COGA is an interdisciplinary project with the overarching goal of understanding the contributions and interactions of genetic, neurobiological and environmental factors towards risk and resilience over the developmental course of AUD, including relapse and recovery. In this overview, we outline the motivation behind and design of COGA as a multi‐modal project. Each of these domains has produced novel findings, highlighted in the companion reviews. However, the fundamental strength of COGA has been our ability to integrate across these domains in a cohort of families with whom we have established a robust research relationship for over three decades. 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.

• These multiple domains of data (described in detail in Begleiter et al. 1995, 1998; Hesselbrock et al. 2001) provide a rich resource for exploring phenotypes related to alcoholism.
• With the exception of the two outlier studies, in the remaining studies, nonshared environmental influences account for at least 30 percent of the variation in alcoholism risk.
• Thus, although heavy drinking is prerequisite to the development of AUD, the latter is a polygenic disorder and variation in genes expressed in the CNS (e.g., DRD2) may be necessary for individuals who drink heavily to develop AUD.
• We calculated PRS for PAU in EUR and AUD in AFR (using summary statistics that leave out the Yale–Penn 3 and PGC sample, which includes Yale–Penn 1).

Yohimbine as a pharmacological probe for alcohol research: a systematic review of rodent and human studies

Of particular value are single-nucleotide polymorphisms (SNPs)—sites at which people differ in a single base pair—in or near genes within the regions of interest. COGA investigators are doing additional genotyping of SNPs in and near candidate genes in the regions of linkage for further analysis of linkage and linkage disequilibrium (i.e., the nonrandom association of alleles). This should allow the investigators to greatly narrow the regions and to identify individual genes in which variations affect the risk for alcoholism and the other phenotypes they are studying. Analyses of 987 people from 105 families in the initial sample provided evidence that regions on 3 chromosomes contained genes that increase the risk for alcoholism (Reich et al. 1998). The strongest evidence was for regions on chromosomes 1 and 7, with more modest evidence for a region on chromosome 2. The DNA regions identified through these analyses were broad, as is typical for studies of complex genetic diseases, and therefore are likely to contain numerous genes.

Topical Collection on Genetic Disorders

Future studies with larger sample sizes are needed to identify additional variation contributing to these alcohol-related traits and to elucidate their interrelationship. We report here the largest multi-ancestry GWAS for PAU so far, comprising genetics of alcoholism over 1 million individuals and including 165,952 AUD/AD cases. The inclusion of multiple ancestries both broadened the findings and demonstrated that the genetic architecture of PAU is substantially shared across these populations. Cross-ancestry fine mapping improved the identification of potential causal variants, and cross-ancestry PRS analysis was a better predictor of alcohol-related traits in an independent sample than single-ancestry PRS.

Description of Additional Supplementary Files

One approach for comparing studies of disorders having a complex mode of inheritance has been a liability, or “threshold,” model. 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. The model assumes that those whose liability exceeds some critical value (i.e., threshold) will become alcoholic.

Authors’ original file for figure 1

We explored trait and disease associations for AUDIT-C-adjusted for AUD and AUD-adjusted for AUDIT-C, and found that the genetic correlations between the alcohol-related traits and other phenotypes did not differ substantially from the unadjusted ones (Supplementary Data 37, 38). Additionally, we explored genetic correlations for AUDIT-C-adjusted for BMI (Supplementary Data 39) and AUD-adjusted for BMI (Supplementary Data 40). Most of the genetic correlations for AUDIT-C-adjusted for BMI did not differ substantially from the unadjusted ones, except for anthropometric traits, where the negative correlation was attenuated (although still significant). Significant genetic correlations for AUD-adjusted for BMI did not differ substantially from those for AUD alone. 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).

Age varying polygenic effects on alcohol use in African Americans and European Americans from adolescence to adulthood

Compared to other genetic predictors, the genomic pattern identified here was also a more sensitive predictor of having two or more substance use disorders at once. The genomic pattern linked to general addiction risk also predicted higher risk of mental and physical illness, including psychiatric disorders, suicidal behavior, respiratory disease, heart disease, and chronic pain conditions. In children aged 9 or 10 years https://ecosoberhouse.com/article/what-difference-between-a-sober-house-and-a-halfway-house/ without any experience of substance use, these genes correlated with parental substance use and externalizing behavior. Because the study was based on a small sample size for AUD, in the future, researchers plan to repeat their analysis using larger gene expression databases from individuals with AUD, which they expect to become available in the next year. Considering only SNPs in genes that achieve genome-wide significance reveals no overlap across the studies, with the exception of the large effects contributed by variation at ADH1B and ALDH2 in Asian populations.

• A drug repurposing analysis identified potential medications that have the potential to inform further pharmacological studies.
• We differentiated participants genetically into five populations (see Methods, Supplementary Fig. 1) and removed outliers.
• C, Comparison for the highest PIPs from cross-ancestry and EUR-only fine mapping in the 92 regions.
• If siblings who are alcoholic share more alleles at a marker than would be expected based on chance, this suggests that genes within the chromosomal region containing the marker contribute to the risk of alcoholism.
• A separate adoption study conducted in Scandinavia (Bohman et al. 1981; Cloninger et al. 1981, 1985) replicated the Copenhagen study findings using different procedures.
• Because alcoholism is a complex genetic disorder, the COGA researchers expected that multiple genes would contribute to the risk.

As a binary trait, AUD provided less statistical power to identify genetic variation than the ordinal AUDIT-C score, but the multiple GWS findings unique to AUD argue against that as an explanation for the non-overlapping GWS findings for the two traits. Individual reviews in this issue provide detailed illustrations of the ways in which COGA data have contributed towards advancing our understanding of the etiology, course and consequences of AUD, and pathways from onset to remission and relapse. COGA’s intergenerational design has, in addition to identifying genetic risk factors, contributed to our understanding of the role of social genetic mechanisms50, 52, 64, 65, 66 in the interplay between genetic liability and the socio‐environmental milieu (e.g., References 40, 48, 67, 68). Diversity in the data have driven gene discoveries within our dataset (e.g., Reference 44) and in collaboration with others (e.g., References 5, 55, 69). Our ability to develop iPSCs from individuals with different genetic loading is producing insights into properties of cells derived from persons with archival electrophysiological and behavioral phenotyping, and how the cells differentially respond to ethanol exposure. A notable contribution of COGA’s family design has been to disentangle antecedents of, and predisposition to AUD from its sequelae.

More Recent Adoption Studies

The single-nucleotide polymorphism (SNP) heritability of alcohol dependence in a family-based, European-American (EA) sample was 16%10 and 22% in an unrelated African-American (AA) sample11. In the meta-analysis of data from the UK Biobank (UKBB) and 23andMe, the SNP heritability of the total AUDIT was estimated to be 12%, while for the AUDIT-C and AUDIT-P it was 11% and 9%, respectively). COGA’s asset is its family‐based longitudinal design that supports an intensive clinical, behavioral, genetic, genomic and brain function data collection. As the project enters its late third decade of scientific exploration, we approach our contributions to the study of AUD with optimism. 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. Through our collaborative gene‐brain‐behavior paradigm, we aspire to address both the causes and consequences of heavy alcohol use and AUD, which still contributes annually to 3 million preventable deaths globally.

The Collaborative Study on the Genetics of Alcoholism: An Update

Moreover, it will be equally important to determine the potential underlying mechanisms through functional studies, including the use of animal models, particularly those in which candidate genes or alleles are introduced into the organism (i.e., knocked-in). Although much work remains to be done, researchers already have made substantial progress. New technological developments that allow for faster and more complete genotyping and sequencing will accelerate progress, as will technical developments allowing targeted overproduction or inactivation of genes in animal models.