Exploring the Genetic and Transcriptomic Landscape of Obsessive-Compulsive Disorder: A Machine Learning-Driven Investigation of lncRNA BDNF-AS

Obsessive-compulsive disorder (OCD) is a multifaceted mental health condition characterized by intrusive thoughts and repetitive behaviors, with its origins remaining poorly understood. While genetic factors are acknowledged to contribute to its development, the specific molecular mechanisms underlying OCD are still largely unexplored. This study employed a machine learning approach to investigate the role of long non-coding RNA (lncRNA) BDNF-AS in relation to the BDNF Val66Met polymorphism and its association with OCD. Our comprehensive analysis of various genomic and transcriptomic datasets revealed a significant upregulation of lncRNA BDNF-AS in OCD patients carrying the BDNF Val66Met variant compared to those who do not possess this genetic variant. Importantly, we observed a dose-dependent relationship between BDNF genotype and BDNF-AS expression, with individuals carrying the Val/Met and Met/Met genotypes exhibiting notably higher levels of expression. These findings suggest a potential connection between the dysregulation of BDNF-AS and the BDNF Val66Met polymorphism in the pathogenesis of OCD. Given that BDNF-AS is known to regulate BDNF expression and signaling—both of which are crucial for neuronal function and synaptic plasticity—disruptions in these pathways may play a significant role in the development of OCD. Identifying this lncRNA-mediated mechanism not only enhances our understanding of OCD's molecular basis but also holds promise for the development of novel biomarkers and targeted therapeutic strategies, paving the way for personalized treatment approaches.

Exploring the Genetic and Transcriptomic Landscape of Obsessive-Compulsive Disorder: A Machine Learning-Driven Investigation of lncRNA BDNF-AS Keywords:

Obsessive-compulsive disorder , BDNF Val66Met , lncRNA BDNF-AS , machine learning , biomarker , therapeutic target