 engagement of HPA and reward circuitry; animal work suggests acute anxiogenic responses with mixed chronic effects. Pharmacovigilance datasets (FAERS/EudraVigilance) show signals for headache, dizziness, sensory changes, and less consistently for depression/anxiety/suicidality; such data are hypothesis‑generating and not causal. Clinical trials and meta‑analyses report heterogeneous outcomes, including small antidepressant effects in some T2DM cohorts. Conclusions: Neuropsychiatric outcomes with GLP‑1RAs appear context‑dependent. Individuals with hypodopaminergic biology may be vulnerable to reward blunting/anxiety, whereas others may experience mood benefits. A pragmatic approach is screen–stratify–monitor, with slow titration and prompt adjustment if psychiatric signals emerge. Prospective, genotype‑informed studies are needed to identify who benefits or is harmed and why.

Autism Spectrum Disorder (ASD) is a complex syndrome with a plethora of clinical manifestations. Although such variability may be attributable to diverse genetic and epigenetic factors, mesolimbic dopaminergic reward pathways are consistently implicated in the illness’ course and neuropathobiology pointing to a need in an updated perspective on such a potential overlap area. To that end, we introduce a structured framework dissecting ASD's pathophysiology into stages while emphasizing the involvement of the classical reward structures. Through data mining of Genome-Wide Association Studies (GWAS) and in silico analyses, we uncovered genetic and epigenetic markers pointing to Reward Deficiency Syn-drome (RDS) component of ASD. We employ innovative approaches, including protein-protein interactions, gene regulatory networks, and systems biology analyses, to identify pathways and ASD gene ontologies. The multi-omics analysis integrates findings across genomics, proteomics, metabolomics, and phenomics, reinforcing the concept of ASD as an endophenotype of RDS. This comprehensive approach offers new insights into the genetic architecture and molecular mechanisms of ASD, paving the way for personalized medicine strategies thus yielding novel therapeutic interventions.

The convergence of neurogenetics, epigenetics, and functional neuroimaging presented in this article marks a critical inflection point in our understanding and management of reward deficiency syndrome (RDS) and its behavioral expressions across pain and addiction medicine. The evidence for a hypodopaminergic state, now supported by decades of molecular, clinical, and imaging data, has culminated in the formulation of a scientifically grounded, personalized, and preventative paradigm— anchored by the concept of early genetic testing to provide risk information linked to “prediction” predominantly due to dopaminergic dysfunction. From a translational standpoint, this model offers more than a framework for understanding neurobiological vulnerability; it provides a practical roadmap for early identification of “pre-addiction,” informed opioid prescribing, relapse prevention, and long-term neurorecovery. The coupling of Genetic Addiction Risk Severity (GARS) with dopaminergic modulation—via safe, non-addictive interventions—could redefine standard treatment algorithms not only for substance use disorders but also for a broader spectrum of compulsive and comorbid behaviors. This study by members of the RDS Consortium explores the concept of “pre-addiction” within addiction biology through a comprehensive in silico analysis of 88,788,381 genome-wide association study-based samples from 1,373 studies, identifying 18 significant genes (e.g., APOE with p=1.0E-126) linked to opioids, pain, aging, and apoptosis pathways. It aims to correlate these genes with GARS, which includes 10 specific genes, and highlights the most connected genes, such as MAOA, COMT, APOE, and SLC4A6, through a STRING model. The analysis expanded to 27 unique genes, emphasizing significant interactions with hsa-miR-16-5p and hsa-miR-24-3p, especially SLC6A4. Through pharmacogenomics mining, 1,173 variant annotations were identified for these genes. Enrichment analysis and meta-analysis further validated these findings, illustrating the pivotal role of dopaminergic pathways in connecting addictive behaviors and depressive symptoms. The results support the conceptualization of RDS as the fundamental preaddiction phenotype, with pain, opioid dependence, aging, and apoptosis as critical endophenotypes.

Background: In the United States, approximately 1 in 5 children experience comorbidities with mental illness, including depression and anxiety, which lead to poor general health outcomes. Adolescents with substance use disorders exhibit high rates of co-occurring mental illness, with over 60% meeting diagnostic criteria for another psychiatric condition in community-based treatment programs. Comorbidities are influenced by both genetic (DNA antecedents) and environmental (epigenetic) factors. Given the significant impact of psychiatric comorbidities on individuals’ lives, this study aims to uncover common mechanisms through a Genome-Wide Association Study (GWAS) meta-meta-analysis. Methods: GWAS datasets were obtained for each comorbid phenotype, followed by a GWAS meta-meta-analysis using a significance threshold of p < 5E−8 to validate the rationale behind combining all GWAS phenotypes. The combined and refined dataset was subjected to bioinformatic analyses, including Protein–Protein Interactions and Systems Biology. Pharmacogenomics (PGx) annotations for all potential genes with at least one PGx were tested, and the genes identified were combined with the Genetic Addiction Risk Severity (GARS) test, which included 10 genes and eleven Single Nucleotide Polymorphisms (SNPs). The STRING-MODEL was employed to discover novel networks and Protein–Drug interactions. Results: Autism Spectrum Disorder (ASD) was identified as the top manifestation derived from the known comorbid interaction of anxiety, depression, and attention deficit hyperactivity disorder (ADHD). The STRING-MODEL and Protein–Drug interaction analysis revealed a novel network associated with these psychiatric comorbidities. The findings suggest that these interactions are linked to the need to induce “dopamine homeostasis” as a therapeutic outcome. Conclusions: This study provides a reliable genetic and epigenetic map that could assist healthcare professionals in the therapeutic care of patients presenting with multiple psychiatric manifestations, including anxiety, depression, and ADHD. The results highlight the importance of targeting dopamine homeostasis in managing ASD linked to these comorbidities. These insights may guide future pharmacogenomic interventions to improve clinical outcomes in affected individuals.

Autism spectrum disorder (ASD) is primarily characterized by core deficits in social skills, communication, and cognition and by repetitive stereotyped behaviors. These manifestations are variable between individuals, and ASD pathogenesis is complex, with over a thousand implicated genes, many epigenetic factors, and multiple environmental influences. The mesolimbic dopamine (DA) mediated brain reward system is held to play a key role, but the rapidly expanding literature reveals intricate, nuanced signaling involving a wide array of mesolimbic loci, neurotransmitters and receptor subtypes, and neuronal variants. How altered DA signaling may constitute a downstream convergence of the manifold causal origins of ASD is not well understood. A clear working framework of ASD pathogenesis may help delineate common stages and potential diagnostic and interventional opportunities. Hence, we summarize the known natural history of ASD in the context of emerging data and perspectives to update ASD reward signaling. Then, against this backdrop, we proffer a provisional framework that organizes ASD pathogenesis into successive levels, including (1) genetic and epigenetic changes, (2) disrupted mesolimbic reward signaling pathways, (3) dysregulated neurotransmitter/DA signaling, and finally, (4) altered neurocognitive and social behavior and possible antagonist/agonist based ASD interventions. This subdivision of ASD into a logical progression of potentially addressable parts may help facilitate the rational formulation of diagnostics and targeted treatments.

The D2 dopamine receptor (DRD2) gene has garnered substantial attention as one of the most extensively studied genes across various neuropsychiatric disorders. Since its initial association with severe alcoholism in 1990, particularly through the identification of the DRD2 Taq A1 allele, numerous international investigations have been conducted to elucidate its role in different conditions. As of February 22, 2024, there are 5485 articles focusing on the DRD2 gene listed in PUBMED. There have been 120 meta-analyses with mixed results. In our opinion, the primary cause of negative reports regarding the association of various DRD2 gene polymorphisms is the inadequate screening of controls, not adequately eliminating many hidden reward deficiency syndrome behaviors. Moreover, pleiotropic effects of DRD2 variants have been identified in neuropsychologic, neurophysiologic, stress response, social stress defeat, maternal deprivation, and gambling disorder, with epigenetic DNA methylation and histone post-translational negative methylation identified as discussed in this article. There are 70 articles listed in PUBMED for DNA methylation and 20 articles listed for histone methylation as of October 19, 2022. For this commentary, we did not denote DNA and/or histone methylation; instead, we provided a brief summary based on behavioral effects. Based on the fact that Blum and Noble characterized the DRD2 Taq A1 allele as a generalized reward gene and not necessarily specific alcoholism, it now behooves the field to find ways to either use effector moieties to edit the neuroepigenetic insults or possibly harness the idea of potentially removing negative mRNA-reduced expression by inducing “dopamine homeostasis.”

There are no FDA-approved treatments for the chronic sequelae of concussion. Repetitive magnetic transcranial stimulation (rTMS) has been explored as a therapy but outcomes have been inconsistent. To address this we developed a personalized rTMS (PrTMS) protocol involving continual rTMS stimulus frequency adjustment and progressive activation of multiple cortical sites, guided by spectral electroencephalogram (EEG)-based analyses and psychological questionnaires. We acquired pilot clinical data for 185 symptomatic brain concussion patients who underwent the PrTMS protocol over an approximate 6 week period. The PrTMS protocol used a proprietary EEG spectral frequency algorithm to define an initial stimulation frequency based on an anteriorly graded projection of the measured occipital alpha center peak, which was then used to interpolate and adjust regional stimulation frequency according to weekly EEG spectral acquisitions. PrTMS improved concussion indices and normalized the cortical alpha band center frequency and peak EEG amplitude. This potentially reflected changed neurotransmitter, cognitive, and perceptual status. PrTMS may be a promising treatment choice for patients with persistent concussion symptoms. This clinical observational study was limited in that there was no control group and a number of variables were not recorded, such as time since injury and levels of depression. While the present observations are indeed preliminary and cursory, they may suggest further prospective research on PrTMS in concussion, and exploration of the spectral EEG as a concussion biomarker, with the ultimate goals of confirmation and determining optimal PrTMS treatment parameters.