Gene-Gene interactions and pleiotropy in the brain nicotinic pathway associated with the heaviness and precocity of tobacco smoking among outpatients with multiple substance use disorders
Icick, R.; Besson, M.; Zerdazi, E. H.; Prince, N.; Bloch, V.; Laplanche, J.-L.; Faure, P.; Bellivier, F.; Maskos, U.; Vorspan, F.
Show abstract
IntroductionTobacco smoking is a major health burden worldwide, especially in populations suffering from other substance use disorders (SUDs). Several smoking phenotypes have been associated with single nucleotide polymorphisms (SNPs) of nicotinic acetylcholine receptors (nAChRs). Yet, little is known about the genetics of tobacco smoking in populations with other SUDs, particularly regarding gene-gene interactions and pleiotropy, which are likely involved in the polygenic architecture of SUDs. Thus, we undertook a candidate pathway association study of nAChR-related genes and smoking phenotypes in a sample of SUD patients.\n\nMethods493 patients with genetically-verified Caucasian ancestry were characterized extensively regarding patterns of tobacco smoking, other SUDs, and 83 SNPs from the nicotinic pathway, encompassing all brain nAChR subunits and metabolic/chaperone/trafficking proteins. Single-SNP, gene-based and SNP x SNP interactions analyses were performed to investigate associations with relevant tobacco smoking phenotypes. This included Bayesian analyses to detect pleiotropy, and adjustment on clinical and sociodemographic confounders.\n\nResultsAfter multiple adjustment, we found independent associations between CHRNA3 rs8040868 and a higher number of cigarettes per day (CPD), and between RIC3 rs11826236 and a lower age at smoking initiation. Two SNP x SNP interactions were associated with age at onset (AAO) of daily smoking. There was pleiotropy regarding three SNPs in CHRNA3 (CPD, AAO daily smoking), ACHE (CPD, HSI) and CHRNB4 (CPD, both AAOs).\n\nDiscussionDespite limitations, the present study shows that the genetics of tobacco smoking in SUD patients are both distinct and partially shared across smoking phenotypes, and involve metabolic and chaperone effectors of the nicotinic pathway.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program 95%
- Drinking and smoking polygenic risk is associated with neurodevelopmental outcomes of children and young adults independently of psychopathology and substance use 93%
- Genome-wide DNA methylation analysis of heavy cannabis exposure in a New Zealand longitudinal cohort 93%
Similar papers in this journal
- Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry 94%
- Alcohol consumption and telomere length: observational and Mendelian randomization approaches 92%
- Genome-wide association study identifies common genetic risk factors for alcohol, heroin and methamphetamine dependence 92%
Similar papers in this journal
- Epigenome-wide Association Study of Alcohol Use Disorder in Five Brain Regions 94%
- Genetic overlap between mood instability and alcohol-related phenotypes suggests shared biological underpinnings 93%
- Increased functional coupling of the mu opioid receptor in the anterior insula of depressed individuals 93%
Similar papers in this journal
Similar papers in this journal
- Polygenic Scores Predict the Development of Alcohol and Nicotine Use Problems from Adolescence through Young Adulthood 93%
- Transitions from smoking to exclusive e-cigarette use, dual use, or stopping nicotine use in ALSPAC and their association with modifiable and sociodemographic factors 92%
- Maternal and child genetic liability for smoking and caffeine consumption and child mental health: An intergenerational genetic risk score analysis in the ALSPAC cohort 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.