| Title: | Sex- and Substance-Specific Associations of Circadian-Related Genes with Addiction in the UK Biobank Cohort Implicate Neuroplasticity Pathways |
| Journal: | Brain Sciences |
| Published: | 20 Dec 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39766481/ |
| DOI: | https://doi.org/10.3390/brainsci14121282 |
| URL: | https://www.mdpi.com/2076-3425/14/12/1282/pdf?version=1734945701 |
Publication 13960
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Abstract
BACKGROUND/OBJECTIVES: The circadian clockwork is implicated in the etiology of addiction, with circadian rhythm disruptions bidirectionally linked to substance abuse, but the molecular mechanisms that underlie this connection are not well known.</p>
METHODS: Here, we use machine learning to reveal sex- and substance-specific associations with addiction in variants from 51 circadian-related genes (156,702 SNPs) in 98,800 participants from a UK Biobank cohort. We further analyze SNP associations in a subset of the cohort for substance-specific addictions (alcohol, illicit drugs (narcotics), and prescription drugs (opioids)).</p>
RESULTS: We find robust (OR > 10) and novel sex-specific and substance-specific associations with variants in synaptic transcription factors (ZBTB20, CHRNB3) and hormone receptors (RORA), particularly in individuals addicted to narcotics and opioids. Circadian-related gene variants associated with male and female addiction were non-overlapping; variants in males primarily involve dopaminergic pathways, while variants in females factor in metabolic and inflammation pathways, with a novel gene association of female addiction with DELEC1, a gene of unknown function.</p>
CONCLUSIONS: Our findings underscore the complexity of genetic pathways associated with addiction, involving core clock genes and circadian-regulated pathways, and reveal novel circadian-related gene associations that will aid the development of targeted, sex-specific therapeutic interventions for substance abuse.</p>
5 Authors
- Ayub Khan
- Mete Minbay
- Ziad Attia
- Ahmet Ali Ay
- Krista K. Ingram
1 Application
| Application ID | Title |
|---|---|
| 85474 | Machine learning analyses to identify circadian-related genotypes predictive of anxiety |
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