Genetics of Alcohol Use Disorder: A Review PMC

Hence, Vrieze et al. (2013) found that substance use phenotypes, including those pertaining to alcohol use, and behavioral disinhibition share a genetic etiology, and that measured genetic variants contribute to their heritability. Although the protective effects of moderate drinking are controversial, we found that alcohol consumption in the absence of genetic risk for AUD may protect from cardiovascular disease, diabetes mellitus, and major depressive disorder. In contrast, individuals with genetic risk for https://ecosoberhouse.com/article/why-we-have-a-fear-of-being-sober-5-fears-about-it/ AUD are at elevated risk for some adverse secondary phenotypes, including insomnia, smoking, and other psychiatric disorders. However, individuals who have had health problems resulting from drinking are more likely to reduce or stop drinking by middle age or under-report their alcohol consumption. This offers an alternative explanation for the opposite genetic associations38, particularly in an older clinical sample in which a large proportion report current abstinence (reflected in an AUDIT-C score of 0).

alcoholism and genetics

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. Although this approach to studying complex behaviors was first proposed in the 1970s by psychiatric researchers investigating schizophrenia, it has recently proved even more valuable with modern tools for assessing biologic processes and analyzing genetic data. These included mean age-adjusted AUDIT-C scores, which are more stable than measures at a single point in time (more likely reflecting traits rather than states) and contrast with meta-analytic studies that may use phenotypes reflecting the lowest-common denominator among the studies comprising the sample.

Supplementary Data 39

With recent advances in technology, the most promising results stem from recent GWAS, which have helped to identify new variants in the genetics of AUD. Among the variants identified, the most significant SNPs remain in the alcohol metabolism enzyme genes, ADH and ALDH. Importantly, the prevalence of the various isoforms of ADH and ALDH differs among ethnicities and populations. Therefore, lower alcohol consumption in certain populations, as a result of the protective effect of alcohol metabolism SNPs, may be due to gene-environment interactions. A changing definition of the heterogeneous phenotype of AUD may also pose a challenge to identifying genetic variants through GWAS.

One of the earliest illustrations of gene– environment interaction in the area of substance use research demonstrated that genetic influences on alcohol use were greater among unmarried women, whereas having a marriage-like relationship reduced the impact of genetic influences on drinking (Heath et al. 1989). Religiosity also has been shown to moderate genetic influences on alcohol use among female subjects, with genetic factors playing a larger role among individuals without a religious upbringing (Koopmans et al. 1999). Insight, Not DestinyThe coga project has been structured around families, but this type of research has also strengthened Genetics of Alcoholism understanding of the relative importance of specific gene variants as risk factors in different ethnic groups. This is not to say that certain ethnicities are more prone to alcoholism; instead, like the ALDH1 gene version that makes many East Asians intolerant of alcohol, certain of the genetic variants that contribute to risk are much more prevalent in some ethnic groups than in others. The knowledge that such genes are likely 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.

Genetical Sensitivities to Alcohol

Recent attempts to address this issue have used pathway analysis and polygenic risk score approaches (Gelernter et al., 2014) but have not been widely applied to AUD genetic analyses. Some researchers have hypothesized that there may be large panels of rare functional variants, each of large effect, that predict risk for alcoholism with different variants occurring in different people. It is becoming increasingly easy, and the costs are rapidly decreasing, to detect rare variants using next-generation sequencing. Sequencing is rapidly becoming the key tool for characterization of the genetic basis of human diseases [84]. Clearly very large sample sizes are required to detect large panels of rare variants and there are significant bioinformatic requirements to deal with vast quantities of data.



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