COGA is the most comprehensive research project ever to be conducted on the inherited aspects of alcohol use disorder (AUD).COGA has the goal of identifying genes that influence an individual’s risk of developing alcohol problems, and understanding how that risk unfolds across the lifespan. These discoveries are used to develop more tailored and effective strategies to prevent and to treat alcohol problems. Phenotypic data were collected from MVP participants using questionnaires and the VA EHR and a blood sample was obtained for genetic analysis. In the previous PAU study9, the rg between MVP AUD and PGC AD was 0.98, which justified the meta-analysis of AUD (includes AUD and AD) across the two datasets, and the rg between AUD and UKB AUDIT–P was 0.71, which justified the proxy-phenotype meta-analysis of PAU (including AUD, AD and AUDIT–P) across all datasets.
Health Topics: Genetics and Alcohol Use Disorder
This genetic and environmental variability (i.e., heterogeneity) makes the task of identifying individual genes difficult. However, the COGA project was designed with these difficulties in mind and incorporated strategies to meet the challenges. This article briefly reviews these strategies and summarizes some of the results already obtained in the ongoing COGA study. This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA).
- In addition, five differentially expressed genes in different areas of postmortem human brains of alcoholics were replicated in any of three transcriptional profiling studies (Table 1) 36–41.
- Thus, prevalence rates for alcoholism are available for the total sample of biological parents and adoptees.
- Previous studies had found that a reduced amplitude of the P300 wave is a heritable phenotype that correlates with alcohol dependence and other psychiatric disorders (Porjesz et al. 1998).
- We performed fine mapping for TWAS in EUR using FOCUS, a method that models correlation among TWAS signals to assign a PIP for every gene in the risk region to explain the observed association signal.
Am I at Risk of Becoming Addicted to Alcohol?
A key finding from recent studies is that both AUD and AUDIT–P differ phenotypically and genetically from typical alcohol consumption7,10,13. 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)). 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.
From model organisms to human genetics
A separate adoption study conducted in Scandinavia (Bohman et al. 1981; Cloninger et al. 1981, 1985) replicated the Copenhagen study findings using different procedures. Although additional information was available from other agencies (e.g., welfare reports and national health insurance records) and was used in some articles by Cloninger and colleagues, the data reanalyzed here are limited to Temperance Board registration data as reported by Cloninger and colleagues (1985). The investigators also identified a subset of biological parents who had given up one child for adoption but had reared a second child themselves. If growing up in the environment of an alcoholic parent contributes significantly to alcoholism risk, this risk should be higher in the nonadopted sons and daughters of alcoholics, compared with the adopted-away sons and daughters.
Is AUD genetic?
The AUDIT-C yielded some GWS findings that did not overlap with those for AUD, https://ecosoberhouse.com/article/how-to-stop-alcohol-cravings/ which reflects genetic independence of the traits. This broadens our previous observations using SNPs in ADH1B, in which we validated the AUDIT-C score as an alcohol-related phenotype33. In that study, after accounting for the effects of AUDIT-C score, AUD diagnoses accounted for unique variance in the frequency of ADH1B minor alleles. Given the changes that occur over time in the levels of alcohol consumption and the rates of alcohol problems, it is somewhat surprising that the importance of genetic factors has not changed, although a similar finding has been observed for genetic influences on smoking behavior (Heath et al. 1993; Heath and Madden 1995). It could have been anticipated that increasing exposure to alcohol would make genetic factors become more important. Subsequent studies using samples ascertained from birth records have confirmed, without exception, a higher risk to MZ compared with DZ twins of alcoholics, although this difference has not always been significant.
- Family history reports by the adoptees suggest that, if anything, alcohol problems were less prevalent in the adoptive fathers of the adoptees who were sons of alcoholics (12 percent) than in the control adoptees (22 percent), a result which also suggests that selective placement of adoptees could not explain these findings.
- As the project enters its late third decade of scientific exploration, we approach our contributions to the study of AUD with optimism.
- Acetate is conjugated to coenzyme A and the resulting acetyl-CoA can be metabolized in the Krebs cycle, or utilized for the synthesis of fatty acids.
- We were also able to examine the risk posed by early initiation of alcohol use on later drinking milestones using several analytic paradigms (e.g., References 29, 30).
- All participants responded to questionnaires (e.g., personality) and the Semi‐Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring).
Our functional genomics efforts continue to accelerate the pace at which genetic discoveries can be placed in a biological context. Furthermore, whole genome sequencing (WGS) methods, especially as their accessibility increases, would substantively improve COGA’s ability to study rarer and structural variants, the role of which continues to emerge for psychiatric disorders. 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. Similarly, our genetics of alcoholism ability to measure the brain’s activity during resting state and during various cognitive tasks with exquisite temporal accuracy, allows us to develop and implement EEG protocols that uniquely address questions regarding the course of AUD. The design of COGA as a large, multi‐modal, family‐based study that was enriched for AUD liability also brings forth certain caveats.
Functional significance of GWAS variants
Other genes that also have been identified encode components of the neurotransmitter systems using dopamine, endogenous opioids, serotonin, and acetylcholine; nicotinic receptors; and a hormonal system known as the hypothalamic–pituitary axis. 1 This means that the samples of case and control subjects may not be sufficiently matched with respect to such factors as ethnicity or other population characteristics, which influence the prevalence of many gene variants or other factors that also may influence alcoholism risk. We tested the difference between genetic correlations for AUDIT-C and AUD using a two-tailed z-test. After correction for 714 tested traits, the genetic correlations for 188 traits showed significant differences between the two alcohol-related traits (Supplementary Data 36).
This pattern would only be expected if the same risk factors, genetic or environmental, operate across the entire spectrum of alcohol problems, from mild to severe. In addition to these findings, recent analyses demonstrate strong evidence for a locus that affects brain wave oscillations as measured by electroencephalography (Porjesz et al. 2002). Thus, a gene or genes that affect brain rhythms lies in a region of chromosome 4 that contains a cluster of genes encoding proteins (i.e., receptors) which interact with the brain chemical gamma-aminobutyric acid (GABA). NIAAA’s “Core Resource,” although intended for health care professionals, has helpful information for the public as well.
The number of unaffected sibling pairs genotyped in the replication sample was too small to analyze. Another phenotype that may reflect a protective influence against alcoholism is the maximum number of drinks a person has consumed in a 24-hour period (MAXDRINKS). This phenotype is quantitative and heritable, and a low number of drinks consumed in a 24-hour period may reflect a reduced tolerance for high levels of alcohol. An advantage of a quantitative phenotype is that everyone in a study can contribute to the genetic analysis, not just people who meet diagnostic criteria.
Published today in Nature Mental Health, the study was led by researchers at the Washington University in St. Louis, along with more than 150 coauthors from around the world. 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. Just as risk factors increase your chance of experiencing a condition, protective factors lower your risk. Other factors, such as friend groups and level of financial security, may be subject to change.