Genetic influences on nicotinic α5 receptor (CHRNA5) CpG methylation and mRNA expression in brain and adipose tissue
Bibliographic record
Abstract
INTRODUCTION: The nicotinic α5 receptor subunit, encoded by CHRNA5, harbors multiple functional single nucleotide polymorphisms (SNPs) that affect mRNA expression and alter the encoded protein. These polymorphisms are most notably associated with drug-taking behaviors and cognition. We previously identified common SNPs in a distant regulatory element (DRE) that increase CHRNA5 mRNA expression in the human prefrontal cortex (PFC) and confer risk for nicotine dependence. Genome-wide epigenetic studies in PFC and adipose tissue find strong effects of the DRE SNPs on CpG methylation. However, it is unclear whether DRE SNPs influence CpG methylation en route to modulating CHRNA5 mRNA expression. It is also unclear whether these polymorphisms affect expression in other brain regions, especially those mediating drug-taking behaviors. RESULTS: By measuring total and allelic CHRNA5 mRNA expression in human habenula and putamen autopsy tissues, we found that CHRNA5 DRE variants considerably increase mRNA expression by up to 3.5-fold in both brain regions. Our epigenetic analysis finds no association between CpG methylation and CHRNA5 mRNA expression in the PFC or adipose tissues. CONCLUSIONS: These finding suggests the mechanisms responsible for the genetic modulation of CpG methylation and mRNA expression are independent despite the DRE SNPs being highly associated with both measures. Our findings support a strong association between the DRE SNPs and mRNA expression or CpG methylation in the brain and periphery, but the independence of the two measures leads us to conclude that environmental factors affecting CpG methylation do not appear to directly modulate gene expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".