ReviewRecent Efforts to Dissect the Genetic Basis of Alcohol Use and Abuse
Section snippets
Design Strategies for Enhancing AUD Genetic Discovery
For decades, candidate gene studies were used to determine the contribution of specific genes that increase risk for AUD. Candidate gene studies tended to focus on genes that influenced pharmacokinetic and pharmacodynamic (e.g., dopaminergic, glutamatergic, and opioid signaling systems) factors. Larger genetic studies have generally not replicated the findings from candidate gene studies (11). An exception is the genes encoding ethanol metabolizing enzymes, particularly alcohol dehydrogenase (
Recent Discoveries on the Molecular Genetics of Alcohol Use Behaviors
Table 1 summarizes the most recent GWASs of alcohol use behaviors (N = 16). Figure 2 provides an overview of the chronology of these studies. Figure 3 shows that the list of genes identified by these studies is highly heterogeneous. These data suggest incomplete genetic overlap between measures of alcohol use behaviors (Figure 4), although ascertainment bias and limited power (see Figure 5) are likely to be additional contributing factors.
The 4q23 region, which contains the genes for several
Polygenic Methods Generate Hypotheses to Test Across Alcohol Use Behaviors
One successful application of GWASs has been their use for assigning polygenic risk scores (PRSs), which provide estimates of an individual’s genetic risk of developing a given disorder. Reassuringly, PRSs for alcohol use behaviors predict equivalent phenotypes in independent cohorts, such as alcohol consumption (51), AD (13), and AUD symptoms (52). E.C. Johnson, Ph.D., et al. (unpublished data, 2019) recently identified that compared with PRSs for alcohol consumption (AUDIT-C), PRSs for
Alcohol Consumption and Misuse Show a Distinct Genetic Architecture
Before the era of large-scale genomic research, twin and family-based studies identified a high degree of genetic overlap between the genetic risk for AUD and psychopathology by modeling correlations among family members, such as Kendler et al. (58). With the recent development of linkage disequilibrium score regression, it is now possible to estimate the genetic correlations between specific alcohol use behaviors (Figures 1 and 4) and a plethora of psychiatric, health, and educational outcomes
Limitations and Future Directions
Before the availability of large population studies and collaborative consortia efforts, few genes were reliably associated with AUD. The use of intermediate traits or endophenotypes (e.g., alcohol consumption as an intermediate phenotype for AUD) has become increasingly common, and multiple new loci have been associated with alcohol use behaviors. Using intermediate phenotypes also facilitates translational research, we can mimic aspects of human alcohol use using animal models, including
Conclusions
AUD is a complex, heterogeneous disorder encompassing a variety of behavioral, psychological, and physiological traits with a complex longitudinal structure, thus posing an enormous challenge for genetic analysis. Instead, AUD can be fractionated into dimensions or symptoms. Several recent GWASs have used this approach, and it is now common to study quantitative measures, including alcohol consumption and aspects of disordered drinking, in large population samples. As a result, GWASs of alcohol
Acknowledgments and Disclosures
This work was supported by the Frontiers of Innovation Scholars Program (Grant No. 3-P3029 [to SS-R]), the Interdisciplinary Research Fellowship in NeuroAIDS (Grant No. MH081482 [to SS-R]), a pilot award from the National Institutes of Health (NIH) (Grant No. DA037844 [to SS-R]) and a 2018 NARSAD Young Investigator Grant (Grant No. 27676 [to SS-R]). This work was also supported by funds from the California Tobacco-Related Disease Research Program (TRDRP) (Grant No. 28IR-0070 and T29KT-0526 [to
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2022, Metabolism: Clinical and ExperimentalCitation Excerpt :In addition to SNPs at the FGF21 locus, SNPs in the GCKR gene was strongly associated with circulating FGF21 in the present GWAS. The GCKR locus is widely reported to have pleiotropic effects on metabolic phenotypes, such as triglycerides [49–51], but primarily fasting glucose [51], and type 2 diabetes [32], and is also strongly associated with alcohol [24,52] and macronutrient [53] intake. It is plausible that genetic variations in GCKR do not directly affect FGF21 concentrations but is otherwise associated with other traits, such as alcohol consumption and nutritional factors, and most important genetic forms of diabetes and hyperglycemia.
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