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,cannabis growing system 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 , 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.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. Counter intuitively, 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 DSM-IV 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,hydroponics rack system 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. The proportion of variance explained by genetic variants on GWAS chips ranges from 4 to 13%. It is possible that a significant portionof 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.With the introduction of combination antiretroviral therapy mortality among HIV-infected patients diminished significantly. However, some patient subgroups have different survival patterns,and have shown less decline in death rates.8 These include patients with psychiatric or substance use disorders, which are highly prevalent among patients treated for HIV/AIDS.There is also a high co occurrence between psychiatric and substance use disorders among the HIV-infected,as in other populations; and severity is greater in each type of disorder when there is co occurrence.Together they place individuals at elevated risk for poor health outcomes. Because psychiatric and substance use disorders frequently co-occur, it is important to examine the combined impact of these disorders among people with HIV infection. Research among HIV-infected patients has shown an association between depression symptoms, HIV disease progression and mortality; and mental illness and substance abuse are barriers to optimal adherence to combination antiretroviral regimens.One study of U.S. veterans found that survival was associated with greater number of mental health visits.Yet few studies have examined survival patterns for HIV-infected individuals who use alcohol or illicit drugs, but are generally not injection drug users, and have been diagnosed with psychiatric or SU disorders from a private health plan; nor have studies examined both psychiatric and SU disorders in relation to mortality. Previous research has shown that access to psychiatric and SU disorder care among HIV-infected patients varies based on sociodemographic factors and HIV illness severity.The current study compares mortality in HIV-infected patients diagnosed with psychiatric disorders and/or SU disorders to patients without either diagnosis receiving medical care from a private, fully integrated health plan where access to care and ability to pay for care are not significant factors. We also ex amine the effects of accessing psychiatric or SU treatment services. Improvement in depression has been associated with better adherence to combination antiretroviral therapy and increased CD4 cell counts.Social support for HIV-infected patients has been associated with improved immune system functioning.Therefore, we hypothesize that accessing services is associated with decreased mortality among patients with HIV infection.We conducted a retrospective observational cohort study for years 1996 to 2007 among HIV-infected patients who were members of the Kaiser Permanente Northern California health plan. The KPNC is an integrated health care system with a membership of 3.5 million individuals, re presenting 34% of the insured population in Northern California. The membership is representative of the northern California population with respect to race/ethnicity, gender, and socioeconomic status, except for some under representation of both extremes of the economic spectrum.HIV infected patients are seen at medical centers throughout the KPNC 17-county catchment region. The study population consisted of 11,132 HIV-infected patients who received health care at KPNC at some time be tween January 1, 1996 and December 31, 2006. The study sample included all HIV-infected patients who were 14 years of age or older on or after January 1, 1996 and had at least 6 months membership during the first year of study observation. This minimum age was chosen be cause the KPNC membership has very few HIV patients under age 14, children are likely to receive different psychiatric diagnoses than adolescents and adults , diagnosis of SU problems generally occurs later than age 13, and children are likely to receive services for these disorders in pediatrics departments rather than in the health plan’s specialty psychiatry and SU treatment programs. Patients could enter the study until December 31, 2006. In the data analyses, we also excluded 83 patients whose SU disorder diagnosis status was unclear. This resulted in a study analysis sample of 9751 patients.Since 1988, the KPNC Division of Research has maintained a surveillance system of patients who are HIV-1– seropositive, ascertained through monitoring electronic inpatient, outpatient, laboratory testing, and pharmacy dispensing databases for sentinel indicators of probable HIV infection. HIV-1 seropositivity is then confirmed through review of patient medical records.