On the basis of these advances, we identified existing medications predicted to be potential treatments for PAU, which can be tested. 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. 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.
- A GWAS of AUDIT-C with AUD as a covariate identified 10 GWS loci in EAs and 2 GWS loci in AAs (Supplementary Data 7).
- Scientists are learning more and more about how epigenetics can affect our risk for developing AUD.
- “Substance use disorders and mental disorders often co-occur, and we know that the most effective treatments help people address both issues at the same time.
- The AUDIT-C yielded some GWS findings that did not overlap with those for AUD, which reflects genetic independence of the traits.
- 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.
DNA Regions with Susceptibility Genes
Because the diagnosis of AUD is based on features other than alcohol consumption per se2,5, use of the AUD diagnosis from the EHR augmented the information provided by the AUDIT-C phenotype. Although EHR diagnostic data are heterogeneous, large-scale biobanks such as the MVP yield greater statistical power to link genes to health-related traits repeatedly documented over time in the EHR than can ordinarily be achieved in prospective studies23, justifying the lower resolution of EHR data. However, because the MVP sample is predominantly comprised of EA males, statistical power was limited in both the GWAS and the post-GWAS analyses of other populations and some female samples.
Extended Data Fig. 1 Manhattan and QQ plots for PAU/AUD meta-analyses in different ancestries.
Researchers hope to use this knowledge to develop new, more effective, and more targeted treatment and prevention strategies. Instead, variations in many, and perhaps hundreds, of genes likely have a small but measurable influence on disease risk that ultimately adds up to a substantial impact. Moreover, the impact of any one gene variation depends both on the individual’s genetic background (i.e., other genetic variations genetics of alcoholism the person carries) and on the environment. These factors further complicate the identification and confirmation of the role of any one gene. This overview briefly summarizes some of the strategies that can be used to identify specific gene variants that influence the risk of alcoholism and reviews some of the findings obtained to date, setting the stage for the following articles in this Special Section.
- The methods used in these genetic analyses and other aspects of the COGA study are described in more detail in the article by Bierut and colleagues, pp. 208–213, in this issue.
- This encompasses issues often referred to as alcohol dependence, alcohol misuse, alcohol addiction, and even the oft-used term—alcoholism.
- We also explored prior GWAS associations for the GWS SNPs from AUDIT-C and AUD analyses and found associations with other phenotypes for five of them (Supplementary Data 41).
- 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].
- Other relevant cell types for AUDIT-C, but not for AUD, included cardiovascular, adrenal or pancreas, liver, and musculoskeletal.
Attention-deficit hyperactivity disorder
Alcohol is metabolized primarily in the liver, although thereis some metabolism in the upper GI tract and stomach. The first step in ethanolmetabolism is oxidation to acetaldehyde, catalyzed primarily by ADHs; there are 7closely related ADHs clustered on chromosome 4 (reviewed in20). The second step is metabolism of theacetaldehyde to acetate by ALDHs; again, there are many aldehyde dehydrogenases,among which ALDH2 has the largest impact on alcohol consumption20. We have observed prevalence of 19.3 and 23.3% for current and life-time MDD respectively. This prevalence is more than the global prevalence of 3.4% which has been reported for depressive disorders [1].
In this study, we determined the prevalence and correlates of MDD, suicidality, GAD, PTSD, probable ADHD and alcohol abuse among participants of the GPC of MRC/UVRI and LSHTM Uganda research Unit, who contributed genetics data to the Uganda Genome Resource. There was no statistically significant association between any of the investigated factors and PTSD and current MDD (please see supplementary material, Table S1). Also, there were nonsignificant interactions among the examined socio-demographic factors across all the investigated mental disorders (please see supplementary material, Table S2) and thus we did not report adjusted effect sizes after any interaction. Mental disorders have persisted among the top ten leading causes of disease burden worldwide, with no evidence of global reduction in the burden since 1990 [1]. Mental disorders account for 4.9% of the global disability-adjusted life-years and 14.6% of the global years lived with disability [1].
Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders
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. The data from the second part of the split sample—the replication sample, which comprised 1,295 people from 157 families—generally supported the initial findings (Foroud et al. 2000). Thus, the replication sample again provided evidence that genes increasing the risk of alcoholism were located in the same regions of chromosomes 1 and 7, albeit with less statistical support.
The genetic analyses of the COGA participants identified four regions, on chromosomes 2, 5, 6, and 13, that appear to contain genes affecting the amplitude of the P300 (Begleiter et al. 1998). Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol‐related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene‐brain‐behavior framework.