As it turns out, there is no “alcoholic” gene in the human genome, nor is there an absolute “AUD-causing” environment or situation. Alcoholism has a substantial impact on both mental and physical health and can present different features among affected individuals. Due to this, the mechanisms and possible causes of alcoholism cannot be as easily identified as diseases such as hemophilia, which presents clear physical symptoms.

For example, results from several twin studies 9-13 have detected consistently a considerable overlap in the genetic liability to alcoholism and nicotine dependence, particularly in individuals who drink or smoke heavily. Rates of smoking are declining; however, studies reported during the past 20 years have indicated that as many as 80% of alcohol-dependent individuals are heavy smokers 14,15. Approximately 50% of the genetic vulnerability to nicotine dependence is shared with alcoholism, whereas 15% of the genetic vulnerability to alcoholism is shared with nicotine dependence 9. 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.

Qualified investigators can access freely available GWAS datasets via the database of Genotypes and Phenotypes (dbGaP) 83 and several studies have used this resource for replication samples. For individual studies, Heath and colleagues (in press) estimated the proportions of variability in alcoholism liability explained by genetic and family environmental influences. The only recent United States adoption studies on alcoholism for which results have been published are those conducted by Cadoret in Iowa. The fact that only one investigator has been able to conduct such studies may reflect the high degree of tenacity required to overcome State privacy regulations restricting access to information about the biological families of adoptees. In his earliest studies, Cadoret studied samples from Lutheran Social Services (LSS) (Cadoret et al. 1985; Cadoret 1994) and Iowa Children and Family Services (CFS) (Cadoret 1994; Cadoret et al. 1987).

The Collaborative Study on the Genetics of Alcoholism: An Update

Ethanol is metabolized largely in the liver by alcohol dehydrogenases (ADH) to the toxic acetaldehyde which is then converted to acetate by aldehyde dehydrogenases (ALDH), primarily by the mitochondrial enzyme ALDH2. The class I ADH enzymes encoded by the ADH1A, ADH1B and ADH1C genes contribute about 70% of the total ethanol oxidizing capacity, and the class II enzyme encoded by ADH4 contributes about 30% 19. However, results reached significance if the diagnostic criteria were either narrowed (to require withdrawal symptoms) or broadened (to include problem drinking). This article focuses on studies that have systematically used samples ascertained from birth or adoption records. Unfortunately, however, this review excludes several important studies (Gurling et al. 1984; Pickens et al. 1991; Caldwell and Gottesman 1991; McGue et al. 1992; Heath et al. 1994), because various technical issues place those studies beyond the scope of this review (for further details, see Heath et al. in press).

Understanding more about how genes and environment act, co-act, and interact to determine differences in alcoholism risk remains a key goal of ongoing twin and adoption studies. Meanwhile, the evidence from twin and adoption studies has provided researchers with the impetus to investigate other methods of genetic alcoholism research, such as molecular genetics studies and the development of animal models. Together, these endeavors will continue to shed light on the genetic contribution to alcoholism. The reanalysis reviewed here has confirmed the consistency of the evidence for an important genetic influence on alcoholism risk from both twin and adoption studies. Many studies that followed children of alcoholics prospectively to identify precursors of alcoholism risk have focused on sons of alcoholics, assuming a stronger genetic influence in men than in women (for further discussion of markers, see the article by Anthenelli and Tabakoff, pp. 176–181).

Treatment for Alcoholism

So far, results appear to be particularly robust because of validation from different perspectives for MAOA and HTT. Alcoholism coexists frequently with other addictions, including illicit substance abuse and nicotine dependence more often than would be expected by chance 7. Such comorbidity between disorders can indicate the existence of etiological factors that are shared (co-causation), but can also reflect inter-causation.

DNA Regions Associated with Co-Occurring Disorders

The knowledge that such genes are likely Does Marijuana Kill Brain Cells to be influencing dependence in patients belonging to one of these populations is another tool that can be used to assess the nature of an individual’s problem and to tailor treatment accordingly. Clues in Human VariationsGenes powerfully influence a person’s physiology by giving rise to some 100,000 different types of protein, each of which has a direct role in the daily functioning of the body and brain or in regulating the activity of other genes. The strong connection between variations in basic physiology and an individual’s susceptibility to alcohol problems is well illustrated by the very first gene to be identified as affecting the risk of developing alcohol dependence. COGA’s asset is its family‐based longitudinal design that supports an intensive clinical, behavioral, genetic, genomic and brain function data collection.

As whole exome and whole genome sequencingtechnologies come down in cost, they are being applied to identifying rarevariants. For studies of rare variants, families are quite valuable for sortingout true positives from the background of individual variations that we allharbor. 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. Meta-analyses, whichcombine results across a number of studies in order to attain the criticalsample sizes needed, are being developed.

1. Large families that are densely affected may not be representative of the constellation of genetic and socio‐environmental risk and resilience factors influencing AUD in the general population.
2. Since then, there have been significant advances in techniques available for mapping genes and as a result considerable changes in outlook have occurred.
3. In Finland, Koskenvuo and colleagues (1984) conducted such a match using only an alcoholism discharge code and found a significantly higher risk ratio for male MZ than for male DZ twins of males hospitalized for alcoholism (i.e., 11.8 versus 5.5).

If genetic influences, in particular, are important, a significantly higher risk ratio should occur in MZ compared with DZ twin pairs. The two earliest Iowa adoption studies (i.e., the LSS and CFS) show significantly elevated risk to adopted-away sons from alcoholic biological backgrounds compared with control adoptees (i.e., risk ratios of 3.5 and 3.6, respectively), consistent with a genetic influence on alcoholism risk in men. For male adoptees in the remaining two samples, the risk to those from an alcoholic background is not significantly higher than that for control adoptees.