Predicting alcohol consumption in adolescence from alcohol-specific and general externalizing genetic risk factors, key environmental exposures and their interaction.
- Authors
- Kendler, K S; Gardner, C; Dick, D M
- Year
- 2011
- Journal
- Psychological medicine
- PMID
- 20942993
- DOI
- 10.1017/S003329171000190X
- PMCID
- PMC3103618
BACKGROUND: Alcohol consumption is influenced by specific genetic risk factors for alcohol use disorders (AUDs), non-specific genetic risk factors for externalizing behaviors and various environmental experiences. We have limited knowledge of how these risk factors inter-relate through development. METHOD: Retrospective assessments in 1796 adult male twins using a life history calendar of key environmental exposures and alcohol consumption from early adolescence to mid-adulthood. Analysis by linear mixed models. RESULTS: The importance of non-specific genetic risk factors on maximal alcohol consumption rose rapidly in early to mid-adolescence, peaked at ages 15-17 years and then declined slowly. Alcohol-specific genetic risk factors increased slowly in influence through mid-adulthood. We detected robust evidence for environmental moderation of genetic effects on alcohol consumption that was more pronounced in early and mid-adolescence than in later periods. Alcohol availability, peer deviance and low prosocial behaviors showing the strongest moderation effects. More interactions with environmental risk factors were seen for the non-specific externalizing disorder risk than for specific genetic risk for AUDs. CONCLUSIONS: The impact of specific and non-specific genetic influences on alcohol consumption have different development trajectories. Genetic effects on alcohol use are more pronounced when social constraints are minimized (e.g. low prosocial behaviors or parental monitoring) or when the environment permits easy access to alcohol and/or encourages its use (e.g. high alcohol availability or peer deviance). Gene-environment interactions influencing alcohol intake may be more robust at younger ages, indicating greater plasticity of genetic influences early in the development of drinking patterns.
The strength of association, operationalized as a regression coefficient, between the genetic risk for alcohol use disorders (- -β - -; GR-AUD) and the genetic risk for externalizing disorders (ββ²β, GR-ExtD) and maximal alcohol consumption as a function of age. For GR-AUD, all of these regression coefficients are significant at p<0.0001. For GR-ExtD, the regression coefficients for ages 12β14, 15β17, 18β21 and 22β25 are all significant at p<0.0001 and are significant at p=0.006 for ages 26β29 and p=0.001 for ages 30β33.
LLM interpretation
This line graph shows the regression coefficients predicting maximal alcohol intake across six age groups (12β33 years) for two genetic risk factors: GR-AUD (dashed line with squares) and GR-ExtD (solid line with triangles). Both risk factors show an initial increase in association strength from ages 12β14 to 15β17, after which GR-ExtD generally declines while GR-AUD remains relatively stable or increases. According to the legend, all GR-AUD coefficients are significant at p<0.0001, and GR-ExtD coefficients are significant across all age groups (p<0.006).
The prediction of the maximal yearly alcohol consumption from ages 12β14, as measured by standardized monthly intake, by the genetic risk for externalizing disorders (Ext Dis), alcohol availability (Alc Avl) and their interaction. The results, from the best-fit regression model with parameter estimates as outlined in Table 1, are depicted for three hypothetical individuals with a moderately high level of alcohol availability [βββ, values 1 standard deviation (S.D.) above the mean], an average level of alcohol availability (β ββ β β, mean value) and a moderately low level of alcohol availability (- -β²- -, values 1 S.D. below the mean). Maximal yearly alcohol consumption is expressed by the average monthly alcohol consumption in that year, standardized so that, at the mean level of genetic risk and the mean level of alcohol availability, the score is approximately zero. The y-axis then depicts this mean score in standard deviation units.
LLM interpretation
This is a line graph showing the relationship between genetic risk for externalizing disorders (x-axis) and predicted maximal yearly alcohol consumption, measured as $\ln(\text{drinks/month})$ (y-axis). Three lines represent different levels of alcohol availability: moderately high (solid line with circles), average (dashed line with squares), and moderately low (dotted line with triangles). The graph shows that alcohol consumption increases with genetic risk across all groups, but the slope is steepest for individuals with high alcohol availability, indicating an interaction effect.
The prediction of the maximal yearly alcohol consumption from ages 12β14, as measured by standardized monthly intake, by the genetic risk for alcohol use disorders (AUD), peer group deviance (PGD) and their interaction. The results, from the best-fit regression model with parameter estimates as outlined in Table 1, are depicted for three hypothetical individuals with moderately high peer group deviance [βββ, values 1 standard deviation (S.D.) above the mean], an average level of peer group deviance (β ββ β β, mean value) and a moderately low level of peer group deviance (- -β²- -, values 1 S.D. below the mean).
LLM interpretation
This is a line graph showing the predicted maximal yearly alcohol consumption ($\ln(\text{drinks/month})$) as a function of genetic risk for alcohol use disorders (AUD). Three lines represent different levels of peer group deviance (PGD): moderately high (solid line with circles), average (dashed line with squares), and moderately low (dotted line with triangles). The graph shows that alcohol consumption increases with genetic risk for those with average or high PGD, while it remains flat for those with low PGD.
The prediction of the maximal yearly alcohol consumption from ages 12β14, as measured by standardized monthly intake, by the genetic risk for externalizing disorders (Ext Dis), level of prosocial activities (LPSA), reverse coded, and their interaction. The results, from the best-fit regression model with parameter estimates as outlined in Table 1, are depicted for three hypothetical individuals with a moderately low levels of prosocial activities [βββ, values 1 standard deviation (S.D.) above the mean], an average level of prosocial activities (β ββ β β, mean value) and a moderately high level of prosocial activities (- -β²- -, values 1 S.D. below the mean).
| Name | Type |
|---|---|
| 141 subjects local | cohort |
| adolescent substance use | phenotype |
| age | phenotype |
| ages 12β14 local | cohort |
| ages 15β17 local | cohort |
| alcohol | phenotype |
| alcohol abuse | phenotype |
| alcohol availability | phenotype |
| alcohol dependence | phenotype |
| alcohol-related phenotypes | phenotype |
| Alcohol-specific genetic risk factors local | variant |
| Alcohol Use | phenotype |
| Alcohol Use Disorder | phenotype |
| alcohol use disorders | phenotype |
| antisocial personality disorder | phenotype |
| AUD | phenotype |
| average monthly alcohol consumption local | phenotype |
| beer | drug |
| Broad Genetic Susceptibility to Externalizing Disorders/Traits local | gene |
| Church activities (Sunday school or church youth group) local | phenotype |
| church attendance | phenotype |
| Community activities (YMCA and scouting) local | phenotype |
| delinquency | phenotype |
| deviant behavior | phenotype |
| DNA polymorphisms local | variant |
| drinkerβnon-drinker dichotomy local | phenotype |
| drinkers | phenotype |
| drug dependence | phenotype |
| DSM-IV adult antisocial symptoms local | phenotype |
| DSM-IV conduct disorder local | phenotype |
| early onset alcohol dependence | phenotype |
| educational attainment | phenotype |
| environmental factors | drug |
| externalizing behavior | phenotype |
| externalizing disorders | phenotype |
| Externalizing Disorders/Traits local | phenotype |
| genetic risk for AUDs local | phenotype |
| genetic risk for externalizing disorders local | phenotype |
| GR-AUD local | gene |
| GR-AUD local | phenotype |
| GR-AUD local | variant |
| GR-ExtD local | gene |
| GR-ExtD local | phenotype |
| GR-ExtD local | variant |
| Heavy alcohol exposure local | drug |
| High alcohol availability local | phenotype |
| human alcoholics | phenotype |
| Kendler 1994 cohort local | cohort |
| Late onset alcohol dependence local | phenotype |
| liquor | drug |
| low church attendance local | phenotype |
| Low parental bonding local | phenotype |
| low prosocial behavior local | phenotype |
| Low prosocial behavior local | phenotype |
| Low prosocial behaviors local | phenotype |
| maximal alcohol consumption local | phenotype |
| Maximal alcohol consumption local | phenotype |
| maximum reported yearly alcohol use local | phenotype |
| MM3 interview local | cohort |
| Monitoring the Future study | cohort |
| Non-specific genetic risk factors local | variant |
| Organized sports activities local | phenotype |
| Original twin cohort local | cohort |
| Parental bonding local | phenotype |
| parental monitoring | phenotype |
| parentβchild bonding local | phenotype |
| Parentβchild bonding local | phenotype |
| parents | cohort |
| peer deviance | phenotype |
| peer group deviance local | phenotype |
| Peer group deviance local | phenotype |
| prosocial behavior | phenotype |
| psychiatric disorders | phenotype |
| psychopathology | phenotype |
| quantity of alcohol consumed | phenotype |
| School activities (clubs and bands) local | phenotype |
| Specific Alcohol-Related Genetic Risk Factors local | gene |
| subjects | cohort |
| substance use | phenotype |
| Third wave participation local | phenotype |
| Twin cohort | cohort |
| Virginia Adult Twin Study of Psychiatric and Substance Use Disorders local | cohort |
| wine | drug |
| zygosity | phenotype |
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