Despite the success of GWAS of alcohol use the mechanisms by which these newly identified genetic associations exert their effects are largely unknown. More importantly, alcohol consumption and misuse appear to have distinct genetic architectures . Ever-larger studies, particularly those extending mere alcohol consumption phenotypes, are required to find the genetic variants that contribute towards the transition from normative alcohol use to misuse, and development of AUD.One successful application of GWAS has been their use for assigning polygenic risk scores , which provide estimates of an individual’s genetic risk of developing a given disorder. Reassuringly, PRS for alcohol use behaviors predict equivalent phenotypes in independent cohorts [e.g. alcohol consumption , AD , AUD symptoms. Johnson et al recently identified that, compared to PRS for alcohol consumption , PRS for alcohol misuse were superior predictors of a range of alcohol-related phenotypes, particularly those pertaining to the domains of misuse and dependence. These findings further illustrate that alcohol consumption alone may not be a good proxy for AUD. PRS can also be used to test specific hypotheses; for example, PRS can be used to measure how environmental, demographic, and genetic factors interact with one another. Are there developmental windows where the effects of alcohol use and misuse are more invasive? Can we identify biomarkers that would inform the transition from normative alcohol use to excessive use and dependence? For instance, the alcohol metabolizing genetic effects on alcohol use appeared to be more influential in later years of college than in earlier years ,mobile vertical rack revealing that the nature and magnitude of genetic effects vary across development.
It is worth noting important limitations of PRS analyses. First, polygenic prediction is influenced by the ancestry of the population studied. For example, PRS for AUD generated in an African American cohort explained more of the variance in AUD than PRS derived from a much larger cohort of European Americans . This illustrates that the prediction from one population to another does not perform well . Second, the method of ascertainment may bias the results. As an example, PRS for DSM-IV AD derived from a population based sample predicted increased risk for AD in other population samples but did not associate with AUD symptoms in a clinically ascertained sample . Third, the variance explained by PRS is still low, and hence PRS have limited clinical application. For example, in the largest study of alcohol consumption , the alcohol consumption PRS accounted for only ~2.5% of the variance in alcohol use in two independent datasets. Recent work suggested that predictions may improve by incorporating functional genomic information. For example, McCartney et al showed that, compared to conventional PRS, risk scores that took into account DNA methylation were better predictors of alcohol consumption. Nonetheless, the way in which such methods can be used for prevention or treatments of AUD has yet to be established. Lastly, it remains to be determined the nature of these associations. Mendelian randomization analyses can serve to further understand and explore the correlations between alcohol use behaviors and comorbid traits .Before the era of large-scale genomic research, twin and family-based studies identified a high degree of genetic overlap between the genetic risk for AUD and psychopathology by modeling correlations among family members ). With the recent development of linkage disequilibrium score regression , it is now possible to estimate the genetic correlations between specific alcohol use behaviors and a plethora of psychiatric, health and educational outcomes using GWAS summary statistics. Most notably, the genetic overlap between alcohol consumption and AD was positive but relatively modest , suggesting that, although the use of alcohol is necessary to develop AD, some of the genetic liability is specific to either levels of consumption or AD.
Another consistent finding from genetic correlation analyses has been that alcohol consumption and AUD show distinct patterns of genetic overlap with disease traits. Counterintuitively, alcohol consumption tends to correlate with desirable attributes including educational attainment and is negatively genetically correlated with coronary heart disease, type 2 diabetes and BMI . These genetic correlations are unlike those observed when analyzing alcohol dependent individuals: AD was negatively genetically correlated with educational attainment and positively genetically correlated with other psychiatric diseases, including major depressive disorder , bipolar disorder, schizophrenia and attention deficit/hyperactivity disorder . Importantly, alcohol consumption and misuse measured in the same population showed distinct patterns of genetic association with psychopathology and health outcomes . This set of findings emphasize the importance of deep phenotyping and demonstrates that alcohol consumption and problematic drinking have distinct genetic influences. Ascertainment bias may explain some of the paradoxical genetic correlations associated with alcohol consumption . Population based cohorts, such as UKB and 23andMe, are based on voluntary participation and tend to attract individuals with higher education levels and socioeconomic status than the general population and, crucially, lower levels of problem drinking. In contrast, ascertainment in the PGC and MVP cohorts was based on DSMIV AD diagnosis and ICD codes for AUD, respectively. Collider bias has been proposed to underlie some of the genetic correlations between alcohol consumption and BMI ; however, BMI has been consistently negatively correlated with alcohol use in several subsequent studies . Furthermore, it is also possible that the genetic overlap between AD and aspects of alcohol consumption are dependent on the specific patterns of drinking. For example, Polimanti et al identified a positive genetic correlation between AD and alcohol drinking quantity , but not frequency. Prior to the availability of large population studies and collaborative consortia efforts, few genes were reliably associated with AUD. The use of intermediate traits or endophenotypes has become increasingly common and hundreds of new loci have now been associated with alcohol use behaviors.
Using intermediate phenotypes also facilitates translational research; we can mimic aspects of human alcohol use using animal models, including alcohol consumption, novelty response, impulsivity,vertical grow rack withdrawal and sensitivity . Animal models provide an opportunity to evaluate the role of newly identified genes at the molecular, cellular and circuit level. We may also be able to perform human genetic studies of specific components of AUD such as DSM-IV AD criterion count and alcohol withdrawal . To date these traits have only been studied in smaller samples but this approach will be invaluable as sample sizes increase. Another challenge for AUD genetics is that AUD is a dynamic phenotype, even more so than other psychiatric conditions, and therefore may necessitate yet larger sample sizes. Everlarger studies, particularly those extending mere alcohol consumption phenotypes, are required to find the genetic variants that contribute towards the transition from normative alcohol use to misuse, and development of AUD. Furthermore, genetic risk unfolds across development, particularly during adolescence, when drug experimentation is more prominent and when the brain is most vulnerable to the deleterious effects of alcohol . The Adolescent Brain Cognitive Development , with neuroimaging, genotyping and extensive longitudinal phenotypic information including alcohol use behaviors , offers new avenues for research, namely to understand how genetic risk interacts with the environment across critical developmental windows. Population biobanks aligning genotype data from thousands of individuals to electronic health records are also promising emerging platforms to accelerate AUD genetic research . Despite these caveats, the GWAS described in Table 1 have already vastly expanded our understanding of the genetic architecture of alcohol use behaviors. It is evident that alcohol use behaviors, like all complex traits, are highly polygenic .
The proportion of variance explained by genetic variants on GWAS chips ranges from 4 to 13% . It is possible that a significant portion of the heritability can be explained by SNPs not tagged by GWAS chips, including rare variants . For instance, a recent study showed that rare variants explained 1-2% of phenotypic variance and 11-18% of total SNP heritability of substance use phenotypes . Nonetheless, rare variants are often not analyzed when calculating SNP heritability, which can lead to an underestimate of polygenic effects, as well as missing biologically relevant contributions for post-GWAS analyses . Equally important is the need to include other sources of -omics data when interpreting genetic findings, and the need to increase population diversity . Therefore, a multifaceted approach targeting both rare and common variation, including functional data, and assembling much larger datasets for meta-analyses in ethnically diverse populations, is critical for identifying the key genes and pathways important in AUD.Alcohol use disorder is a highly prevalent, chronic relapsing disorder with a high disease burden in the United States. Despite current and lifetime prevalence rates of 13.9% and 29.1%, respectively, it remains largely untreated as only 7.7% of those with 12-month and 19.8% of those with lifetime diagnoses sought treatment in 2012– 2013. In spite of low treatment rates, pharmacotherapy offers a promising treatment method for AUD. The Federal Drug Administration has approved of four medications for AUD: disulfiram , oral naltrexone , extended-release injectable naltrexone , and acamprosate. However, these currently approved pharmacotherapies are only modestly effective, so there is still a great need to develop more effective interventions. Medications development is a very costly, cumbersome, and inefficient process that can take nearly 20 years from discovery to market. In particular, the development of treatments for alcoholism has been difficult with over 20 medications having been tested in humans yet only three were able to receive FDA approval, the last of which was granted over a decade ago. Therefore, there is a pressing need to develop valid and efficient methods to decrease the cost and length of medications development to better shepherd novel compounds from the lab to dissemination. The development of novel medications for AUD is a high priority research area, but the drug development process is long and challenging, with many compounds stuck in the transition from preclinical to clinical testing, also known as the “valley of death”. Beyond the “valley of death,” there is an overall need to develop effective methodologies for efficiently running clinical trials, particularly in screening novel compounds in early phase 2 trials. Early phase 2 trials, also known as “proof-of concept” studies, help determine if a novel medication is safe, tolerable, and efficacious using clinically relevant phenotypes such as cue-induced craving or subjective response to alcohol. These trials largely incorporate human laboratory paradigms to assess medication efficacy, providing valuable information on whether or not the medication warrants a larger clinical trial. However, human laboratory paradigms have not always demonstrated translational validity and often lack the ecological validity of clinical trials where medication efficacy is established through clinically meaningful endpoints. Therefore, there are major opportunities to refine this process of screening novel medications by combining the internal validity of human laboratory models and the external validity of clinical trials. To that end, the current study aims to develop and validate a novel early efficacy paradigm to screen medications for AUD. This early efficacy paradigm is the practice quit attempt model adapted from the smoking cessation medication development literature. In the original practice quit attempt model, individuals who report intrinsic motivation to quit smoking undergo a 7-day practice quit attempt while taking study medication. Individuals with high intrinsic motivation to quit smoking fared better on active medication, compared to placebo, on increased abstinence, while individuals with low intrinsic motivation showed no effect of active medication. Additionally, the practice quit model demonstrated specificity in which bupropion, an FDA-approved medication for smoking cessation, increased number of days abstinent, whereas modafinil, a medication ineffective for smoking cessation, was no different than placebo. The success of the practice quit attempt model for screening medications for nicotine dependence provides a basis for the development of a similar approach modified for AUD. In addition to the standard procedures of the practice quit attempt, we have included an established human laboratory paradigm to ensure that the novel model will be sensitive to medication effects. The cue-reactivity paradigm measures alcohol craving by having individuals hold and smell their preferred alcoholic beverage and a control beverage. Naltrexone , which is FDA-approved for AUD, is effective at significantly reducing alcohol-cue elicited craving compared to matched placebo.Thus, our current study will include CR in order to detect medication effects on cue-induced craving which will also verify the sensitivity of the novel practice quit attempt model to those medication effects.