Recent advances in genetic studies of alcohol use disorders

Of the 17 studies reviewed by Yeomans, ten showed increased food intake following alcohol consumption 5. One explanation is that there is a learned association between alcohol and eating; however, several experimenters disguised the presence of alcohol in their protocols and still found increased energy intake 5. It is unclear whether alcohol promotes food intake in the absence of hunger; however, it has been noted that alcohol may amplify individuals’ perception of appetite in response to food stimuli 5. Therefore, the objective of this article is to provide an update on the link between alcohol intake and obesity.

Core Resource information on genetic vulnerability to AUD

Genomic Structural Equation Modeling (gSEM; Ref.25) is a novel statistical genetic technique that builds upon LDSC to fit multivariate models of genetic associations, allowing researchers to identify the latent genetic factor structure of multiple phenotypes. This approach makes it possible to index genetic overlap among phenotypes, as well as variance that is unique to each trait (e.g., alcohol use quantity and frequency vs. AUD). Indeed, work by our group using gSEM found that the best fitting model was one that differentiated genetic factors for alcohol use and AUD relative to models with all alcohol-related indicators loading onto a single factor26. Further, this work demonstrated that the genetic correlation with PTSD for a common alcohol factor indexing shared genetic variance across alcohol indicates was null.

What are the risk factors for AUD?

They are shown in the direction in which the genes are transcribed (arrows), but this is opposite to their orientation on chromosome 4q (i.e., ADH5 is closest to the region where the chromosome arms are joined i.e., the centromere). Humans have seven different genes, called ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, and ADH7, that encode medium-chain ADHs (see Table 1).2 These genes all are aligned along a small region of chromosome 4 (Figure 1). The ADH enzymes they encode function as dimers—that is, the active forms are composed of two subunits. Based on similarities in their amino acid sequences and kinetic properties (e.g., the rate at which ethanol is oxidized), the seven ADH types have been divided into five classes (see Table 1). The three class I genes, ADH1A, ADH1B, and ADH1C, are very closely related; they encode the α, β, and γ subunits, which can form homodimers or heterodimers3 that account for most of the ethanol-oxidizing capacity in the liver (Hurley et al. 2002; Lee et al. 2006).

GENETICS DATA

Polygenic risk can also be challenging to communicate, and can lead to unrealistic expectations of what genomic medicine can do for the treatment and prevention of AUD. These were developed in collaboration with digital communication specialists and include short videos, text descriptions, interactive graphical elements, and key take‐aways, and can be found at cogastudy.org. An accompanying blog provides an overview of new findings with an eye towards public communication. From the outset, COGA utilized a single linking variable (record identifier, but without personal identifying information) that was unique to each family, and a sub‐variable for individuals within each family indicative of their relationship to the proband. However, all data are connected to a specific study participant via this common “id” variable regardless of longitudinal wave or phase of data collection (data are further anonymized prior to sharing with repositories or external collaborators).

People who meet criteria for dependence often have multiple cases of alcoholism in their families. The tendency to become dependent on alcohol has long been known to run in families, which for some only added to the social stigma attached to this complicated condition. But to scientists, that apparent heritability suggested that some genetic component underlying vulnerability to alcohol problems was being transmitted from generation to generation. Although the plan had been to include all items described above, upon viewing the loadings of the three factor model, the AUDIT-T from 23andMe had a near zero loading (−0.01, NS) on the AUD factor. Since the AUDIT-T is comprised of items capturing alcohol use (AUDIT-C) and alcohol problems (AUDIT-P), we opted to omit this item from the model entirely, rather than allowing it to load at a near zero level on one of those two factors and potentially water down the factor it loads on.

• Our science aims to identify pathways to enduring remission and processes that can be modified to minimize the deleterious impact of AUD across the lifespan.
• The accompanying review (3. Brain Function) covers the available brain function data and resulting findings in detail.
• Research has identified differences among population groups in the enzyme systems that regulate alcohol metabolism; those differences are thought to account for some cultural differences in drinking patterns.
• Thedifficulties of genetic studies are compounded by environmental heterogeneity inaccess to alcohol and social norms related to drinking.
• Not taking into account some of these potential confounding factors can certainly lead to biased estimates of the relationship between alcohol intake and body weight given that large inter-individual variations exist.

If more information could be gained about those groups of people, that knowledge could be applied to efforts to prevent alcohol abuse and alcoholism in the Native American population. Research on alcohol problems among urban Indians also would be useful, because it would improve understanding of how contextual social variables affect the course of alcohol abuse. On the one hand, they view drinking as a social mechanism that facilitates interactions with family and friends and increases bonding; on the other hand, alcohol abusers are acutely aware of the destruction it has wrought in their lives.

• Moreover, family studies require more effort to determine the participants’ genetic makeup (i.e., genotype), because even with the simplest type of family study, genotypes must be determined for sets of three people (e.g., two parents and an affected child) rather than just for individual case and control subjects.
• Understanding the factors that contribute to the high rate of alcohol-related problems in the Indian population is helpful in developing prevention and treatment strategies.
• In a review of existing data, May and Moran (1995), for instance, cited the rate of alcohol-related deaths for Indian men as 26.5 percent of all deaths and the rate for women as 13.2 percent.

Estimates of genetic and environmental effects did not appear to vary significantly within the group of U.S. studies or the group of Scandinavian studies. The estimate of the shared environmental contribution to alcoholism risk from the Kaij (1960) study is much greater than in all other studies. The reasons for this are unknown, although it is possible that in the work by Kaij some registrations were accidentally overlooked.

• Kaij conducted followup interviews with alcoholic twins and their co-twins,3 showing that twins having at least one Temperance Board registration exhibited a fivefold increase in probability of being diagnosed as alcoholic, thereby confirming the validity of registrations as a measure of alcohol problems.
• By characterizing brain and behavior in offspring from families enriched for AUD liability—both genetic and environmental—prior to the onset of maladaptive drinking behaviors, COGA data have shown the importance of precursors of AUD in a neurobehavioral framework (e.g., References 23, 34, 70, 71, 72).
• However the use of microarrays and advances in next-generation RNA-sequencing (RNA-Seq) 35 have conferred the ability to quantify mRNA transcripts in postmortem brain and analyze expression differences between alcoholics and controls within gene networks 36–39.
• Pyruvate carboxylase and malic enzyme mediate a cyclic metabolic pathway, which via the mitochondrial citrate and pyruvate transporters results in the transport of acetyl-CoA across the mitochondrial membrane and generation of cytosolic NADPH.
• A particularly attractive feature of studying rare variation in COGA is its family design, which aids the identification of both private and disorder‐generalized mutations.
• 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.

In this model, a person’s liability to develop alcoholism is assumed to be determined by the combined effects of many separate risk factors—genetic, environmental, or both. The distribution of liability to alcoholism in the general population is assumed to be continuous and to follow a bell curve. The majority of people exhibit an intermediate risk; some, a very low risk; and some, a very high risk.

Allgulander and colleagues (1991, 1992) found substantial risk ratios for MZ and DZ female twins of female alcoholics (i.e., 41.9 and 16.5, respectively), but again these do not differ significantly. Data from the LSS and CFS studies also allow us to examine the association between alcohol problems in the adoptive family and the occurrence of alcoholism in the adoptee. It should be noted, however, that because alcohol problems in the adoptive families could include problems experienced by siblings, analysis could overestimate the influence of the adoptive parents on the what percentage of alcoholism is genetic adoptees’ outcomes.