The genes with the clearest contribution to the risk for alcoholism andalcohol consumption are alcohol dehydrogenase 1B (ADH1B) andaldehyde dehydrogenase 2 (ALDH2; mitochondrial aldehydedehydrogenase), two genes central to the metabolism of alcohol (Figure 1)20. Alcohol is metabolized primarily in the liver, although thereis some metabolism in the upper GI tract and stomach. The first step in ethanolmetabolism is oxidation to acetaldehyde, catalyzed primarily by ADHs; there are 7closely related ADHs clustered on chromosome 4 (reviewed in20).

• Is there any scientific evidence that your genes may predispose you to have an alcohol dependency if your parents or grandparents did?
• Therefore, many genetic studies of alcoholism also concentrated on nonclinical phenotypes, such as alcohol consumption and Alcohol Use Disorders Identification Test (AUDIT)17–19, from large population based cohorts.
• The Microsetta Initiative studies human microbiomes, analyzing diverse data to reveal links between microbes, diet, and health for improved public health.
• This Curated Collection includes ARCR articles that explore the role of genetic influences on the risk for alcohol use disorder, genetic susceptibility to alcohol-related harm to the body, and different risk vulnerabilities across subpopulations.

Is There an Alcohol Addiction Gene?

AUD doesn’t form because of a single gene, nor are genetics the only reason why someone develops an alcohol use disorder. Single gene studies in mice have implicated more than 70 candidate genes in alcohol-related phenotypes (Crabbe et al. 2006). Additional insights in the metabolism of alcohol come from studies on the fruit fly Drosophila melanogaster. Fruit flies encounter ethanol in is alcoholism inherited their natural habitat, since larvae feed on fermented food sources, which provide substrates for lipid synthesis (Geer et al. 1985).

Genetic predisposition to alcohol use disorder

An item-level study of the AUDIT questionnaire confirmed a two-factor structure at the genetic level, underscoring unique genetic influences on alcohol consumption and alcohol-related problems14 and noted that the genetics of drinking frequency were confounded by socioeconomic status. A similar pattern—genetic distinctions between substance use disorder (SUD) versus nondependent use—has also been observed for cannabis use disorder and cannabis use15. Furthermore, aggregating across multiple SUDs suggests that problematic and disordered substance use has a unique genetic architecture that, while shared across SUDs, does not overlap fully with nondependent substance use per se16. Several of the candidate risk genes for alcohol dependence identified in these studies contribute to alcohol-related behaviors in animal models.

Health Costs of Alcohol Abuse

COGA is one of the few family‐based genetic projects with a significant number of African Americans, who are greatly underrepresented in such studies, particularly those with family‐based designs. A large number of studies aimed at identifying genes that contribute to variation in alcohol-related phenotypes have relied on gene mapping strategies. At least 24 quantitative trait loci (QTL) have been identified in the mouse genome (Crabbe et al. 1999) and four genomic regions were found in rat (Saba et al. 2011). Meta-analysis of QTL mapping across eight different studies on murine alcohol consumption provided strong support for four QTL regions located on mouse chromosomes 2, 3, 4 and 9 (Belknap and Atkins 2001). However, evidence that links candidate genes within QTL regions causally to the phenotype remains difficult to obtain. While there is overlap between alcohol use disorder and alcohol consumption, the researchers did further analysis and found a “distinct genetic architecture” differentiating alcohol abuse from alcohol consumption.

• These findings are important for researchers because of similar overlap with other addictive behavior, said lead researcher Prof. Abraham Palmer.
• Advances in our understanding of the genetic etiology of AUD will continue to depend on more detailed, family‐based designs in data‐rich samples like COGA, as well as large‐scale, collaborative meta‐analyses that incorporate summary data from COGA alongside many other cohorts.
• While many studies have been done, and experts agree that there is a hereditary connection, genetics is not the only factor, and we don’t quite know the full impact it has on alcoholism.
• AUD and AUDIT–P index aspects of excessive alcohol intake and higher risk of which correlate with genetic liability to psychiatric and psychosocial factors (for example, higher risk for major depressive disorder and lower educational attainment (EA)).
• A large sample size and number of SNPs are required for accurate estimation, which explains the nonrobust estimates for EAS and SAS samples.
• In the study of complex disorders, it has become apparent that quitelarge sample sizes are critical if robust association results are to beidentified which replicate across studies.

In total, 80,028, 36,330, 10,150, 701 and 107 cases were included in EUR, AFR, LA, EAS and SAS, respectively, and 368,113, 79,100, 28,812, 6,254 and 389 controls were included in EUR, AFR, LA, EAS and SAS, respectively. BOLT-LMM65 was used to correct for relatedness, with age, sex and the first ten PCs as covariates. This article does not contain any studies with human or animal subjects performed by any of the authors. Additionally, about 1.7% of adolescents ages 12 to 17 were reported as having alcohol use disorder in 2019. According to the National Institute on Alcohol Abuse and Alcoholism (NIAAA), 5.6% of adults in the United States were living with alcohol use disorder in 2019.

• In children aged 9 or 10 years without any experience of substance use, these genes correlated with parental substance use and externalizing behavior.
• Among the behavioral traits parents can pass on to their children is a predisposition toward alcohol abuse and addiction.
• With the advent of microarrays that can measure hundreds of thousands tomillions of single nucleotide polymorphisms (SNPs) across the genome,genome-wide association studies (GWAS) have provided a relatively unbiased wayto identify specific genes that contribute to a phenotype.
• It was supported by the National Institute on Drug Abuse (NIDA), the National Institute on Alcohol Abuse and Alcoholism (NIAAA), the National Institute of Mental Health (NIMH), the Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Institute on Aging.

Under the model of PAU as substantially a brain disorder, we did fine mapping while prioritizing predictive models using a brain tissue-prioritized approach. With increasing number of AFR GWAS now published, mainly from MVP, we were able to estimate genetic correlations between AUD and a limited set of traits in AFR. As in EUR, AUD in AFR was genetically correlated with substance use traits including OUD, smoking trajectory (that identifies groups of individuals that follow a similar progression of smoking behavior), and maximum habitual alcohol intake. PheWAS of PRS in AFR from PsycheMERGE and Yale–Penn confirmed that AUD is genetically correlated with substance use traits. The lack of a wider set of phenotypes for comparison by ancestry is a continuing limitation.

Vanderbilt University Medical Center’s Biobank

With the advent of microarrays that can measure hundreds of thousands tomillions of single nucleotide polymorphisms (SNPs) across the genome,genome-wide association studies (GWAS) have provided a relatively unbiased wayto identify specific genes that contribute to a phenotype. To date, GWAS havefocused on common variants, with allele frequencies of 5% or higher.Most GWAS are case-control studies or studies of quantitative traits inunrelated subjects, but family-based GWAS provide another approach. GWAS arebeginning to yield robust findings, although the experience in many diseases isthat very large numbers of subjects will be needed.

Differential gene expression linked to alcohol use disorder, offering new treatment possibilities

Nevertheless, different studies reveal different aspects of the genetic underpinnings of the physiological and behavioral effects of ethanol, while underscoring the underlying genomic complexity of the genotype-phenotype relationship. Combining and integrating information from experimentally tractable model systems with human genetic studies provides a powerful strategy to disentangle the genomic elements that contribute to alcohol-related phenotypes. The rate at which alcohol is metabolized and the nature and fate of its degradation products are important factors that determine its physiological effects.

• Fruit flies encounter ethanol in their natural habitat, since larvae feed on fermented food sources, which provide substrates for lipid synthesis (Geer et al. 1985).
• Factors like your environment and ability to handle situations triggering dependency are just as important as genetics.
• Conditions under which flies show preferential intake of ethanol have been reported and it has been proposed that such conditions could mimic aspects of addiction (Devineni and Heberlein 2009; Kaun et al. 2012; Peru y Colón de Portugal et al. 2013).
• Family, twin, and adoption studies have shown that alcoholism definitely has a genetic component.

Studies arerevealing other genes in which variants impact risk for alcoholism or relatedtraits, including GABRA2, CHRM2,KCNJ6, and AUTS2. As larger samples areassembled and more variants analyzed, a much fuller picture of the many genesand pathways that impact risk will be discovered. We report here the largest multi-ancestry GWAS for PAU so far, comprising over 1 million individuals and including 165,952 AUD/AD cases. The inclusion of multiple ancestries both broadened the findings and demonstrated that the genetic architecture of PAU is substantially shared across these populations. Cross-ancestry fine mapping improved the identification of potential causal variants, and cross-ancestry PRS analysis was a better predictor of alcohol-related traits in an independent https://ecosoberhouse.com/ sample than single-ancestry PRS. We prioritized multiple genes with convergent evidence linking association to PAU with gene expression and chromatin interaction in the brain, and we investigated genetic correlations with multiple traits in AFR, also not possible previously.