The Collaborative Study on the Genetics of Alcoholism: Overview

One size does not fit all and a treatment approach that may work for one person may not work for another. Treatment can be outpatient and/or inpatient and be provided by specialty programs, therapists, and health care providers. There is evidence that heavy episodic (binge) drinking, which results inexposure of tissues to high levels of alcohol, is particularly harmful81, 87, 88.

1. Resilience and protective factors

Because the diagnosis of an AUD requires the presence of a set ofsymptoms from a checklist, there are many different ways one could meet thecriteria. There are 35 different ways one could pick 3 criteria from 7 (DSM-IValcohol dependence) and 330 ways to pick 4 from 11 (DSM-5 severe AUD). Thedifficulties of genetic studies are compounded by environmental heterogeneity inaccess to alcohol and social norms related to drinking. Alcohol use disorder (AUD) is a medical condition characterized by an impaired ability to stop or control alcohol use despite adverse social, occupational, or health consequences. It encompasses the conditions that some people refer to as alcohol abuse, alcohol dependence, alcohol addiction, and the colloquial term, alcoholism.

• Of these 12,145 samples with genotype data, 136 only have C‐SSAGA data, so there are 12,009 COGA participants with full SSAGA and genotype data.
• All these advances in the understanding of the genetics of alcoholism should facilitate the development of more accurately targeted therapies using molecular diagnostic approaches.
• Binge drinkingis generally defined as a man consuming 5 standard drinks within 2 hours; women are typically smaller and have a lower percentage of body water, so 4 standarddrinks can reach similar alcohol levels.
• COGA’s asset is its family‐based longitudinal design that supports an intensive clinical, behavioral, genetic, genomic and brain function data collection.
• More recently, recognizing the numerous changes including marriage, divorce, childbirth and career transitions that can significantly impact the course of alcohol use, AUD and remission, COGA has focused on longitudinal data collection of those in mid‐life (30–40s).

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NIDA and other Institutes at NIH supported a recently released report on responsible use and interpretation of population-level genomic data by the National Academies of Sciences, Engineering, and Medicine. 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 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 without any experience of substance use, these genes correlated with parental substance use and externalizing behavior. In the study of complex disorders, it has become apparent that quitelarge sample sizes are critical if robust association results are to beidentified which replicate across studies.

BEHAVIORAL AND CLINICAL DATA

In initial efforts to understand who has healthy outcomes despite high genetic risk, we found that higher father–child relationship quality in adolescence promoted delays in alcohol initiation.141 With large, complex, multi‐generational pedigrees enriched for AUD, COGA provides an opportunity to look more closely not only at risk factors, but also factors that protect against the development of AUD. COGA ascertained probands in treatment for alcohol dependence, and a smaller number of comparison individuals from the same communities, and then recruited their families. Initial recruitment prioritized families with at least three first degree relatives meeting criteria for alcohol dependence (i.e., densely affected) although many families include more than three individuals with AUD, hence the higher than population prevalence of alcohol dependence and AUD (Table 1).

• Our knowledge of behaviors predisposing to alcoholism, including anxiety and impulsivity, is advancing rapidly through animal and human studies.
• The oral cavity and esophagus aredirectly exposed to those levels, and the liver is exposed to high levels from theportal circulation.
• COGA data have validated the 3‐stage neurobiological model73 of AUD and added to conceptualizations of related multi‐modal assessments (e.g., Reference 74) while also extending them by identifying novel contributors to “exiting” the cycle of AUD towards remission and recovery, amplifying the role of familial liability (e.g., References 23, 31, 33, 75).
• 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.
• 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.

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. To learn more about alcohol treatment options and search for quality care near you, please visit the NIAAA Alcohol Treatment Navigator. Updates regarding government operating status and resumption of normal operations can be found at opm.gov.

Linkage studies are relatively robust to populationdifferences in allele frequencies (because they test within-family inheritance), andcan find a signal even if different variants in the same gene or region areresponsible for the risk in different families. The drawback to this approach isthat linkage studies find broad regions of the genome, often containing manyhundreds of genes. In many cases, the initial linkage studies were followed by moredetailed genetic analyses employing single nucleotide polymorphisms (SNPs) that weregenotyped at high density across the linked regions. Some of the genes identifiedthrough this approach have been replicated across a number of studies and appear tobe robust genetic findings.

COGA data have validated the 3‐stage neurobiological model73 of AUD and added to conceptualizations of related multi‐modal assessments (e.g., Reference 74) while also extending them by identifying novel contributors to “exiting” the cycle of AUD towards remission and recovery, amplifying the role of familial liability (e.g., References 23, 31, 33, 75). 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). These meetings have been critical in empowering investigators to incorporate a data modality into their COGA analyses that they may be typically unfamiliar with, by partnering with a field expert and utilizing shared resources for data harmonization, code and protocol documents. The participation of all COGA investigators at these meetings also ensures that a legacy is in place for onboarding new scientists joining the group.

Many approaches to creating polygenic scores, from linkage disequilibrium (LD) clumping or pruning and thresholding approaches, to modern Bayesian methods, and even functional polygenic signatures, are available. While a high‐risk sample such as COGA can clearly contribute to characterizing genetic and environmental liability for AUD, it also presents a unique opportunity to study resilience and protective factors. The possibility of identifying such genetic “resilience” variants that may help protect against the development of an alcohol use disorder could provide insight into novel treatments or prevention efforts. For example, Hess et al.140 created a “polygenic resilience score” for schizophrenia by matching unaffected individuals at high genetic risk with risk‐matched cases, and then identifying genetic variants that contribute to resilience to schizophrenia and do not overlap with risk loci. Individuals with high genetic loading for AUD risk may also be resilient for entirely environmental reasons, such as strong familial and community support, or choosing not to drink after witnessing the effects of addiction on family members with alcohol use problems.

New NIH study reveals shared genetic markers underlying substance use disorders

The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large‐scale GWAS consortia. COGA’s wealth of publicly genetics of alcohol use disorder national institute on alcohol abuse and alcoholism niaaa available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits. With the advent of microarrays that can measure hundreds of thousands tomillions of single nucleotide polymorphisms (SNPs) across the genome,genome-wide association studies (GWAS) have provided a relatively unbiased wayto identify specific genes that contribute to a phenotype. To date, GWAS havefocused on common variants, with allele frequencies of 5% or higher.Most GWAS are case-control studies or studies of quantitative traits inunrelated subjects, but family-based GWAS provide another approach.

For instance, our multi‐omic cell‐type specific approach to analyzing existing summary statistics of AUD and typical drinking yielded strong associations with genes implicated in neurodegenerative diseases.59 COGA’s multi‐pronged functional genomic approach weaves data generation and curation together, and related findings are reviewed in an accompanying article (5. Functional Genomics). These data continue to serve, not only as a platform for characterization of loci discovered in our own GWAS of behavioral and brain data but also for emerging signals from larger scale meta‐analytic GWAS of AUD. While the first tranche of COGA GWAS data followed a case–control design,72, 73 all subsequent COGA analyses have used family‐based analytic approaches.

PECRis located within broad linkage peaks for several alcohol-related traits,including alcoholism66,comorbid alcoholism and depression67, level of response to alcohol68, and amplitude of the P3(00)response69, 70. COGA was among the first studies to pursue GWAS genotyping, first for diagnostic and then, increasingly, for quantitative traits. This shift reflected, in part, the growing recognition that genes of large effect were likely to be the exception rather than the norm for complex traits like AUD, and that GWAS approaches would be necessary to elucidate the many genes and variants of individually small effect sizes contributing to disorders. The inclusion of data from different ancestral groups in this study cannot and should not be used to assign or categorize variable genetic risk for substance use disorder to specific populations. As genetic information is used to better understand human health and health inequities, expansive and inclusive data collection is essential.

1. Biospecimen data access

A detailed description of these findings is outlined in the accompanying review (2. Sample and Clinical Data). The availability of parent‐offspring trio GWAS data in COGA facilitates examination of the environmental mechanisms through which parental genotypes influence offspring outcomes. In a recent application of these “nature of nurture” models in COGA,124 parental polygenic scores were partitioned into alleles that were transmitted and nontransmitted to the child.

The accompanying review (3. Brain Function) covers the available brain function data and resulting findings in detail. COGA’s brain function data (see, 3. Brain Function) have also been paired with the project’s functional genomics pipeline (see, 5. Functional Genomics) to provide mechanistic insights. In an example of this, several variants within KCNJ6 (encoding the GIRK2 G‐protein coupled inwardly rectifying potassium channel) were identified as genome‐wide significant in our family‐based GWAS of a frontal theta EEG phenotype75 (an endophenotype for AUD14). 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.

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The initial family‐based GWAS of COGA,74, 75 conducted in a second subset of the data, was analyzed using Genome‐Wide Association analyses with Family data (GWAF76). Subsequent GWAS combining the case–control and family‐based resources77 allowed for analyses using the Generalized Disequilibrium Test78 and linear mixed effects modeling with kinship matrices (lmekin) from the coxme package79 in R.80 As genotyping of the COGA sample proceeded, integrated analyses of imputed GWAS data across COGA’s dense and multigenerational families posed computational challenges. For instance, commonly used software such as PLINK62 or analytic modules that accounted for fewer degrees of relatedness (e.g., siblings or trios that could be analyzed using generalized estimating equations81, 82) did not adequately model these complex family structures, and Genome‐wide Complex Traits Analysis83 proved far too computationally burdensome for the high dimensional relatedness matrices arising in COGA. It is likely that, as for most complex diseases, alcohol dependence and AUDsare due to variations in hundreds of genes, interacting with different socialenvironments. An additional challenge in the search for genetic variants that affectthe risk for AUDs is that there is extensive clinical heterogeneity among thosemeeting criteria.

These include questions such as parsing direct versus indirect genetic effects, testing whether identified loci and polygenic signals are robust to careful control for confounding via within‐family comparisons, and fine‐grained examination of how genetic predispositions for AUD manifest across development and their pleiotropic effects on other traits and disorders. Advances in our understanding of the genetic etiology of AUD will continue to depend on more detailed, family‐based designs in data‐rich samples like COGA, as well as large‐scale, collaborative meta‐analyses that incorporate summary data from COGA alongside many other cohorts. Alcohol is widely consumed, but excessive use creates serious physical,psychological and social problems and contributes to many diseases. Alcoholism(alcohol dependence, alcohol use disorders) is a maladaptive pattern ofexcessive drinking leading to serious problems. Abundant evidence indicates thatalcoholism is a complex genetic disease, with variations in a large number ofgenes affecting risk.