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Genetic Vulnerability to Substance Use Disorders
Introduction
Genetic influences on addictive substance use vary across developmental stages of life. When an individual initiates substance use (i.e., experiments with drugs), environmental factors have a greater impact on his or her substance use patterns. Access to drugs, peer pressure, and socioeconomic factors are all crucial determinants of an individuals’ substance use patterns. Environmental factors are especially important in adolescents, as more of their activities are under monitoring of authority figures (parental). As an individual moves along the trajectory of continued heavy use, genetic influences become more prominent, and individual differences can be explained by the unique environmental conditions that interact with genetic factors. Unlike in other complex psychiatric disorders, substance use disorders (SUDs) require exposure to and ingestion of the substance as an obligatory environmental component for the development of SUDs and related phenotypes. Since the observation that the family history of substance use is a crucial component in developing SUDs, the exploration of specific genetic loci contributing to the moderate to high heritability has become the goal of many genetic analyses in the addiction genetics field. This chapter discusses basic concepts utilized in the quest to find genes for SUD vulnerability and the most replicated findings to date, with an emphasis on biological relevance and dynamic changes of gene expression when exposed to the abused substances.
Heritability ( h 2 ) of SUDs
Heritability Based on Family, Adoption, and Twin Genetic Studies
The first empirical evidence for a genetic basis of SUDs comes from family, adoption, and twin studies carried out in the pre-genomic era. The traditional family studies were observational studies that reported familial aggregation of addictive phenotypes. First-degree relatives of individuals with a SUD were reported to be at two times or more risk of developing a substance use problem compared with siblings of non–drug-dependent relatives. These observational studies were, however, not designed to examine whether the familial clustering of addictive phenotypes were due to the environment, genes, or their interaction. Adoption and twin study designs were employed to tease apart genetic from environmental effects on addiction vulnerability.
Adoption study design compares the similarity in SUDs or patterns between adopted children and their biological parents with the adopted children and their adoptive parents or between adopted sibling pairs and biological sibling pairs. Several well-powered studies conducted in the United States and Europe in the 1970s and 1980s showed that the risk for developing an alcohol use disorder (AUD) was much greater in the offspring of alcoholics, even when children were raised by nonalcoholic foster parents. The risk of developing an AUD was shown to be about four times greater in sons of alcoholics compared to sons of nonalcoholics adopted by nonalcoholic foster parents. Furthermore, rearing by an alcoholic parent had a greater influence on alcohol abuse but not on alcohol dependence in the offspring, which suggested a strong genetic influence on dependence as a phenotype. Similarly, the risk for nicotine dependence also was reported to be greater between biological siblings but not the adopted siblings, and the sons and their nicotine-dependent biological mothers. The age at adoption is an important confounder of genetic effects in adoption studies, as early environmental exposures can overestimate genetic influences.
Twin studies compare the agreement in the behavior between monozygotic (MZ) or identical twins who are genetically identical, and dizygotic (DZ) or fraternal twins who share on average 50% of their genetic makeup. The term concordant is used if both twins engage in the same behavior (e.g., they both drink heavily). A higher rate of concordance in MZ than DZ twins suggest that genetics likely contributes to addiction vulnerability in addition to environmental factors. Heritability of a phenotype is estimated statistically by modeling the percentage of variation in the phenotype that is explained by genes (heritability), experiences shared by family members (shared environment), and experiences unique to the individual (nonshared environment). Twin studies assume that both twins are exposed to equal environmental influences that affect their substance use behavior. If MZ twins are exposed to more similar environments than DZ twins are, twin studies provide inflated estimates of genetic influences on the phenotype.
The heritability estimates from family, adoption, and twin studies for addictive substances range from 40% to 70%, with lowest rates for hallucinogens and highest for cocaine addiction. In addition to addiction, substance initiation and heaviness of use also are heritable phenotypes. Some studies have suggested gender and race differences in heritability of SUDs, but the replicability of these findings are not consistent across studies. Notably, adoption and early twin studies are suggestive of genetic effects but do not imply any specific genetic loci for addiction vulnerability. Furthermore, data from these large twin registries also can be used to compare how one twin’s dependence on a substance influences his/her co-twin becoming dependent on a different class of substances. In addition, there is only a modest amount of family data available to compare concordance in first- versus second-degree relatives. The existing evidence does not, however, support less concordance in second-degree relatives than we would anticipate based on the observed concordance in first-degree relatives.
SNP-Based Heritability ( h 2 SNP )
Estimation of heritability in traditional family, adoption, and twin studies relied on data from closely related individuals. The recent developments in the molecular genetics field allow estimation of heritability in unrelated individuals by using the variance explained by all single nucleotide polymorphisms (SNPs) used in genome-wide association (GWA) array—defined as h 2 SNP . The definition for h 2 SNP is now extended to include variance explained by any set of SNPs, whether they are a set of candidate SNPs or all SNPs from whole-genome sequencing (WGS). Generally, heritability estimates captured by common SNPs (frequency >1%) for many substances are much lower than their initial estimates from classical genetic studies. A comparison of published twin study–based and SNP-based estimates of heritability for the five most widely used addictive substances in the United States (excluding prescription drugs) and related phenotypes are presented in Table 12.1 . Possible reasons for the discrepancies between heritability estimates derived from twin and SNP-based methodologies are discussed further discussed in the section Missing Heritability.
Table 12.1
A Comparison of SNP-Based ( h 2 SNP ) and Twin Study–Based Heritability ( h 2 ) Rates.
| SUDs and Related Phenotypes | h 2 [references] | h 2 SNP [references] |
|---|---|---|
| Alcohol use disorders Problem drinking Intoxication frequency |
40–60 43–50 50 |
33–40 13–26 |
| Nicotine use disorders Number of cigarettes per day (CPD) Smoking initiation Tolerance Withdrawal |
27–60 45–86 50–75 73 9–53 |
38 0.68 15 29 67 |
| Cannabis use disorders Cannabis-use initiation |
51–72 40–59 |
21 6–25 |
| Opiate use disorders Opiate-use initiation |
23–54 58 |
|
| Cocaine use disorders Cocaine-use initiation |
42–79 14 |
>80 a , |
SNP, Single nucleotide polymorphisms; SUDs, substance use disorders.
All heritability estimates are given as percentiles and represent estimates in both genders.
a Heritability for a continuous variable created by summing up the number of positive responses to the seven DSM-IV cocaine dependence criteria with equal weights.
Genetic Architecture of Addiction Vulnerability
Based on molecular genetic studies from the past few decades, we now know that the genetic architecture of vulnerability to developing an SUD using legal or illegal addictive substances in the population is polygenic and is influenced by variants in individual genes, and that each contributes modest amounts to this overall phenotypic variability. Most of the known genetic variations increase the risk for development, progression, and severity of SUDs. Although a few of the risk or protective genetic influences are specific to one class of substance, most of them influence neurobiological mechanisms common to SUDs regardless of the class of abused substance.
Mapping Genetic Loci Influencing Vulnerability to SUDs
Mapping of specific genetic loci that correlate with phenotypes/traits began with mapping DNA markers to chromosomes in affected family members of extensive pedigrees. This locus-driven linkage mapping approach identified chromosomal regions or loci containing many genes that cosegregated with several SUDs.
The first linkage analysis in AUD was carried out by the Collaborative Study on the Genomics of Alcoholism (COGA). This multisite study initially enrolled 10 nuclear and multigenerational families with 987 individuals. Findings of the first genome-wide linkage scan identified chromosomes 1 and 7 as conferring risk and chromosome 2 as being suggestive of protective effects for developing Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Alcohol Dependence. A follow-up study confirmed these loci and provided evidence of additional linkage to chromosome 3. The linkage locus on chromosome 4p harbors the GABRA2 / B1 cluster and was detected in early linkage analyses of both Southwestern Indian and Caucasian populations. Similarly, strong linkage signals are detected for nicotine addiction on chromosome 9q22 that harbors GABRB1 . Many reviews are available on findings from other well-powered linkage analyses to identify chromosomal locations associated with vulnerability to develop nicotine, opioid, cocaine, and cannabis use disorders. a
a References 1, 22, 31, 43, 68, 73, 86.
The next stage of genetic mapping is highlighted by the search to identify specific alleles associated with SUDs. Genetic association studies are a form of linkage analyses based on alleles, rather than loci that consist of multiple genes. Four different association analyses approaches have been used to identify specific alleles: (1) fine mapping of chromosomal loci from linkage analyses; (2) hypothesis-driven candidate gene approaches; (3) GWAs using SNP arrays; and (4) GWAs based on DNA sequencing. A limited number of large studies were performed with samples collected from individuals with SUDs and their family members. The majority of association studies were performed with unrelated individuals from racially diverse populations with and without a diagnosis of SUDs. The statistical power to detect an association between a specific genetic variant affecting a complex phenotype such as SUD depends on: (1) the effect size of a genetic variant (mutation) affecting some aspect of SUD; (2) how many times the phenotype-affecting variant segregates in the studied population; (3) the number of such phenotype-affecting variants detected in the studied population; (4) the studied sample size; (5) heterogeneity of the trait being studied; and (6) coverage of the genetic panel used in the genome-wide association studies (GWAS) or candidate-based analyses to screen for variants.
Candidate Gene Analyses
Hypothesis-driven small-to-medium scale candidate gene analyses have been reported on a wide range of SUD-related phenotypes and analyzed in populations of different racial and geographic origins. Candidate gene analyses have now covered genes within almost all of the neurotransmitter systems, including γ-aminobutyric acid (GABA), serotonin, dopamine, and glutamate. Other candidate analyses have looked at substance-specific metabolic pathways, and neuroimmune, neuroendocrine, and cell-adhesion pathways.
Genome-Wide Association Results for Addiction
All analyses exploring genotype-phenotype associations with genetic data covering the entire genome can essentially be termed genome-wide association studies (or GWAS), irrespective of whether the acquisition of genotypes was based on sequencing or array-based technology. Application of GWAS in SUDs is now at least 10 years old. GWAS was initially designed as an experimental method to identify SNPs spanning the entire genome that contribute to complex disorders, especially those with polygenic genetic bases. That is, those derived from effects at many gene loci, each with modest effects through their interactions with environmental elements. To date, nearly 10,000 robust SNP-trait associations have been discovered by GWAS for complex traits across all areas of biomedical sciences that reach genome-wide statistical significance level of 5x10 -8 .
SNP Array-Based GWAS
Early GWAS in SUD focused mainly on SNP associations with the DSM-IV diagnosis of substance dependence as a trait. These early array-based GWAS compared allelic differences for SNPs in those who did not meet or who met any three of the seven DSM-IV criteria for substance dependence. The lack of precision of the phenotype is now being viewed as one of the reasons that led to disappointing results in the early GWAS era in addiction research. As the field of addiction genetics evolved, focus has shifted more toward quantitative and qualitative intermediate phenotypes or endophenotypes that constitute the broader phenotype of dependence. These secondary endophenotypes include the age at initiation of substance use, and the degree of substance use such as cigarettes per day or standard drinks per day. Individual studies have analyzed arrays consisting of about 2 million SNPs in Asian, African, and European populations of up to 20,000.
GWAS, as well as smaller candidate genotyping analyses, to date rely on the correlation structure, that is, linkage disequilibrium (LD) estimates that exist between variants in the human genome that process a causal effect on the trait and the genotyped variants in an experimental panel. This is one of the reasons that the commercially available SNP arrays are not yet powerful enough to capture the effects of causal rare variants. Apart from these conceptual issues, relatively smaller sample sizes to detect rare genetic effects and other technical limitations of SNP arrays have contributed to a lack of variant discovery in these studies. There are a number of excellent articles that discuss limitations of early GWAS studies in depth. A commonly used strategy to gain some of the missed information from SNP array genotyping is to statistically infer (i.e., impute) the ungenotyped variants from haplotypes observed in a fully sequenced reference panel. Reference panels for European, African, and Asian ancestry human genomes are publicly available through large-scale population genome sequencing projects such as 1000 Genomes Project, International HapMap Project, and the Personal Genome Project Korea ( http://opengenome.net/ ).
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