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  • Perspective
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The Genetically Informed Neurobiology of Addiction (GINA) model

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Abstract

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

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Fig. 1: Corticostriatal and corticolimbic circuits underlying addiction.
Fig. 2: The genomic architecture of SUDs.
Fig. 3: Using genomics to validate hypotheses of addiction.
Fig. 4: The GINA model.

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Acknowledgements

The authors acknowledge the following funding from the United States National Institutes of Health: R.B. (R01DA54750; R21AA27827, U01DA055367), A.S.H. (T32DA007261, K01AA030083), E.C.J. (K01DA51759), A.A. (K02DA32573, R01DA54750). Funders were not involved in the preparation of this manuscript in any way.

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All authors researched data for the article and wrote the article. A.A., A.S.H. and R.B. contributed substantially to discussion of the content. A.A., E.C.J. and R.B. reviewed and/or edited the manuscript before submission.

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Correspondence to Ryan Bogdan or Arpana Agrawal.

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Nature Reviews Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

HEALthy Brain and Cognitive Development (HBCD) study: https://heal.nih.gov/research/infants-and-children/healthy-brain

Glossary

Anhedonia

The loss of pleasure or lack of reactivity to pleasurable stimuli.

Binging

Consuming a large amount of a substance (typically alcohol) in a short period of time.

Candidate gene

A gene posited to be associated with a phenotype based on prior knowledge.

Compulsive use

Drug consumption that is not under control and typically functions to achieve drug-present homeostasis and alleviation of negative affect/withdrawal as opposed to drug-induced euphoric reward.

Craving

A persistent desire to use a substance.

Developmental vulnerability

Vulnerability to a given outcome that arises in the context of typical development.

Executive function

Complex mental processes and cognition (for example, working memory) that control skills (for example, organizing, solving) and regulate emotion and behaviour.

Expression quantitative trait loci

(eQTL). Genetic variants that modify the expression of a gene by acting upon the regulatory elements of the gene.

Fractional anisotropy

A measure of the degree of anisotropy of a diffusion process ranging from 0 to 1. In the context of diffusion tensor imaging, it reflects the uniform directionality of white-matter fibres in the brain and is often conceptualized as an index of white matter integrity and structural connectivity.

Gene variants

Sections of DNA sequence that differ across groups of individuals.

Genetic architectures

Distinct genetic factors that influence one or more traits.

Genetic liability

The contribution of genetic factors to the likelihood of observing a phenotype.

Genetic nurture

The effect of genetically influenced parent behaviour on offspring behaviour.

Genome-wide association studies

(GWAS). A hypothesis-free analysis of the association between common genetic variation across the genome and a phenotype.

Genomic structural equation modelling

A statistical genetics method for identifying genetic variants that influence multiple phenotypes as well as each individual phenotype.

Heritability

The proportion of total variation in a phenotype that is due to genetic factors.

Incentive salience

A cognitive process that motivates behaviour towards reward.

Machine learning

A data-driven approach that iteratively examines a training data set for patterns across large numbers and diverse types of variables associated with an outcome and, upon ‘learning’ these data patterns, can be used to test whether these patterns accurately predict the outcome in independent data sets.

Negative reinforcement

The removal of something unpleasant or uncomfortable by a stimulus and/or behaviour.

Negative urgency

A personality facet related to impulsive behaviour in the context of negative mood or experiences.

Pleiotropic effects

The influences of a variant, gene or groups of variants on multiple phenotypes.

Polygenic

The genetic characteristic of traits that is due to the aggregated small effects of many genetic variants.

Positive reinforcement

Reward obtained after a stimulus and/or behaviour.

Positive urgency

A facet of personality related to impulsive behaviour in the context of anticipated reward.

Predictive reward signals

Neural signals that demarcate the expected delivery of reward following extrinsic and/or intrinsic cues.

Predispositional liability

The aspect of an outcome that is attributable to predispositional (that is, genetic variation, prior experiences) factors.

Regulatory elements

Components of a gene, such as the promoter and introns, that regulate its expression.

Resting-state functional connectivity

Correlated signal between brain regions in the absence of any stimulus or task.

Single nucleotide polymorphism

(SNP). A single base pair in the genome that varies across individuals.

Trait-like vulnerability

Vulnerability to a given trait.

Twin studies

Comparisons of phenotype correlations in identical and fraternal twins to parse the role of genetic and environmental effects on a given phenotype or set of phenotypes.

Withdrawal

Physical (for example, headaches and insomnia) and psychological (for example, depressed mood) aversive experiences that occur when use of a substance is discontinued.

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Bogdan, R., Hatoum, A.S., Johnson, E.C. et al. The Genetically Informed Neurobiology of Addiction (GINA) model. Nat Rev Neurosci 24, 40–57 (2023). https://doi.org/10.1038/s41583-022-00656-8

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