Author: James Anderson

How Is Drug Addiction Related to Your Genes and Environment?

Understanding the role genes play in addiction leads to better, more specific treatments. Sometimes a potential addiction-related gene is discovered in people, like in the example above. Other genes are discovered first in an animal model and confirmed later in people. The detection of locus-to-locus or locus-to-phenotype genetic linkage.

Twin studies can to some extent disentangle the roles of genetic heterogeneity and polygenicity–epistasis. 2,14 under the epistatic model, combinations of genetic variants, each represented as a puzzle piece, determine phenotypes. Estimates of the heritability of CocUD range from ~0.40 to 0.80, with evidence of a common genetic vulnerability with other SUDs, especially cannabis, and little evidence of cocaine-specific genetic influences (Kendler et al., 2007). Prior to DSM-5, DSM-IV distinguished substance abuse from dependence.

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Our future understanding of addictions will be enhanced by the identification of genes that have a role in altered substance-specific vulnerabilities such as variation in drug metabolism or drug receptors and a role in shared vulnerabilities such as variation in reward or stress resiliency. Current SUD PGS explain a relatively small proportion of variance (generally 1–5%) in SUD-related outcomes, especially relative to other known risk factors (SES, SUD family history, comorbid psychiatric disorders; Barr et al., 2020). Furthermore, the best-powered GWAS of SUDs to date have been conducted primarily in samples of European ancestries, limiting their predictive utility to individuals who are also of European ancestry (Martin, Daly, Robinson, Hyman, & Neale, 2019). Finally, PGS can be difficult to interpret in layperson’s terms (i.e. being in the 95th percentile of polygenic risk for alcohol dependence does not mean you have a 95% chance of developing the disorder).

Compared to other genetic predictors, the genomic pattern identified here was also a more sensitive predictor of having two or more substance use disorders at once. The genomic pattern linked to general addiction risk also predicted higher risk of mental and physical illness, including psychiatric disorders, suicidal behavior, respiratory disease, heart disease, and chronic pain conditions. In children aged 9 or 10 years without any experience of substance use, these genes correlated with parental substance use and externalizing behavior. Rare genetic variants relevant to addiction have been found within the serotonin receptor 2B gene (HTR2B) and MAOA, and several of the functional CYP26 alleles are also rare or uncommon. Both HTR2B and MAOA influence impulsivity and behavioral control and findings for these genes in humans remarkably parallel animal models. Another potential direction is the integration of human genetic data with findings from animal models of addiction endophenotypes (Reynolds et al., 2020).

Fagerström Tolerance Questionnaire (FTQ), Fagerström Test for Nicotine Dependence (FTND)] in comparison to NicUD as determined by DSM diagnostic criteria (Cohen, Myers, & Kelly, 2002; Payne, Smith, McCracken, McSherry, & Antony, 1994). “Using genomics, we can create a data-driven pipeline to prioritize existing medications for further study and improve chances of discovering new treatments. While finding the precise genetic cause is tricky, multiple lines of research do show that genes influence substance use.

This approach can operate in a bidirectional fashion, with datasets rich in ‘omics informing follow-up in large-scale GWAS of addiction phenotypes/biomarkers and vice versa (Figure 1), and accelerate understanding of the biology underlying statistical associations with addiction phenotypes. The addictions are common chronic psychiatric diseases that today are prevented and treated using relatively untargeted and only partially effective methods. The addictions are moderately to highly heritable, which is paradoxical because these disorders require use; a choice that is itself modulated by both genes and environment. The addictions are interrelated and related to other psychiatric diseases by common neurobiological pathways, including those that modulate reward, behavioural control and the anxiety or stress response.

Candidate Genes

Where once a single ‘omics type was used, studies that capture multiple ‘omics data types in the same dataset are emerging. As these datasets become available, concurrent integration that jointly assess all data may unveil relationships not evident when each data type is analyzed separately by virtue of increased statistical power and their explicit biological relationships (Figure 1). It is expected that genetic variants with large effect sizes are identifiable in sequential analyses but that concurrent integration will enable the identification of genetic variants with moderate-sized, but multi-faceted, functional or regulatory effects.

1. One gene therapy being tested in mice generates antibodies that trap methamphetamine, preventing it from reaching the brain.
2. They found that adopted children had a higher risk of drug addiction if their biological parents were addicted.
3. GWAS have discovered several novel, replicable variants contributing to addiction.
4. It is expected that genetic variants with large effect sizes are identifiable in sequential analyses but that concurrent integration will enable the identification of genetic variants with moderate-sized, but multi-faceted, functional or regulatory effects.

For example, lifetime cannabis ever-use shows positive genetic correlations with education and age at first birth, and a negative correlation with BMI (+, +, −; Pasman et al., 2018), while CanUD shows genetic correlations in the opposite direction of effect for these three traits (−, −, +; Johnson et al., 2020b). This suggests that, while necessary for the development of CanUD, cannabis initiation is at least partly genetically distinct from CanUD. Mirroring findings from twin and family studies, GWAS of CanUD have identified significant genetic overlap between CanUD and other SUDs and measures of substance use. CanUD showed significant positive genetic correlations with smoking initiation, ND, CPD, DPW, and AUD (rg ranging from 0.31 to 0.66; Table 1; Johnson et al., 2020b). One overarching question that has emerged from the first-generation of well-powered SUD GWAS is whether measures of non-problematic substance use have divergent genetic underpinnings from SUDs, and if so, to what extent. Another area of interest has been dissecting the genetic relationships between SUDs, other psychiatric disorders, and relevant complex traits; by leveraging large GWAS and advanced statistical genetics methods [e.g.

Interplay between Genetic and Enviromental Factors

And people who share certain high-risk gene variations may or may not have the trait. Next, the researchers look for segments of chromosomes that are more common in affected people compared to unaffected. Because people have complex and varied lives, in-depth studies are often done using animals in a controlled lab setting.

Additional studies have examined subdomains of alcohol consumption, suggesting potential etiological differences between alcohol consumption frequency and alcohol consumption quantity (Mallard et al., 2020; Marees et al., 2020b). Specifically, alcohol consumption quantity was found to be more genetically similar to AUD and psychopathology, while frequency demonstrated negative relationships with AUD and other psychiatric outcomes, and was found to be influenced by measures of SES (Mallard et al., 2020; Marees et al., 2020b). Thus, evidence of genetic dissimilarity between consumption and AUD may be being driven by frequency of drinking, which in turn, is being influenced by indices of SES. Further studies probing this relationship will be needed to fully disentangle the nuance of the shared and unique genetic etiology across the spectrum of alcohol consumption levels (e.g. normative consumption, binge drinking) and AUD.

Long-term, they may be used to predict which treatments are likely to be most effective based on an individual’s genetic profile. Each new addiction-related gene discovered is a potential “drug target.” Researchers can focus on the gene product (protein) and develop a drug to modify its activity. The goal is to correct signals or pathways and restore proper brain function.

Further research is needed to fully understand the potential benefits, and possible harms, of incorporating genetic information (e.g. PGS) into SUD treatment planning (Driver, Kuo, & Dick, 2020; Lebowitz, 2019; Lebowitz & Ahn, 2018). Despite divergent patterns of genetic overlap suggesting non-uniform genetic influences, it should be noted that genes influencing alcohol-metabolizing enzymes (e.g. ADH1B, ALDH2) directly affect alcohol consumption, and in turn, play a role in the risk of AUD development. The coding variants in these genes provide a protective effect for AUD by producing aversive effects when drinking alcohol, often resulting in lower levels of consumption and AUD risk (Edenberg & Mcclintick, 2018). However, it is likely that thousands of additional genetic loci play a role beyond the genes encoding alcohol metabolizing enzymes.