Externalizing as a common genetic influence for a broad spectrum of substance use and behavioral conditions: A developmental perspective from the Avon Longitudinal Study of Parents and Children
Bibliographic record
Abstract
Abstract Background and aims Recent large studies have established the genetic basis of several conceptually linked phenotypes of externalizing. Polygenic risk scores (PRSs) for these constructs are associated with a range of substance use and mental disorder phenotypes but have not been examined with both pharmacological and non‐pharmacological addictive behaviors, or across a developmental window. This study identified biological pathways responsible for observed associations between PRSs and addiction phenotypes. Design, setting, participants We selected genome‐wide association studies of 22 phenotypes, including substance use, general factors of externalizing and addiction, impulsivity and psychiatric conditions. Using summary statistics, we constructed PRSs in the offspring from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n max = 4995). Participants were genetically confirmed to be unrelated and of European‐like genetic similarity. Measurements We examined the associations between PRSs and addiction‐related phenotypes including substance use, gambling, eating behaviors and internet use across different life stages, from adolescence to young adulthood. PRSs were partitioned by biological pathways to examine the common and unique mechanisms underlying the genetics of addiction‐related phenotypes. Findings The PRS of externalizing factor (PRS EXT ) showed the strongest association across phenotypes for substance use (minP = 2.6 × 10 ‐31 , adjusted R 2 = 0.10–4.72%), gambling (minP = 1.0 × 10 ‐9 , adjusted R 2 = 0.18–1.50%), eating behaviors (minP = 8.2 × 10 ‐4 , adjusted R 2 = 0.11–0.65%) and internet use (minP = 1.4 × 10 ‐7 , adjusted R 2 = 0.17–1.04%). Sensitivity analyses excluding a small subset of ALSPAC participants who also contributed to the externalizing summary statistics, yielded consistent association effect sizes (R 2 = 0.98), suggesting minimal bias. The results also revealed several time‐varying associations between several PRSs and addiction phenotypes. Notably, the genetic influence of externalizing factor on alcohol and tobacco use was significantly stronger at younger ages. Finally, we identified multiple biological pathways that contribute to the link between addiction‐related phenotypes and PRS EXT , emphasizing the importance of synaptic functions and neuronal plasticity in the context of gambling and substance use. Conclusions There appears to be genetic evidence implicating externalizing as a common mechanism of substance and behavioral addictive behaviors. These results support the shared genetic liability across substance misuse, problematic gambling and internet use, and demonstrate the potential utility of externalizing traits as a transdiagnostic dimension across diverse forms of psychopathology. Notably, the predictive power of externalizing genetic liability appears developmentally dynamic, supporting the view that externalizing represents a broad, non‐time‐invariant risk factor that may give way to more specific disorder‐related influences over time.
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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".