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September 20, 2024PRS can also be used to test specific hypotheses; for example, PRS can be used to measure how environmental, demographic, and genetic factors interact with one another. Can we identify biomarkers that would inform the transition from normative alcohol use to excessive use and dependence? For instance, the alcohol metabolizing genetic effects on alcohol use appeared to be more influential in later years of college than in earlier years (55, 56), revealing that the nature and magnitude of genetic effects vary across development. Different from the previous candidate-gene studies, GWAS is a hypothesis-free method that scans genome-wide common variants using microarray genotyping or sequencing to identify associations with study traits (Figure 1) (42, 43). Substantial progress has been made in the wave of genetic studies of AUD using GWAS (Figure 2 and Table 1). In summary, omics studies are improving our understanding of the biological mechanisms between genetic variation and phenotypes, and environmental effects on biology.
Advances in research on medications for the treatment of alcohol use disorders: A review
In this context, the application of ketamine and its enantiomer, esketamine, offers a potential treatment pathway not only for treatment-resistant depression but also brings new hope for patients with AUD. MMF and ACH performed the DNA and RNA extraction and SHH, PH, FD, and MMN were responsible for generating genome-wide methylation data. DNA was extracted from bulk brain tissue using the DNeasy extraction kit from Qiagen (Qiagen, Hilden, Germany). For the microarray analysis, the samples were randomized based on AUD case/control status and sex, and pipetted on processing plates.
These results demonstrate that the genetic architecture of alcohol consumption only partially overlaps with the genetics of clinically defined AUD. We discuss the limitations of using quantitative measures of alcohol use as proxy measures for AUD, and outline how future studies will require careful phenotype harmonization to properly capture the genetic liability to AUD. (c) AUD is a highly polygenic disorder, with hundreds of variants at least contributing to the risk (80, 95). Unlike other traits or behaviors that can be measured directly and assessed in large populations or biobanks — for example, GWAS of height (96), educational attainment (97), and alcohol consumption (98) have been conducted in 3~5 million participants — clinical diagnosis of AUD in large cohorts is still lagging. Similar to point (a), increasing sample size and incorporating multiple ancestries could improve the power and resolution of causal variant fine-mapping (80). Besides the well-known functional coding variants in the alcohol metabolic genes, most variants identified through large GWAS have small to very small effects on the risk of AUD, reducing the yield of the extensive effort of following functional studies on individual variants.
IND-Enabling Development of Novel Compounds
The lack of GABA adaptation could be due to the absence of GABAergic signaling in the excitatory-enriched neural cultures, since three-week exposure to chronic ethanol significantly increased transcription levels of GABRA1 and GABRG2 in both AD and CTL groups. GABRD only showed a modest increase (24%) for the AD but not the CTL group (Lieberman et al., 2018), which is consistent with GABRD suggested as an AUD-candidate gene (Rodd et al., 2007). The comparison of differential effects of ethanol on human cells derived from healthy and alcohol dependent subjects illustrates how to use hiPSC-derived cells to study AUDs. Two large-scale twin studies of illicit drugs showed that most genetic risk is common across the abuse of or dependence on different psychoactive substances6,7.
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- Different from the previous candidate-gene studies, GWAS is a hypothesis-free method that scans genome-wide common variants using microarray genotyping or sequencing to identify associations with study traits (Figure 1) (42, 43).
- However, these analyses require very large sample sizes for adequate power (hundreds of thousands to millions of individuals), parallel to GWAS, on which genotypic, phenotypic, and environmental data are available, making this an unlikely option for intervention trials in the near future.
- Two-sample MR approaches, which are based exclusively on genome-wide association statistics, reduced the limitations due to the use of individual-level data, allowing for a more extensive range of studies109.
Mazumdar and Eberhart addressed the genetic factors influencing susceptibility to ethanol-induced birth defects, focusing on the role of nicotinamide nucleotide transhydrogenase (Nnt) in mitigating oxidative stress. This study, conducted in zebrafish embryos, demonstrated that ethanol exposure leads to increased apoptosis and craniofacial malformations in Nnt mutants due to elevated reactive oxygen species. To further explore the mechanisms underlying PAE-induced oxidative stress, Darbinian et al. examined mitochondrial DNA (mtDNA) in fetal brain tissues from PAE-exposed rats and humans, as well as fetal brain-derived exosomes (FB-Es) obtained from maternal blood.
Interventions
E1 reflects the common factor for the unique environmental effects that affects risk for dependence across all of the psychoactive substances examined. The top part of the figure depicts genetic factors that influence drug dependence and the bottom part reflects environmental factors. The numbers on the paths represent standardized loadings and thus need to be squared to reflect the proportion of variance in the observed SUD accounted for by the factor. A wide range of behavioral and psychological treatments are available for alcohol use disorder, and many treatments are equally effective in supporting abstinence or drinking reduction goals (71–74). Twelve-step facilitation, which was designed specifically to connect individuals with mutual support groups, has also been shown to be effective (75). In addition, harm reduction treatments, including guided self-control training and controlled drinking interventions, have been successful in supporting drinking reduction goals (70).
For example, a microfluidic local perfusion chamber has been developed to manipulate distinct synaptic regions, directing synapse formation in distinct parallel rows through microgrooves connecting two distinct neuronal populations (Taylor et al., 2010). HiPSC-derived neurons are compatible with micropatterning for long term growth and circuit organization. Neurons adhere to regularly spaced adherent surfaces with neurites crossing cell-repellant surfaces to form connections (Burbulla et al., 2016). This system facilitates long-term studies of mitochondrial dynamics, axonal transport, and dynamic network formation (Burbulla et al., 2016).
Data preprocessing, quality control, and filtering
- Xu et al. investigated the potential therapeutic benefits of choline in mitigating ethanol-induced cell death in the developing neural tube using BXD strains of mice, known to vary in their sensitivity to ethanol’s teratogenic effects (Downing et al., 2012).
- For example, the frequency of AUD in the UKB is lower than the population average 7% (19), indicating that certain population studies may be underpowered to detect genetic effects specific to dependence (20).
- The double-headed arrow connecting the illicit and licit substance genetic factors represents the genetic correlation between these factors.
- However, there is no published evidence of severe liver toxicity at the lower FDA-approved dosage of naltrexone for alcohol use disorder (50 mg per day).
- In 2009, the first GWAS of AUD was conducted in a German sample comprising 487 cases of AUD and 1,358 population-based controls; no variants reached the genome-wide significant (GWS) threshold (44).
Alternatively, those with major depression might have a higher risk for alcohol dependence because they are more likely to seek the anxiolytic effects of alcohol, as those with conduct disorder are more likely to select deviant peer groups who provide them with alcohol. Many different phenotypes are now commonly assessed, and these often provide substantially stronger evidence for the involvement of particular genes in alcohol dependence risk. How do the genetic risk factors for SUDs fit into the broader scheme of genetic risk for other psychiatric disorders?
As recently shown181, alcohol consumption, tobacco smoking, and phenotyping of other traits are subject to misreports and longitudinal changes, causing biases in gene discovery and follow-up analyses. Appropiate phenotyping strategies are needed to avoid this possible confounder, especially with respect to self-reported frequency and quantity of substance use. Here, we identified novel associations of differential DNA-methylation between AUD cases and controls, which are prominent in alcohol-related pathways and diseases linked with AUD. Human postmortem brain tissue is difficult to obtain and very few brain banks focus on substance use disorders. Three preclinical studies delved deeper into the cellular level effects of ethanol exposure during brain development, and investigated a potential therapeutic. Cealie et al. used a mouse-model of human third-trimester exposure to specifically focus on microglia dynamics, morphology and interactions with Purkinje cells within the cerebellum during offspring adolescence.
The inherently different function of rodent and human BBB (Aday et al., 2016) accounts for some of the failed CNS therapeutic trials (Alavijeh et al., 2005) as well as being implicated in neurodegenerative and psychiatric diseases (Desai et al., 2007; Saito and Ihara, 2014). These more complex hiPSC-derived cultures provide an important approach for modeling disease specific circuitry and drug delivery. In other risk pathways, it remains unclear whether common comorbidities with major depression, conduct disorder and antisocial personality disorder are due to direct etiological overlap between the conditions.
Efforts are underway to explore how the currently recommended endpoints designated by the FDA can be expanded to include alternative measures focused on drinking reduction. For example, current analyses using NIAAA-supported epidemiological and multisite clinical trial data examine the clinical relevance of different levels of drinking by correlating them with acute and long-term alcohol-related consequences. Hopefully, analyses of the “validated” drinking endpoints would consistently lead to a meaningful reduction in consequences. Secondary analyses of NIAAA-supported clinical trials are being conducted, comparing the sensitivity of these alternative endpoints to more traditional ones in terms of their ability to detect the effect of medication relative to placebo. Ultimately, the adoption of a new endpoint will require that it is both clinically meaningful and sensitive to the effect of medication. By introducing the chlorine atom at the 6-position, the high electronegativity and smaller size of the chlorine atom help to optimize the electron distribution and steric structure of the molecule, which enhances the binding efficiency with PDE4.
Lastly, variability in hiPSC derived neurons even amongst control subjects can be a concern for disease modeling (Choi et al., 2015; Kilpinen et al., 2017; Kyttälä et al., 2016). For example, a recent study exhaustively characterized differentiation capacity, cellular morphology, and copy number recent advances in genetic studies of alcohol use disorders pmc alterations through genome wide profiling of 711 hiPSC lines from 301 healthy individuals revealing that most variation results from differences between individuals (Kilpinen et al., 2017). Designing a hiPSC study with sufficient statistical power would require large samples sizes, which is impractical both in terms of cost and lack of techniques for high-throughput functional analyses. However, implementation of genetically-engineered hiPSCs can provide a reasonable solution to this limitation (see above). Lieberman and colleagues used hiPSCs to investigate the biological effects of alcohol on human brain cells (Lieberman et al., 2012).
In conclusion, epigenetic studies have provided limited insights into the molecular mechanisms underlying AUD. Considering the known genetic and etiologic complexity of AUD risk, and the contributions of both genes and environment, larger samples will be required to draw durable conclusions about AUD epigenetics. The integration of DNA methylation, histone modifications, and noncoding RNAs into our understanding of AUD pathogenesis holds promise for identifying novel therapeutic targets and developing personalized interventions. As technology advances and research methodologies are refined, the field of epigenetics is expected to profoundly contribute to unraveling the complexities of AUD. (g) Another profound gap is that the current predictive performance of PRS for AUD based on GWAS common variants — i.e., using genetic variation to predict risk in genotyped individuals — is strongly statistically significant but numerically still weak and has not yet entered the range of clinical utility. Despite the increase in sample size, the SNP-based heritability (h2) by GWAS is low (h2 ranges from 5.6% to 12.7% with liability-scale h2 ranging from 8.9% to 16.2%, refs. 64, 72, 75, 78, 80) compared with the total heritability but comparable to what is observed for many other genetically complex traits.