| Title: | Machine learning analyses to identify circadian-related genotypes predictive of anxiety |
| Lead Institution: | Colgate University |
| Principal investigator: | Professor Ahmet Ay |
Application 85474
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About
Mood disorders, including depression and anxiety, are becoming more prevalent globally, affecting nearly one-fifth of the adult population. These disorders negatively impact productivity, social relationships, and overall quality of life in individuals. The search for genetic and environmental factors contributing to the epidemic of mental health has uncovered numerous links between circadian rhythm disruptions and mood disorders including major depressive disorder (MDD), schizophrenia, bipolar disorder (BD), and anxiety. Clinical studies have also demonstrated that circadian rhythms and circadian clock genes can modulate mood and psychiatric disorders but few of these studies have explicitly focused on anxiety. Here, we seek to identify circadian clock-related genotypes that are predictive for risk of anxiety symptoms in males and females. The duration of the project is estimated at two years to complete analyses and draft manuscripts for publication. The public health impact of our results includes the identification of circadian clock gene variants that increase risk of anxiety symptoms; these variants may represent valuable therapeutic targets that will drive the development of non-invasive treatments for anxiety-related disorders.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 13960 | Sex- and Substance-Specific Associations of Circadian-Related Genes with Addiction in the UK Biobank Cohort Implicate Neuroplasticity Pathways | Ayub Khan (+4) | 2024 | Brain Sciences |
Enabling scientific discoveries that improve human health