Recent advances in the genetic epidemiology and molecular genetics of substance use disorders.

paper Cited Public

Authors: Kendler, Kenneth S; Chen, Xiangning; Dick, Danielle; Maes, Hermine; Gillespie, Nathan; Neale, Michael C; Riley, Brien
Year: 2012
Journal: Nature neuroscience
PMID: 22281715
DOI: 10.1038/nn.3018
PMCID: PMC3297622

Figure 1

Parameter estimates for the best-fitting confirmatory model for symptoms of cannabis, cocaine, alcohol, caffeine and nicotine dependence. The double-headed arrow connecting the illicit and licit substance genetic factors represents the genetic correlation between these factors. A stands for additive genetic and E for environmental effects unique to each twin. E1 reflects the common factor for the unique environmental effects that affects risk for dependence across all of the psychoactive substances examined. The top part of the figure depicts genetic factors that influence drug dependence and the bottom part reflects environmental factors. The numbers on the paths represent standardized loadings and thus need to be squared to reflect the proportion of variance in the observed SUD accounted for by the factor.

Figure 2

Parameter estimates for the contributions to variation in liability to psychoactive drug use of additive genetic effects (a2, red), familial environmental factors (c2, light blue) and the individual-specific environment (e2, dark blue) by year for average daily number of cigarettes, ages 13–40. Data from ref. 11.

Figure 3

An example of gene-environment interaction: as parental monitoring increases, the importance of genetic factors significantly decreases, and the influence of common environmental factors becomes more important such that the most important etiological factors affecting adolescent smoking vary markedly as a function of parental monitoring. Data from ref. 18; graph shows results of model-fitting as described in detail in ref. 18.

Figure 4

Hypothetical item response curves for SUD diagnostic criteria. The item depicted by curve 1 is a criterion likely to be met by all individuals, except those with very low risk of SUD. It discriminates well between those who meet it and those who do not because the curve is steep, and this is also true curve 2, which has the same steepness but distinguishes between individuals 1 s.d. higher on the liability distribution. Curve 3 is the flattest and is consequently the least discriminating because the probability of showing this symptom changes more slowly as the risk of SUD increases. Curve 4 discriminates perfectly, but it is only useful for measuring individuals close to the 1.5 s.d. point on the distribution.

#	Section	Preview
0	Introduction	Classical twin studies, which compare the similarity of monozygotic and dizygotic twins, are a…
1	Introduction	Twin studies provide substantial evidence that genetic factors contribute to the etiology of SUDs.…
2	Introduction	How specific are the genetic risk factors for SUDs? Studies that address this question have tended…
3	Introduction	Two large-scale twin studies of illicit drugs showed that most genetic risk is common across the…
4	Introduction	How do the genetic risk factors for SUDs fit into the broader scheme of genetic risk for other…
5	Introduction	Finally, preliminary studies have examined whether individual SUD diagnoses reflect single or…
6	Genes and environment: how they contribute to SUDs	Genetic epidemiological studies have made progress in clarifying how genetic and environmental…
7	Developmental inter-relationships	Within a developmental framework, how do shared environmental experiences in the home and community…
8	Developmental inter-relationships	When twins were asked to record their average daily cigarette usage over their lifetime11, analyses…
9	Developmental inter-relationships	These results suggest that high levels of SUD typically arise through a two-stage process. First,…
10	Gene-environment correlation	Most individuals think about genes and environment as two separate and independently acting sets of…
11	Gene-environment correlation	Gene-environment correlation for SUD (perhaps more accurately termed “genetic controls of exposure…
12	Gene-environment correlation	Thus for SUD, which is so clearly influenced by social phenomena, two pathways exist from genes to…
13	Gene-environment interaction	There is accumulating evidence from twin studies that the importance of genetic influences on…
14	Gene-environment interaction	higher alcohol sales15, presumably through easier access for those with a predisposition. In fact,…
15	Gene-environment interaction	Conversely, a related mechanism for gene-environment interaction is that of social expression or…
16	Gene-environment interaction	Yet another potential mechanism of relevance for SUDs is the connection to stress. Stress and stress…
17	Gene-environment interaction	The impact of stress on genetic risk for SUDs has not been as widely studied in the twin literature…
18	Building contingency in models for substance use disorders	Genetic studies of SUD need to cope with one obvious fact: genes for SUD cannot act in the absence…
19	Building contingency in models for substance use disorders	Twin studies offer a way around this problem. We can estimate the strength of this relationship in…

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Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications.	2017	28240821
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From genes to treatments: a systematic review of the pharmacogenetics in smoking cessation.	Salloum NC et al.	—	2018	→
Genetic Association and Expression Analyses of the Phosphatidylinositol-4-Phosphate 5-Kinase (PIP5K1C) Gene in Alcohol Use Disorder-Relevance for Pain Signaling and Alcohol Use.	Lee JS et al.	—	2018	→
Genome wide analysis of rare copy number variations in alcohol abuse or dependence.	Rodríguez-López J et al.	—	2018	→
Individual decision-making in the causal pathway to addiction: contributions and limitations of rodent models.	Ahmed SH	—	2018	→
Role of placebo effects in pain and neuropsychiatric disorders.	Belcher AM et al.	—	2018	→
Shaping vulnerability to addiction - the contribution of behavior, neural circuits and molecular mechanisms.	Egervari G et al.	—	2018	→
Socioeconomic Status and Adolescent Alcohol Involvement: Evidence for a Gene-Environment Interaction.	Davis CN et al.	—	2018	→
Testing Genetic and Environmental Associations Between Personality Disorders and Cocaine Use: A Population-Based Twin Study.	Gillespie NA et al.	—	2018	→
The Value of Biosamples in Smoking Cessation Trials: A Review of Genetic, Metabolomic, and Epigenetic Findings.	Saccone NL et al.	—	2018	→
Trying to make sense of rodents' drug choice behavior.	Ahmed SH	—	2018	→
Voltage-Sensitive Potassium Channels of the BK Type and Their Coding Genes Are Alcohol Targets in Neurons.	Dopico AM et al.	—	2018	→
A Concept Analysis of Substance Misuse to Inform Contemporary Terminology.	Mahmoud KF et al.	—	2017	→
Adolescent substance use behavior and suicidal behavior for boys and girls: a cross-sectional study by latent analysis approach.	Wang PW et al.	—	2017	→
Attention deficit hyperactivity disorder and substance abuse: An investigation in young Austrian males.	Riegler A et al.	—	2017	→
Cdh13 and AdipoQ gene knockout alter instrumental and Pavlovian drug conditioning.	King CP et al.	—	2017	→
Genetic influences on hormonal markers of chronic hypothalamic-pituitary-adrenal function in human hair.	Tucker-Drob EM et al.	—	2017	→
Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-Response Behaviors in Model Organisms.	Adkins AE et al.	—	2017	→
Parent and peer influences on emerging adult substance use disorder: A genetically informed study.	Bountress K et al.	—	2017	→
Polygenic Scores for Major Depressive Disorder and Risk of Alcohol Dependence.	Andersen AM et al.	—	2017	→
Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications.	Dick DM et al.	—	2017	→
A Primer on the Genetics of Comorbid Eating Disorders and Substance Use Disorders.	Munn-Chernoff MA et al.	—	2016	→
Associations between Polygenic Risk for Psychiatric Disorders and Substance Involvement.	Carey CE et al.	—	2016	→
DNA methylation and substance-use risk: a prospective, genome-wide study spanning gestation to adolescence.	Cecil CA et al.	—	2016	→
Dopamine and serotonin genetic risk scores predicting substance and nicotine use in attention deficit/hyperactivity disorder.	Groenman AP et al.	—	2016	→
Fischer 344 and Lewis Rat Strains as a Model of Genetic Vulnerability to Drug Addiction.	Cadoni C	—	2016	→
Genetic Influences on Hormonal Markers of Chronic HPA Function in Human Hair	Tucker-Drob EM et al.	—	2016	—
Maternal smoking during pregnancy and offspring's tobacco dependence. A study of exposure-discordant sibling pairs.	Rydell M et al.	—	2016	→
Narrative review of genes, environment, and cigarettes.	Do E et al.	—	2016	→
Onset of Alcohol Use Disorders and Comorbid Psychiatric Disorders in a Military Cohort: Are there Critical Periods for Prevention of Alcohol Use Disorders?	Fink DS et al.	—	2016	→
Overview of the Genetics of Alcohol Use Disorder.	Tawa EA et al.	—	2016	→
Polygenic Risk for Externalizing Psychopathology and Executive Dysfunction in Trauma-Exposed Veterans.	Sadeh N et al.	—	2016	→
Sex differences in genetic and environmental contributions to alcohol consumption from early adolescence to young adulthood.	Seglem KB et al.	—	2016	→
The Genetic and Environmental Association Between Parental Monitoring and Risk of Cannabis, Stimulants, and Cocaine Initiation in a Sample of Male Twins: Does Parenting Matter?	Olivares EL et al.	—	2016	→
The Relationships of Parental Alcohol Versus Tobacco and Marijuana Use With Early Adolescent Onset of Alcohol Use.	Capaldi DM et al.	—	2016	→
Time to connect: bringing social context into addiction neuroscience.	Heilig M et al.	—	2016	→
Developmental Changes in Genetic and Shared Environmental Contributions to Smoking Initiation and Subsequent Smoking Quantity in Adolescence and Young Adulthood.	Bares CB et al.	—	2015	→
Evaluating the Validity of Caffeine Use Disorder.	Budney AJ et al.	—	2015	→
Genetic and Environmental Interplay in Adolescent Substance Use Disorders.	Hines LA et al.	—	2015	→
Interactions between DRD4 and developmentally specific environments in alcohol-dependence symptoms.	Carlson MD et al.	—	2015	→
Intergenerational continuity in parents' and adolescents' externalizing problems: The role of life events and their interaction with GABRA2.	Salvatore JE et al.	—	2015	→
SWI/SNF chromatin remodeling regulates alcohol response behaviors in Caenorhabditis elegans and is associated with alcohol dependence in humans.	Mathies LD et al.	—	2015	→
The brain disease model of addiction: is it supported by the evidence and has it delivered on its promises?	Hall W et al.	—	2015	→
The mathematical limits of genetic prediction for complex chronic disease.	Keyes KM et al.	—	2015	→
Treatment of Adolescent Substance Use Disorders and Co-Occurring Internalizing Disorders: A Critical Review and Proposed Model.	Hulvershorn LA et al.	—	2015	→
Academic achievement as a moderator of genetic influences on alcohol use in adolescence.	Benner AD et al.	—	2014	→
A redescription of data does not count as a general theory.	Ahmed SH	—	2014	→
Exposure to maternal smoking during pregnancy as a risk factor for tobacco use in adult offspring.	Rydell M et al.	—	2014	→
Genetic basis of delay discounting in frequent gamblers: examination of a priori candidates and exploration of a panel of dopamine-related loci.	Gray JC et al.	—	2014	→
In-utero exposure to maternal smoking is not linked to tobacco use in adulthood after controlling for genetic and family influences: a Swedish sibling study.	Rydell M et al.	—	2014	→
Personality traits and vulnerability or resilience to substance use disorders.	Belcher AM et al.	—	2014	→
Protection genes in nucleus accumbens shell affect vulnerability to nicotine self-administration across isogenic strains of adolescent rat.	Chen H et al.	—	2014	→
The behavioral economics of substance use disorders: reinforcement pathologies and their repair.	Bickel WK et al.	—	2014	→
The role of the BK channel in ethanol response behaviors: evidence from model organism and human studies.	Bettinger JC et al.	—	2014	→
A targeted review of the neurobiology and genetics of behavioural addictions: an emerging area of research.	Leeman RF et al.	—	2013	→
Consensus paper of the WFSBP task force on biological markers: biological markers for alcoholism.	Hashimoto E et al.	—	2013	→
Extended heroin access increases heroin choices over a potent nondrug alternative.	Lenoir M et al.	—	2013	→
Shared genetic vulnerability for disordered gambling and alcohol use disorder in men and women: evidence from a national community-based Australian Twin Study.	Slutske WS et al.	—	2013	→
Striatal ups and downs: their roles in vulnerability to addictions in humans.	Leyton M et al.	—	2013	→
The genetics of alcohol dependence.	Rietschel M et al.	—	2013	→
Genetic factors control nicotine self-administration in isogenic adolescent rat strains.	Chen H et al.	—	2012	→