Genetics and alcoholism PMC

Blood tests on subjects displaying this effect showed increased levels of acetaldehyde, a breakdown product of alcohol, which resulted in an uncomfortable sensation of warmth in the skin, palpitations and weakness. By the 1980s investigators traced the reaction to an enzyme involved in alcohol metabolism, aldehyde dehydrogenase, and eventually to the gene that encodes it, ALDH1. The Sober House enzyme breaks down acetaldehyde, but slight variations in the gene’s DNA code in these subjects caused the enzyme to work more slowly. When these individuals ingested alcohol, the acetaldehyde–which may be toxic in high doses–was building up in their bodies. AUD is a complex disorder, and likely hundreds if not thousands of genes contribute to its broad and varied phenotype.

Genetic susceptibility to optic neuropathy in patients with alcohol use disorder

• The idea is grounded in an assumption that endophenotypes can reveal the biological bases for a disorder better than behavioral symptoms because they represent a fundamental physical trait that is more closely tied to its source in a gene variant.
• Current power and sample size estimates for GWAS with effect sizes of 1.05–1.2 range from 30,000 – 120,000 (Owen et al., 2010; Schizophrenia Working Group of the Psychiatric Genomics, 2014).
• These factors further complicate the identification and confirmation of the role of any one gene.
• Trials that enrolled more men than women also showed more of a benefit, perhaps because men are at higher risk of stroke in general.

Furthermore, AUD frequently co-occurs with other psychiatric disorders, including mood and anxiety disorders (Regier et al., 1990), post-traumatic stress disorder (Sampson et al., 2015), and other substance use disorders (Kessler et al., 1997). These data highlight the heterogeneity https://fintedex.com/top-5-advantages-of-staying-in-a-sober-living-house/ of AUD and overlap with other psychiatric disorder that often also have strong genetic heritability estimates. Compared to other genetic predictors, the genomic pattern identified here was also a more sensitive predictor of having two or more substance use disorders at once.

Seeking the Connections: Alcoholism and Our Genes

Although much work remains to be done, researchers already have made substantial progress. New technological developments that allow for faster and more complete genotyping and sequencing will accelerate progress, as will technical developments allowing targeted overproduction or inactivation of genes in animal models. Genetic factors (i.e., variations in specific genes) account for a substantial portion of the risk for alcoholism. Researchers have used both case–control and family studies to identify genes related to alcoholism risk. In addition, different strategies such as candidate gene analyses and genome-wide association studies have been used. The strongest effects have been found for specific variants of genes that encode two enzymes involved in alcohol metabolism—alcohol dehydrogenase and aldehyde dehydrogenase.

Genetics and alcoholism

The oral cavity and esophagus aredirectly exposed to those levels, and the liver is exposed to high levels from theportal circulation. Thus it is not surprising that diseases of the GI system,including cirrhosis, pancreatitis, and cancers of the upper GI tract are affected byalcohol consumption80-86. Folate plays a key role in cell growth and building DNA, the complex molecule that forms our genetic blueprint. It is believed that folate may play a role in both suppressing some types of early cancer, as well as progressing established cancers if high doses of folic acid are used. Since these early observations about homocysteine, most but not all studies have linked high levels of homocysteine with a modest increase in risk of heart disease and stroke.

This article does not contain any studies with human or animal subjects performed by any of the authors. American Addiction Centers (AAC) is committed to delivering original, truthful, accurate, unbiased, and medically current information. Your socioeconomic status is made up of economic and societal factors such as your income, level of education, employment, location of residence, and available resources.

• If you’re concerned about someone who drinks too much, ask a professional experienced in alcohol treatment for advice on how to approach that person.
• Identifying these genes is difficult because each plays a small role in a much larger picture.
• These data highlight the heterogeneity of AUD and overlap with other psychiatric disorder that often also have strong genetic heritability estimates.
• 1The term “phenotype” refers to any observable characteristic or behavior of an individual.
• The DRD2 gene was the first candidate gene that showed promise of an association with alcoholism.

Factors influencing AUD

Several study designs—including case–control studies, population studies, and family studies—have been used to test whether a specific gene or gene variant affects risk for a disease (for more information, see the article by Foroud and Phillips, pp. 266–272). For example, it is much easier to collect individual cases (i.e., people with alcoholism) and control subjects (i.e., nonalcoholic people) or samples of the general population than it is to recruit family samples. On the other hand, family studies avoid the problem of incomplete ethnic/population matching1 that can confound case–control studies. Furthermore, family studies can be more powerful than case–control studies if different variants (i.e., alleles) of the same gene affect a given trait in different families, because multiple families can show an effect of that gene despite not sharing the same alleles.

As noted above, the functional ADH1B polymorphism isnot represented on GWAS platforms; GABA-receptor genes are often nominallysignificant but well below genome-wide significance in these studies. Thus, thegenes and SNPs found through GWAS have had little overlap with previous findingsbased on candidate genes/pathways and linkage analyses. There are several other genes that have been shown to contribute to the riskof alcohol dependence as well as key endophenotypes. In most cases, studiesrecruited families having multiple members with alcohol dependence; such familiesare likely to segregate variants that affect the risk of alcohol dependence. Themost common initial approach was linkage analysis, in which markers throughout thegenome were measured to identify chromosomal regions that appeared to segregate withdisease across many families.

Linkage studies are relatively robust to populationdifferences in allele frequencies (because they test within-family inheritance), andcan find a signal even if different variants in the same gene or region areresponsible for the risk in different families. The drawback to this approach isthat linkage studies find broad regions of the genome, often containing manyhundreds of genes. In many cases, the initial linkage studies were followed by moredetailed genetic analyses employing single nucleotide polymorphisms (SNPs) that weregenotyped at high density across the linked regions. Some of the genes identifiedthrough this approach have been replicated across a number of studies and appear tobe robust genetic findings. This finding suggests that variants of a gene or genes within this region reduced the risk of becoming alcoholic. ADH alleles are known to affect the risk for alcoholism; however, the known protective alleles occur at high frequency in Asian populations but are rare in the Caucasian population that makes up most of the COGA sample (Edenberg 2000).

Although GWAS are much more economical, the financial burden of whole genome sequencing could be outweighed by the value of genetic information obtained. Unlike GWAS, whole-genome sequencing is more likely to identify rare mutations, particularly recessive mutations, in exonic regions of the genome. These coding regions may have a strong impact on disease etiology and shed new light into possible pathophysiological mechanisms (Cirulli and Goldstein, 2010; Ng and Kirkness, 2010; Kato, 2015). Majority of genomic data for large alcohol consumption and AUD meta-analysis was either from UKBiobank or from Million Veterans Project.