Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits

Quach, Bryan C.; Bray, Michael J.; Gaddis, Nathan; Liu, Mengzhen; Palviainen, Teemu; Minică, Camelia C.; Zellers, Stephanie; Sherva, Richard; Alıev, Fazil; Nothnagel, Michael; Young, Kendra A.; Marks, Jesse; Young, Hannah; Carnes, Megan U.; Guo, Yuelong; Waldrop, Alex; Sey, Nancy Y A; Landi, Maria Teresa; McNeil, Daniel W.; Drichel, Dmitriy; Farrer, Lindsay A.; Markunas, Christina A.; Vink, Jacqueline M.; Hottenga, Jouke Jan; Iacono, William G.; Kranzler, Henry R.; Saccone, Nancy L.; Neale, Michael C.; Madden, Pamela A. F.; Rietschel, Marcella; Marazita, Mary L.; McGue, Matthew; Won, Hyejung; Winterer, Georg; Grucza, Richard A.; Dick, Danielle M.; Gelernter, Joel; Caporaso, Neil E.; Baker, Timothy B.; Boomsma, Dorret I.; Kaprio, Jaakko; Hokanson, John E.; Vrieze, Scott; Bierut, Laura J.; Johnson, Eric O.; Hancock, Dana B.

doi:10.1038/s41467-020-19265-z

Cited by 93 publications

(107 citation statements)

References 96 publications

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“…We used summary statistics from the GWAS of the Fagerström Test for Nicotine Dependence 25 (FTND). Because the genetic correlation between FTND and cigarettes per day is high (calculated rG = 0.95 30 ), we applied Multi-Trait Analysis of Genome-wide association study summary statistics (MTAG 31 ) to summary statistics generated from the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) GWAS of cigarettes per day to create a combined phenotype 26 . As this is the only observed genetic correlation between a use and use disorder category that is sufficiently high to allow for MTAG analysis, we used this procedure only for tobacco.…”

Section: Methodsmentioning

confidence: 99%

The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates

Hatoum

Johnson²,

Polimanti

et al. 2021

Preprint

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BackgroundSubstance use disorders (SUDs) commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We estimate the shared genetic architecture across distinct SUDs, its independence from genetic liability to substance use, and its relation to genetic liability to impulsivity, negative affect, and executive function as well as non-substance psychopathology.MethodsWe tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying Genomic structural equation modelling to genome-wide association study statistics (Total N = 1,019,521; substance specific Ns range: 82,707-435,563 of European ancestry), while adjusting for the genetics of substance use (Ns = 184,765-632,802). We tested whether shared liability across SUDs is associated with behavioral constructs (risk taking, executive function, neuroticism; Ns = 328,339-427,037) and non-substance use psychopathology (mood/psychotic, compulsive, and early neurodevelopmental disorders).ResultsShared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction factor (termed a(g)), independent of substance use. OUD and CUD demonstrated the largest loadings. a(g) was associated with risk taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance (standardized residual= .579).InterpretationA common genetic addictions factor partly explains susceptibility for alcohol, tobacco, cannabis and opioid use disorder. a(g) has unique pathways that are not shared with substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology.

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Section: Methodsmentioning

confidence: 99%

The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates

Hatoum

Johnson²,

Polimanti

et al. 2021

Preprint

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“…There are 259 genetic loci associated with smoking initiation, including risk seeking propensity and nicotine metabolism rates, indicating a strong genetic component to developing TUD. 65 One gene specifically, the REV3L protein coding gene, displays a high level of correlation with smoking initiation. Decreasing the expression of the REV3L gene reduces the probability of initiation, identifying it as a potential gene to target with drugs for cessation and prevention.…”

Section: Neurobiologymentioning

confidence: 99%

A 5-Factor Framework for Assessing Tobacco Use Disorder

Bucklin

2021

Tob Use Insights

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Cigarette use is the leading cause of preventable death in the United States. Despite the well documented dangers of smoking, nearly 20% of adults report regular use of tobacco. A majority desire to discontinue but the long-term cessation success rate remains near 4%. One challenge to reducing the prevalence of tobacco use is an incomplete understanding of the individual correlates that reinforce continued use. Evidence from research on nicotine and tobacco suggests that Tobacco Use Disorder is a complex, and multifactorial condition. Personality traits, comorbidities, habits and lifestyle, genetics, socioeconomic status, and mental and physical health all contribute to the risk for dependence and to the likelihood of quitting. This perspective review provides an overview of some common factors that contribute to liability risk for Tobacco Use Disorder and a framework for assessing individual tobacco users. The framework includes 5 areas that research suggests contribute to continued tobacco use: nicotine addiction, psychological influences, behavioral dependencies, neurobiological factors, and social reinforcement. Nicotine addiction includes drug-seeking behavior and the role of withdrawal avoidance. Psychological and emotional states contribute to a perceived reliance on tobacco. Behavioral dependence is reinforced by associative and non-associative learning mechanisms. Neurobiological factors include genetic variables, variations in neurotransmitters and receptors, pharmacogenetics, and interaction between psychiatric illnesses and nicotine use and dependence. Finally, social reinforcement of smoking behavior is explained by a network phenomenon and consistent visual cues to smoke. A comprehensive assessment of individual tobacco users will help better determine appropriate treatment options to achieve improved efficacy and outcomes.

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“…C. Dopaminergic neuronal (DN) CREs were linked to their target genes using DN Hi-C data, while cortical neuronal (CN) CREs were linked to target genes using CN Hi-C data. D. Genes mapped to DN-CREs were highly expressed in midbrain dopaminergic (DOP2) and cholinergic neurons (CHO1), while genes mapped to CN-CREs were highly expressed in telencephalic glutamatergic neurons (GLU1, 3,7,11). E-F.…”

Section: Figure 1 Gene Regulatory Landscape In Cortical and Dopaminementioning

confidence: 99%

“…Genome wide association studies (GWAS) on smoking and alcohol use traits have demonstrated that common variation explains a significant proportion of phenotypic variance of substance use 4 . Nearly 400 genomic loci were found to have an impact on smoking and/or alcohol use traits from GWAS sample sizes up to 1.2 million [4][5][6][7] . However, the vast majority of associated variants reside in non-coding DNA, and their target genes and relevant neurobiological mechanisms are poorly understood.…”

Section: Introductionmentioning

confidence: 99%

“…To understand the functional impact of common variants associated with cigarette smoking and alcohol use, we applied Hi-C coupled MAGMA (H-MAGMA) 12 to GWAS of smoking and alcohol use traits and identified their putative target genes for further characterization 5–7 . Smoking and alcohol use traits likely affect neurocircuits that underlie addiction and include the prefrontal cortex (PFC), nucleus accumbens (NAc), amygdala, and midbrain dopaminergic cell groups such as ventral tegmental area (VTA) and substantia nigra (SN) 14–16 .…”

Section: Introductionmentioning

confidence: 99%

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Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

Won

Akbarian

Hancock

et al. 2021

Preprint

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Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ∼26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

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Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits

Cited by 93 publications

References 96 publications

The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates

The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates

A 5-Factor Framework for Assessing Tobacco Use Disorder

Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

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