Associations of CB1 cannabinoid receptor (CNR1) gene polymorphisms with risk for alcohol dependence
Bibliographic record
Abstract
OBJECTIVES: Reported associations of the cannabinoid receptor 1 (CNR1) single nucleotide polymorphisms (SNPs) with alcohol dependence (AD) have been inconsistent, prompting a meta-analysis to obtain more precise estimates. METHODS: A Boolean search of 4 databases (PubMed, Scopus, Google Scholar, and Mednar) sought articles that evaluated the association between CNR1 polymorphisms and risk of AD. We selected the articles with sufficient genotype frequency data to enable calculation of odds ratios (ORs) and 95% confidence intervals (CIs). Using the Population Intervention Comparators Outcome elements, AD patients (P) were compared by genotype data between AD-participants (I) and non-AD-participants (C) in order to determine the risk of AD (O) attributed to the CNR1 SNPs. Analyzing 4 SNPs (rs1049353, rs1535255, rs2023239, and rs806379) using standard genetic models, we examined associations where multiple comparisons were Holm-Bonferroni corrected. The pooled ORs were assessed for aggregate statistical power and robustness (sensitivity analysis). Subgroups were Caucasians and African-Americans. RESULTS: From 32 comparisons, 14 were significant indicating increased risk, from which 5 outcomes (P-value for association [Pa] = .003 to <.001) survived the Holm-Bonferroni-correction, which were deemed robust. In the rs1535255 outcomes, the codominant effect (OR = 1.43, 95% CIs = 1.24-1.65, Pa < .001) had greater statistical power than the dominant effect (OR = 1.30, 95% CI = 1.08-1.57, Pa = .006). In contrast, the rs2023239 codominant outcome was underpowered. Significance of both rs806379 Caucasian outcomes (ORs = 1.20-1.43, 95% CIs = 1.07-1.57, Pa = .003) contrasted with the null effects in African-Americans (ORs = 0.98-1.08, 95% CIs = 0.70-1.53). CONCLUSIONS: Three CNR1 SNPs (rs1535255, rs2023239, and rs806379) were implicated in their associations with development of AD: based on aggregate statistical power, rs1535255 presented greater evidence for associations than rs2023239; rs806379 implicated the Caucasian subgroup. Multiple statistical and meta-analytical features (consistency, robustness, and high significance) underpinned the strengths of these outcomes. Our findings could render the CNR1 polymorphisms useful in the clinical genetics of AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".