Sex-Specific Genetic Architecture and Comorbidities of Alcohol Use Behaviors
Bibliographic record
Abstract
ABSTRACT Background Sex differences in alcohol use behaviors are well-established: males typically engage in heavier and more frequent drinking and exhibit more externalizing behaviors (e.g., other substance use), while females often transition to dependence more rapidly and present more internalizing psychopathology (e.g., depression). The biological mechanisms underpinning these differences are relatively unknown. Methods In this study, we investigated the sex-differentiated genetic architecture of 11 alcohol use phenotypes pertaining to frequency, quantity and problematic use by leveraging sex-stratified genome-wide association studies (Ns 40,335 to 613,148). Specifically, we compared SNP-based heritability ( h 2 SNP ) estimates, individual genetic locus effects, genetic correlations ( r g ) across alcohol phenotypes and with comorbid traits from independent GWAS, and polygenic score ( PGS ) associations with medical outcomes from clinical populations. Results h 2 SNP was broadly similar between sexes, except for higher estimates in males for beer quantity and problematic alcohol use ( PAU ). We identified four sex-differentiated top loci ( p sex-diff < 5 x 10 -8 ), including a female-specific association in IZUMO1 for drinking frequency and quantity , and three male-specific associations in ADH1B , KLB and FTO for beer quantity and/or PAU . Between-sex genetic correlations ranged from 0.68±0.07 to 0.89±0.04, these estimates were lowest for quantity measures and varied by beverage type, indicating partially distinct polygenic architecture. In males, we identified stronger positive genetic correlations with several externalizing traits (e.g., general addiction) compared to females. In females, we identified a specific positive genetic correlation with a single internalizing trait, self-harm. PGS analyses revealed sex-specific medical associations (e.g., bone/musculoskeletal conditions in females; hepatic/respiratory/infectious sequelae in males) that were obscured in sex-combined analyses; however, sex-specific PGS did not outperform combined-sex PGS for predicting alcohol use disorder diagnosis. Conclusions Sex-aware analyses of alcohol use behaviors can improve our understanding of the genetic etiology of alcohol use and related health outcomes, and future studies should consider cultural variation (e.g., drinking attitudes, social norms) in the relationship between behavior and genetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".