Understanding Alcohol Use Disorder National Institute on Alcohol Abuse and Alcoholism NIAAA

GWAS arebeginning to yield robust findings, although the experience in many diseases isthat very large numbers of subjects will be needed. To date, individual GWASstudies on alcohol dependence and related phenotypes have been relatively modestin size, and most do not reach genome-wide significance. As noted above, the functional ADH1B polymorphism isnot represented on GWAS platforms; GABA-receptor genes are often nominallysignificant but well below genome-wide significance in these studies.

• Large families that are densely affected may not be representative of the constellation of genetic and socio‐environmental risk and resilience factors influencing AUD in the general population.
• Majority of genomic data for large alcohol consumption and AUD meta-analysis was either from UKBiobank or from Million Veterans Project.
• More recently, our longitudinal design has facilitated characterizations of remission and recovery in AUD (e.g., References 31, 32, 33).
• Meta-analyses, whichcombine results across a number of studies in order to attain the criticalsample sizes needed, are being developed.
• The COGA data also remain ripe for future studies aimed at illuminating the pathways from genotype to AUD phenotype, and we highlight a few potential directions here.
• Graphical summary of three of the most important contributions to understanding the etiology of alcohol use disorders that have come, in part, from genetic analyses in COGA.

Researchers identify brain hub with key role in learned response to direct and indirect threats

• To do this accurately, it’s critical that the genetic evidence we gather includes globally representative populations and that we have members of communities historically underrepresented in biomedical research leading and contributing to these kinds of studies,” said Alexander Hatoum, Ph.D., a research assistant professor at Washington University in St. Louis and lead author of the study.
• Overview of genetically informed designs that have been used or are proposed for use in the COGA sample.
• This rich database has grown over the past three decades via the phased recruitment of additional families or family members and longitudinal follow‐up of participants.
• Further, most clinical trials and behavioral studies have focused on individual substances, rather than addiction more broadly.

Lasting changes in the brain caused by alcohol misuse perpetuate AUD and make individuals vulnerable to relapse. The good news is that no matter how severe the problem may seem, evidence-based treatment with behavioral therapies, mutual-support groups, and/or medications can help people with AUD achieve and maintain recovery. COGA's family‐based structure, multimodal assessment with gold‐standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping, when combined with its GWAS data, continues to provide insights into the etiology of alcohol use disorder and related disorders. COGA's wealth of publicly available genetic and extensive phenotyping data is a unique resource for our understanding of the genetic etiology of alcohol use disorder and related traits.

Majority of genomic data for large alcohol consumption and AUD meta-analysis was either from UKBiobank or from Million Veterans Project. Several other cohorts from dbGAP also contributed to large sample size of alcohol consumption GWAS by Liu et al, 2019. Genome-wide data on 14,904 genetics of alcohol use disorder national institute on alcohol abuse and alcoholism niaaa DSM-IV diagnosed AD individuals and 37,944 controls from 28 case/control and family-based studies were meta-analyzed for PGC’s AD GWAS. The initial approach was to use linkage studies in extended and densely affected pedigrees,11, 15, 16 where the key question was whether large sections of chromosomes (measured in centiMorgans, approximately 1 Mb) co‐segregated with AUD within families at a level greater than expected by chance (i.e., observed vs. expected identity‐by‐descent). An initial genome-wide study of German male inpatients followed up bytargeted genotyping of top SNPs and joint analysis provided evidence forassociation of alcohol dependence with two SNPs in the 3′ flankingregion of PECR, peroxisomal trans-2-enoyl-coA coenzymeA reductase65, amember of the short-chain dehydrogenase family of enzymes.

2. Intergenerational transmission of AUD: Delineating the nature of nurture

The design of COGA as a large, multi‐modal, family‐based study that was enriched for AUD liability also brings forth certain caveats. Large families that are densely affected may not be representative of the constellation of genetic and socio‐environmental risk and resilience factors influencing AUD in the general population. COGA has contributed to large, collaborative studies (e.g., References 5, 55, 69) that bring together data from many different studies with different ascertainments, and thereby enriched those studies. However, it is worth noting that effect sizes of loci and of polygenic scores may be influenced by our ascertainment strategy. Reassuringly, many COGA findings have been replicated in other samples (e.g., References 76, 77, 78, 79). COGA is one of the few family‐based genetic projects with a significant number of African Americans, who are greatly underrepresented in such studies, particularly those with family‐based designs.

Genome-wide Association Studies

The results from structural equation models were consistent with the idea that parental genotypes for alcohol problems influenced the likelihood of familial disruption and discord, and in turn children's alcohol outcomes. Exposure to parental relationship discord and parental divorce mediated, in part, the transmission of genetic risk for alcohol problems from parents to children to predict earlier ages regular drinking and intoxication, greater lifetime maximum drinks and more lifetime AUD criteria. Of note, these effects were observed in the European but not African ancestry families, underscoring the need for further empirical attention to nature of nurture processes in samples of non‐European ancestry. The concept that there are both genetic and environmental contributions to risk for AUD and its outcomes can be difficult to explain. 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.

In the 4th edition of the DSM (DSM-IV), alcohol dependence (AD) and abuse were considered as mutually exclusive diagnoses that together made up AUDs. By considering AD and abuse under single umbrella increased the number of diagnosed subjects, but this number was still not large enough to design powerful GWAS studies. Therefore, many genetic studies of alcoholism also concentrated on nonclinical phenotypes, such as alcohol consumption and Alcohol Use Disorders Identification Test (AUDIT)17–19, from large population based cohorts. The AUDIT, a 10-item, self-reported test was developed by the World Health Organization as a screen for hazardous and harmful drinking and can be used as a total (AUDIT-T), AUDIT-Consumption (AUDIT-C) and AUDIT-Problems (AUDIT-P) sub-scores. This review describes the genetic approaches and results from the family‐based Collaborative Study on the Genetics of Alcoholism (COGA).

Adolescent Brain Cognitive Development (ABCD) Study

For studies of rare variants, families are quite valuable for sortingout true positives from the background of individual variations that we allharbor. From its inception, COGA has focused on the importance of brain function and on developing novel brain intermediary phenotypes of risk for and consequences of alcohol use and AUD. This has been done through the examination of neuropsychological tests and noninvasively recorded brain electrical activity during resting state and cognitive tasks, and more recently, by deriving measures of neural synchrony and connectivity (3. Brain Function). About 80% of those with brain function data have more than one assessment, yielding a relatively large longitudinal cohort with these data.

Note that the official names of several ADH genes have been changed, and theliterature has been confused by some groups using non-standard names for some ofthe genes29. Health care professionals use criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), to assess whether a person has AUD and to determine the severity, if the disorder is present. Severity is based on the number of criteria a person meets based on their symptoms—mild (two to three criteria), moderate (four to five criteria), or severe (six or more criteria).

IDENTIFYING AN INTEGRATED APPROACH FOR AUD RESEARCH

At the core of COGA's scientific mission is our expectation that through the systematic characterization of the clinical, genetic, environmental and brain‐related factors that contribute to alcohol use and misuse, we can begin to identify mechanisms that will eventually truncate the course of AUD, if not substantially deter its onset altogether. 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.

A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in‐depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene‐brain‐behavior research into AUD. Information about the underlying genetic factors that influence risk to AUD can be derived from multiple levels of AUD including amounts of drinks (Alcohol consumption), severity and symptoms of alcohol abuse and dependence. Commonly, genome wide association studies (GWAS) of alcoholism have focused on phenotypes based on the Diagnostic & Statistical Manual of Mental Disorders (DSM)14.

“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 shared genetic mechanisms between substance use and mental disorders revealed in this study underscore the importance of thinking about these disorders in tandem,” said NIMH Director Joshua A. Gordon, M.D., Ph.D. “Using genomics, we can create a data-driven pipeline to prioritize existing medications for further study and improve chances of discovering new treatments. To do this accurately, it’s critical that the genetic evidence we gather includes globally representative populations and that we have members of communities historically underrepresented in biomedical research leading and contributing to these kinds of studies,” said Alexander Hatoum, Ph.D., a research assistant professor at Washington University in St. Louis and lead author of the study. It is now appreciated that a whole spectrum of allele frequencies andeffect sizes may play roles, from common variations with small effects throughrare variants of large effect. As whole exome and whole genome sequencingtechnologies come down in cost, they are being applied to identifying rarevariants.

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. COGA is a family based, diverse (~25% self‐identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7–97 years, in 2246 families of which a proportion are densely affected for AUD. 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). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome‐wide association study (GWAS) arrays.