Previous studies have shown that FGF21 and KLB are involved in sweet and alcohol preference in mice , and a recent study in humans found increased FGF21 expression in blood after binge drinking . These findings suggest that KLB and FGF21 act as part of a brain-liver endocrine axis that regulates alcohol consumption. Future studies could explore the effects of analogues of FGF21 on alcohol consumption, which are currently being tested in clinical trials for the treatment of type 2 diabetes and obesity . Although KLB and FGF21 seem to be promising avenues for translational research, it is worth noting that while SNPs in KLB are associated with alcohol consumption, they have not yet shown any association with AUD . This implies that this system might only be relevant for the regulation of normative consumption, although studies of larger AUD populations may yet reveal a role for these loci in AUD. Furthermore, although the locus probably impacts KLB, rs11940694 was found to be an expression quantitative trait locus for RFC1 gene expression in the cerebellum and hemisphere . Another well-replicated locus associated with both alcohol consumption and AUD is the region containing the glucokinase receptor gene, whose product is a regulatory protein that is produced by hepatocytes and is involved in the cellular trafficking of glucokinase. A nonsynonymous SNP in GCKR, rs1260326, was robustly associated with alcohol consumption in the MVP, UKB and 23andMe samples .
Intriguingly, rs1260326 has also been previously associated with multiple metabolic traits,indoor cannabis grow system including diabetes, obesity and liver disease . Given that alcohol consumption is strongly associated with both metabolic and lipid profiles , it is not clear whether the association with rs1260326 pinpoints a pleiotropic process central to metabolic traits, or whether alcohol causally impacts glucose metabolism and lipid levels, in part via GCKR. A recent study characterized the effects of alcohol in neural cell cultures derived from induced pluripotent stem cells and found that genes down-regulated upon alcohol exposure were involved in cholesterol homeostasis in the brain . These findings could suggest that AUD has both psychiatric and metabolic components, a theme that has also been suggested for other psychiatric disorders, such as anorexia nervosa . Additional evidence supporting this provocative hypothesis is the fact that several genes associated with alcohol use and dependence involve brain-endocrinemetabolic mechanisms. KLB is part of a brain-liver feedback loop, acetaldehyde modulates a number of ethanol effects in the brain, and enrichment analyses of alcohol-associated genes found glutamatergic enrichment not only in the brain but also in glucose and carbohydrate processing pathways .In general, the ‘candidate genes’ for AUD that were examined in smaller cohorts have not been replicated by larger and better powered GWAS . One exception is the corticotropin releasing hormone receptor 1 , a candidate gene extensively studied in humans and rodents before the advent of large-scale GWAS studies .
CRHR1 is central to the cortisol stress response as part of the hypothalamic-pituitary-axis. Extensive preclinical literature has shown that CRHR1 is associated with relapse to drug taking in mice [e.g. ] and there is some evidence that variation in CRHR1 modulates the role of psychological stress on alcohol intake . Encouragingly, the genomic region surrounding CRHR1 has been associated with alcohol consumption and misuse in several recent GWAS studies . However, CRHR1 is located in an inversion polymorphism of roughly 900kb that is common in Europeans and induces extensive LD spanning many genes , including CRHR1 and MAPT . MAPT encodes the protein tau, is involved in Parkinson’s and Alzheimer’s disease. Further work is therefore required to determine which variant are causal, as the inversion in this region complicates the ability of GWAS to fully address this question. Recent GWAS have identified several regions containing a set of genes that have pleiotropic effects on many psychiatric disorders and related traits; these genes may be tagging a latent factor . For example, the largest GWAS of alcohol and smoking, which used over 1 million individuals, performed a multivariate GWAS approach to show that 150 loci were associated with multiple substance use phenotypes; variation at PDE4B and CUL3 were associated with both smoking and drinks per week. Similarly, CADM2 has been recently associated with alcohol and cannabis use . CADM2 is a cell adhesion molecule that influences brain wiring and appears to have a role in multiple neuropsychiatric disorders . There is now mounting evidence from independent GWAS showing an association between common genetic variants at CADM2 and risky or impulsive behaviors including risk tolerance, automobile speeding propensity, number of sexual partners , sensation seeking and drug experimentation , cannabis initiation , and obesity and body mass index . CADM2 has also been associated with cognitive phenotypes, including educational attainment .
We therefore hypothesize that genetic variation at CADM2 may underlie a latent personality trait or risk factor that predisposes individuals to engage in risky actions . 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 ,cannabis grow equipment 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 attentiondeficit/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 humanalcohol use using animal models, including alcohol consumption, novelty response, impulsivity, 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. 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. 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 bio-banks 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 .