Decline in genetic influence on the co-occurrence of alcohol, marijuana, and nicotine dependence symptoms from age 14 to 29.
- Authors
- Vrieze, Scott I; Hicks, Brian M; Iacono, William G; McGue, Matt
- Year
- 2012
- Journal
- The American journal of psychiatry
- PMID
- 22983309
- DOI
- 10.1176/appi.ajp.2012.11081268
- PMCID
- PMC3513559
OBJECTIVE: Cross-sectional studies have demonstrated high rates of comorbidity among substance use disorders. However, few studies have examined the developmental course of incident comorbidity and how it changes from adolescence to adulthood. The authors examine patterns of comorbidity among substance use disorders to gain insight into the effect of shared versus specific etiological influences on measures of substance abuse and dependence. METHOD: The authors evaluated the pattern of correlations among nicotine, alcohol, and marijuana abuse and dependence symptom counts as well as their underlying genetic and environmental influences in a community-representative twin sample (N=3,762). Symptoms were assessed at ages 11, 14, 17, 20, 24, and 29 years. A single common factor was used to model the correlations among symptom counts at each age. The authors examined age-related changes in the influence of this general factor by testing for differences in the mean factor loading across time. RESULTS: Mean levels of abuse or dependence symptoms increased throughout adolescence, peaked around age 20, and declined from age 24 to age 29. The influence of the general factor was highest at ages 14 and 17, but decreased from age 17 to age 24. Genetic influences of the general factor declined considerably with age alongside an increase in nonshared environmental influences. CONCLUSIONS: Adolescent substance abuse or dependence is largely a function of shared etiology. As young people age, their symptoms are increasingly influenced by substance-specific etiological factors. Heritability analyses revealed that the generalized risk is primarily influenced by genetic factors in adolescence, but nonshared environmental influences increase in importance as substance dependence becomes more specialized in adulthood.
Mean Change in Symptom Count with Age. Females are displayed on the left and males on the right. Each substance is plotted in a different color. Means for Nicotine Dependence, Alcohol Dependence, and Marijuana Dependence are in red, black, and green, respectively. Error bars represent 95% confidence intervals.
Within and Across Age Correlations between Substance Use Symptom Count Measures. Males are reported in the lower triangle and females in the upper triangle. Correlations (without decimals) are displayed within each colored box. To aid visualization, the matrix is a heat map, with hotter colors signifying higher correlations. The matrix is organized into blocks by age. Note the trend in the bolded diagonal blocks; the colors generally become cooler as one moves from the upper left to lower right, indicating a steady decrease in correlations among the substances over time. The off-diagonals have purposefully been partially obscured to focus the reader’s attention on the block diagonal without omitting relevant information about the cross-age correlations. Females in the younger cohort had just begun their age-29 assessment, and thus the age-14/age-29 block is empty.
Percent of Symptom Count Variance Accounted for by the General Factor at Each Age. The grey lines in the figure show the decline in correlations over time as expressed by decline in the average percentage of variance accounted for by a general factor at each time point (i.e., the “Mean Squared Loading” column from Table 3). The full sample is shown on the left while the subsample of individuals who had atleast one nicotine, alcohol, or marijuana symptom by their age-17 assessment is shown on the right. The decline for both sexes in both samples was statistically significant (see Results). Sample sizes are given in the text as well as in Table 2. In the full male and female samples the blue, green, and purple lines represent the proportions of the general factor phenotypic variance (grey lines) that are due to genetic (blue), shared environmental (purple), and non-shared environmental (green) variance. Male values are always represented by the darker hue. These estimates can be computed directly from values provided in Table 3 by multiplying the mean squared loading by the corresponding ACE value (e.g., by the A value to obtain the additive genetic variance which is plotted in blue). The majority of phenotypic decline in the full sample is due to a statistically significant decline in heritability, as well as a non-statistically significant decline in shared environmental variance. In contrast, non-shared environmental variance significantly increases with age. Note that age-14 estimates are not given because girls at this age had few symptoms (see Results). Genetic and environmental components are not given for the subsample as it was composed of selected individuals, and not selected twin pairs (see Methods).
No entities extracted from this document yet.
No uploaded files.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| A Bivariate Twin Study of Lifetime cannabis Initiation and Lifetime Regular Tobacco Smoking Across Three Different Countries. | Zellers S et al. | — | 2024 | → |
| Generalized genetic liability to substance use disorders. | Miller AP et al. | — | 2024 | → |
| Evaluating longitudinal relationships between parental monitoring and substance use in a multi-year, intensive longitudinal study of 670 adolescent twins. | Alexander JD et al. | — | 2023 | → |
| Investigating predictors of problematic alcohol, cannabis, and nicotine use among legal users of all three substances. | Shephard A et al. | — | 2023 | → |
| Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood. | Deak JD et al. | — | 2022 | → |
| Developmental and etiological patterns of substance use from adolescence to middle age: A longitudinal twin study. | Zellers SM et al. | — | 2022 | → |
| Longitudinal effects and environmental moderation of <i>ALDH2</i> and <i>ADH1B</i> gene variants on substance use from age 14 to 40. | Saunders GRB et al. | — | 2022 | → |
| The clashing nature of rebelliousness: Nontraditional attitudes and counter-normative behaviors show divergent associations with intelligence. | Isen JD et al. | — | 2022 | → |
| Externalizing Risk Pathways for Adolescent Substance Use and Its Developmental Onset: A Canadian Birth Cohort Study: Trajectoires de comportements extériorisés et le risque pour l'initiation et l'usage de substances des adolescents : Une étude de cohorte de naissance canadienne. | Cox SML et al. | — | 2021 | → |
| Orbitofrontal cortex thickness and substance use disorders in emerging adulthood: causal inferences from a co-twin control/discordant twin study. | Harper J et al. | — | 2021 | → |
| Polygenic Score for Smoking is associated with Externalizing Psychopathology and Disinhibited Personality Traits but not Internalizing Psychopathology in Adolescence. | Hicks BM et al. | — | 2021 | → |
| The Effects of Alcohol and Cannabis Use on the Cortical Thickness of Cognitive Control and Salience Brain Networks in Emerging Adulthood: A Co-twin Control Study. | Harper J et al. | — | 2021 | → |
| The roles of child maltreatment and fathers in the development of substance use in an at-risk sample of youth: A longitudinal study. | Yoon S et al. | — | 2021 | → |
| Adolescent Externalizing Psychopathology and Its Prospective Relationship to Marijuana Use Development from Age 14 to 30: Replication Across Independent Longitudinal Twin Samples. | Zellers SM et al. | — | 2020 | → |
| Polygenic Scores Predict the Development of Alcohol and Nicotine Use Problems from Adolescence through Young Adulthood | Deak JD et al. | — | 2020 | — |
| Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. | Liu M et al. | — | 2019 | → |
| Family Aggregation of Substance Use Disorders: Substance Specific, Nonspecific, and Intrafamilial Sources of Risk. | Farmer RF et al. | — | 2019 | → |
| Minnesota Center for Twin and Family Research. | Wilson S et al. | — | 2019 | → |
| Target-related parietal P3 and medial frontal theta index the genetic risk for problematic substance use. | Harper J et al. | — | 2019 | → |
| Unique developmental trajectories of risk behaviors in adolescence and associated outcomes in young adulthood. | Peeters M et al. | — | 2019 | → |
| Conflict-related medial frontal theta as an endophenotype for alcohol use disorder. | Harper J et al. | — | 2018 | → |
| DNA methylation from birth to late adolescence and development of multiple-risk behaviours. | de Vocht F et al. | — | 2018 | → |
| Impact of alcohol use on EEG dynamics of response inhibition: a cotwin control analysis. | Harper J et al. | — | 2018 | → |
| Longitudinal associations between youth tobacco and substance use in waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) Study. | Silveira ML et al. | — | 2018 | → |
| The genetic and environmental architecture of substance use development from early adolescence into young adulthood: a longitudinal twin study of comorbidity of alcohol, tobacco and illicit drug use. | Waaktaar T et al. | — | 2018 | → |
| The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design. | Iacono WG et al. | — | 2018 | → |
| Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students. | Webb BT et al. | — | 2017 | → |
| Personality Traits Predict the Developmental Course of Externalizing: A Four-Wave Longitudinal Study Spanning Age 17 to Age 29. | Walton KE et al. | — | 2017 | → |
| Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics. | Harper J et al. | — | 2017 | → |
| Beyond comorbidity: Toward a dimensional and hierarchical approach to understanding psychopathology across the life span. | Forbes MK et al. | — | 2016 | → |
| Genetic and Environmental Factors Associated with Cannabis Involvement. | Bogdan R et al. | — | 2016 | → |
| Genetic influences on adolescent behavior. | Dick DM et al. | — | 2016 | → |
| The correlates and course of multiple health risk behaviour in adolescence. | Hale DR et al. | — | 2016 | → |
| The Fallacy of Univariate Solutions to Complex Systems Problems. | Lessov-Schlaggar CN et al. | — | 2016 | → |
| Adolescent drinking and brain morphometry: A co-twin control analysis. | Wilson S et al. | — | 2015 | → |
| Association of substance dependence phenotypes in the COGA sample. | Wetherill L et al. | — | 2015 | → |
| Commentary on Kosty et al. (2015): Cannabis abuse from one generation to the next-a heightened vulnerability in women? | Melchior M | — | 2015 | → |
| Genome-Wide Association Study of Behavioral Disinhibition in a Selected Adolescent Sample. | Derringer J et al. | — | 2015 | → |
| Longitudinal stability and predictive utility of the visual P3 response in adults with externalizing psychopathology. | Yoon HH et al. | — | 2015 | → |
| The Power of Theory, Research Design, and Transdisciplinary Integration in Moving Psychopathology Forward. | Vaidyanathan U et al. | — | 2015 | → |
| The role of conduct disorder in the relationship between alcohol, nicotine and cannabis use disorders. | Grant JD et al. | — | 2015 | → |
| Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. | Moss HB et al. | — | 2014 | → |
| Examination of genetic variation in GABRA2 with conduct disorder and alcohol abuse and dependence in a longitudinal study. | Melroy WE et al. | — | 2014 | → |
| Genetic and environmental influences on gambling and substance use in early adolescence. | Vitaro F et al. | — | 2014 | → |
| Identifying childhood characteristics that underlie premorbid risk for substance use disorders: socialization and boldness. | Hicks BM et al. | — | 2014 | → |
| Rare nonsynonymous exonic variants in addiction and behavioral disinhibition. | Vrieze SI et al. | — | 2014 | → |
| The adolescent origins of substance use disorders: a behavioral genetic perspective. | McGue M et al. | — | 2014 | → |
| The role of constraint in the development of nicotine, marijuana, and alcohol dependence in young adulthood. | Vrieze SI et al. | — | 2014 | → |
| Are addictions diseases or choices? | Leyton M | — | 2013 | → |
| P300 amplitude reduction is associated with early-onset and late-onset pathological substance use in a prospectively studied cohort of 14-year-old adolescents. | Perlman G et al. | — | 2013 | → |
| Stability and change of genetic and environmental effects on the common liability to alcohol, tobacco, and cannabis DSM-IV dependence symptoms. | Palmer RH et al. | — | 2013 | → |
| Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring. | Vrieze SI et al. | — | 2013 | → |
| Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world. | Vrieze SI et al. | — | 2012 | → |
| Substance-specific symptoms and general liability to addiction. | Vanyukov MM | — | 2012 | → |