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Genetic research into addiction along with other neuropsychiatric disorders has been based on two different hypotheses. The first is the common disease–common variant hypothesis (Yu and McClellan, 2016). According to this hypothesis, the genetically based susceptibility to a particular neuropsychiatric disorder stems from a pool of risk-conferring gene alleles that are possessed in common throughout the population. Each of these “risk alleles” that you may carry confers a small increase in susceptibility to developing the disorder. Your overall genetically based susceptibility, therefore, is determined by the sum of all the risk alleles you carry. A more recently proposed alternative, called the common disease–rare variant hypothesis, arose as researchers determined that neuropsychiatric disorders tend to be extremely heterogeneous in their genetic risk alleles. That is, different individuals diagnosed with the same disorder may carry vastly different genetic profiles. According to this hypothesis, therefore, a significant portion of the genetic risk for a neuropsychiatric disorder stems from rare mutations or other genetic anomalies such as copy number variations (variable numbers of repeated stretches of DNA). Note that findings supporting the rare variant hypothesis have been obtained largely from studies of non-addictive disorders such as autism rather than studies of addiction itself. Nevertheless, researchers believe that those findings are likely to be applicable to addiction as well. Moreover, the two hypotheses just outlined are not mutually exclusive; indeed, the risk of developing an addictive disorder may well depend on a combination of both common and rare risk alleles.
Three main approaches are used to study the genetics of neuropsychiatric disorders, such as addiction (Yu and McClellan, 2016). These are candidate gene analysis, linkage analysis, and genome-wide association studies (GWAS). Although these terms may sound daunting at first, they are not so difficult to understand. When researchers use the candidate gene approach, they are focusing on the potential involvement of genes that have been preselected based on the biochemical mechanisms thought to underlie the disorder. In the case of addiction, for example, we shall see later in the chapter that the neurotransmitter dopamine (DA) plays a key role in the rewarding and reinforcing effects of many addictive drugs. Based on this information, scientists interested in the genetics of addiction might start by determining whether drug addicts, compared to healthy controls, tend to carry certain alleles for genes that code for proteins that participate in dopaminergic transmission. The relevant proteins in this case would include enzymes involved in DA synthesis and/or metabolism, the membrane DA transporter protein, and the family of DA receptors. Although the candidate gene approach was the first to identify specific genes involved in vulnerability for addiction, it suffers from the so-called streetlight effect. This is “a type of bias whereby people search for something only where it is easiest to search” (Hall et al., 2020, p. 1).*
The second approach, linkage analysis, seeks to find chromosomal regions that tend to associate with the disorder being studied. Once such a region has been identified, the next step is to identify a particular mutation or group of mutations present within that region of DNA that enhances susceptibility to the disorder.
The third approach, GWAS, is the most favored of the three in current research efforts. As the name implies, this approach investigates the entire genome in search of alleles that associate with a disorder. Multiple alleles of a gene often differ by single nucleotides (the AGCTs of the genetic code) at various positions along the DNA sequence that makes up the gene. These variations of individual nucleotides are called single-nucleotide polymorphisms (abbreviated SNPs and pronounced “snips”).** For a SNP to be identified in a particular gene, the gene must not only possess multiple alleles as mentioned above, but also every allele must be present in at least 1% of the population. Such a situation implies that these alleles must have persisted for a significant period of evolutionary time and have been inherited across many generations. When a very rare (much less than 1%) nucleotide change is observed, such a change is interpreted as a recent mutation, either in the carrier of that gene or in a relatively recent ancestor like a parent or grandparent.
Figure 1 shows the distinction between SNPs and mutations. Despite this difference, both SNPs and mutations are important because changing just a single nucleotide usually changes the identity of one of the amino acids in the protein coded for by that gene, and this lone amino acid alteration may significantly modify the functioning of the protein. To illustrate this last point using our example of the dopaminergic system, researchers have found SNPs in the human tyrosine hydroxylase gene that result in lower enzymatic activity and, therefore, a slower rate of DA synthesis (Haavik et al., 2008). Moreover, a SNP in the gene for the human D2 DA receptor causes a reduction in DA binding. Carriers of this form of the receptor appear to exhibit poorer cognitive function and may be at increased risk for attention-deficit/hyperactivity disorder (ADHD; Gluskin and Mickey, 2016).
Figure 1 Variation in a single nucleotide may signify either a single-nucleotide polymorphism (SNP) or a mutation Shown is the nucleotide sequence of a small region of a hypothetical gene in which two different alleles have been discovered by genotyping a large number of people. The results depicted on the left would be interpreted as a SNP in this region of the gene, because the alternate allele (with T in place of C) was found in over 1% of the sample. In contrast, the results depicted on the right would be interpreted as resulting from a mutation, because the alternate allele was only found in a very small percentage of the sample and, therefore, is very rare.
With this background information in mind, what is the current state of our knowledge about the genetics of addiction?
The first point is that most genes that have been found to associate with addiction or a substance use disorder contribute only a very small percentage to the risk for developing the disorder. Thus, substance use disorders align with other neuropsychiatric disorders such as schizophrenia or autism in being polygenic, because multiple gene alleles contribute to the overall risk for the disorder to occur.
Second, although some genes may confer risk for a specific substance use disorder (e.g., alcohol use disorder or cocaine use disorder), we don’t yet know whether there are genes that confer broad risk for any substance use disorder. A recent review of meta-analyses performed on substance use disorder genetics (including results from both candidate gene studies and GWAS) indicated that only the following genes have thus far been reliably associated with at least two different substance use disorders: OPRM1 (µ1 opioid receptor), DRD2 (D2 DA receptor), DRD4 (D4 DA receptor), SL6A4 (serotonin transporter), and BDNF (brain-derived neurotrophic factor) (Lopez-Leon et al., 2021). Two additional genes that have significant relationships to specific substances are ADH1B (alcohol dehydrogenase 1B, an enzyme important for alcohol metabolism) for alcohol dependence (Gelernter et al., 2014) and CHRNA5-CHRNA3-CHRNB4 (a gene cluster coding for the α5, α3, and β4 subunits of the nicotinic cholinergic receptor) for nicotine dependence and other smoking-related disorders (Lee et al., 2018).
The third point is that GWAS for substance use disorders have identified novel risk genes that have no known relationship either to neurotransmitter signaling (like the receptor genes mentioned above or the BDNF gene) or drug metabolism (like the alcohol dehydrogenase gene). Several of these novel genes code for cell adhesion molecules, cell surface proteins that were initially identified as helping cells bind to each other but were later shown to have multiple functions related both to fetal brain development and to neural plasticity later in life (Muskiewicz et al., 2018). The discovery that cell adhesion molecules are implicated in substance use disorders has shown that we must broaden our thinking about addiction genetics beyond the obvious places to look (i.e., make sure we are not limited by the “streetlight effect”).
The final point is that current evidence strongly indicates that the risk for engaging in drug misuse and acquiring a substance use disorder depends not only on the effects of individual genes but additionally on the influence of gene-by-gene interactions, gene-by-environment interactions, and epigenetic regulation of gene expression (a topic discussed later in the chapter). When taken together, these factors are hypothesized to yield a normal distribution of vulnerability for a substance use disorder, where the majority of the population lies within the center of the distribution, flanked by extremes of either high resilience or high vulnerability (Maldonado et al., 2021; Figure 2). Keep in mind that the distribution shown in the figure should not be construed as deterministic. Even if a person possesses a large number of risk factors that are pushing them toward substance misuse and the acquisition of a substance use disorder, we believe that their actions remain voluntary and they still have the choice to reduce their substance use or even abstain from use altogether.
Figure 2 Population model of vulnerability to acquiring a substance use disorder This model is based on the notion that the risk for a substance use disorder depends on multiple factors described in the text. When taken together, these factors are hypothesized to give rise to the normal distribution shown in the figure in which people with the greatest resilience are unaffected (i.e., will probably never acquire a substance use disorder), people who are the most vulnerable are highly likely to be affected, and the majority of people are in the middle of the distribution. The threshold illustrates the hypothetical point at which a person has accumulated sufficient risk to enter the group of affected individuals. Importantly, although the possession of risk alleles is fixed before birth, other factors such as epigenetic regulation of gene expression and the person’s environment can all change, thereby moving the person either to the left or to the right within the distribution. ∑ refers to the sum of all risk factors. (After Maldonado et al., 2021.)
Footnotes
* The term “streetlight effect” was derived a 1942 comic strip in which a policeman confronts a clearly inebriated man who is on his hands and knees under a lamppost searching for a missing coin. When asked, the man admits that he lost the coin two blocks away. The policeman asks, “Why, then, are you searching here?” “Because the light is better here,” replies the man.
** The word polymorphism stems from poly, meaning “many,” and morphe, meaning “form.”
References
Gelernter, J., Kranzler, H. R., Sherva, R., Almasy, L., Koesterer, R., Smith, A. H., Anton, R., et al. (2014). Genome-wide association study of alcohol dependence: significant findings in African and European-Americans including novel risk loci. Mol. Psychiatry, 19, 41–49.
Gluskin, B. S., and Mickey, B. J. (2016). Genetic variation and dopamine D2 receptor availability: A systematic review and meta-analysis of human in vivo molecular imaging studies. Transl. Psychiatry, 6, e747. doi: 10.1038/tp2016.22.
Haavik, J., Blau, N., and Thöny, B. (2008). Mutations in human monoamine-related neurotransmitter pathway genes. Hum. Mutat., 29, 891–902.
Hall, F. S., Chen, Y., and Resendiz-Gutierrez, F. (2020). The streetlight effect: Reappraising the study of addiction in light of the findings of genome-wide association studies. Brain Behav. Evol., 95(5), 230–246.
Lee, S. H., Ahn, W. Y., Seweryn, M., and Sadee, W. (2018). Combined genetic influence of the nicotinic receptor gene cluster CHRNA5/A3/B4 on nicotine dependence. BMC Genomics, 19(1), 826. doi: 10.1186/s12864-018-5219-3.
Lopez-Leon, S., González-Giraldo, Y., Wegman-Ostrosky, T., and Forero, D. A. (2021). Molecular genetics of substance use disorders: An umbrella review. Neurosci. Biobehav. Rev., 124, 358–369.
Maldonado, R., Calvé, P., García-Blanco, A., Domingo-Rodriguez, L., Senabre, E., and Martín-García, E. (2021). Genomics and epigenomics of addiction. Am. J. Med. Genet. B, Neuropsychiatr. Genet., 186, 128–139.
Muskiewicz, D. E., Uhl, G. R., and Hall, F. S. (2018). The role of cell adhesion molecule genes regulating neuroplasticity in addiction. Neural Plast., 2018, 9803764. doi: 10.1155/2018/9803764.
Yu, C., and McClellan, J. (2016). Genetics of substance use disorders. Child Adolesc. Psychiatr. Clin. N. Am., 25, 377–385.