In contrast to biological epistasis, statistical epistasis is a population-level phenomenon that arises from linear and nonlinear patterns of variation in genotypes and complex traits such as alcoholism. As such, detecting and characterizing statistical epistasis requires special analytical modeling methods. An association study by Osier and colleagues (2004) found a potential epistatic interaction between the ADH1B and ADH7 genes among a Han Chinese population. The ADH variant ADH1B Arg47His previously was found to be protective against alcoholism (Osier 1999).
Links to NCBI Databases
A review by Lesch (2005) focuses on serotonin as a link between alcoholism genetics and environment. Based on endophenotypes such as response to ethanol or reaction to stress and anxiety, the authors discuss the role that the serotinergic system plays in modifying each step of the biological hierarchy from genetic basic studies of variants of serotonin to molecular functional imaging. Another review article by Enoch (2006) combines environmental and genetic risk factors into models for high risk of alcoholism. The environmental factors include cultural norms, childhood sexual abuse, and binge drinking as an adolescent. These environmental factors can interact with an individual’s genetic background, making the individual more or less susceptible to genetic risk factors, such as the presence of certain variants of the enzymes monoamine oxidase A (MAOA) or ADH2. Investigating such bridges between gene variants, environment, and endophenotype or phenotype is at the heart of systems genetics and is likely to yield the greatest insight into disease etiology.
PRS for phenome-wide associations
- Among them, OPRM1 implicated naltrexone and GABRA4 may implicate acamprosate, both current treatments for AUD.
- 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 use of large-scale methods to identify and characterize genetic material (i.e., high-throughput technologies) for data gathering and analysis recently has made it possible to investigate the complexity of the genetic architecture of susceptibility to common diseases such as alcoholism on a systems level.
- That is, when interactions among multiple polymorphisms are considered, there are many multilocus genotype combinations that have very few or no data points.
- Biological epistasis is a measure of gene interaction occurring within a single organism, via gene–gene, gene–protein, and protein–protein interaction.
A) Distribution of cases (left bars) and controls (right bars) for each of the three genotypes of single nucleotide polymorphism (SNP) 1 and SNP2. The dark-shaded cells have been labeled “high risk,” and the light-shaded cells have been labeled “low risk.” B) Distribution of cases and controls when the two functional SNPs are considered jointly. A new single attribute is constructed by pooling the high-risk genotype combinations into one group (G1) and the low-risk genotype into another group (G0). Further, most clinical trials and behavioral =https://ecosoberhouse.com/ studies have focused on individual substances, rather than addiction more broadly.
- That doesn’t mean you’ll absolutely develop AUD if you have a family member living with the condition.
- Just as risk factors increase your chance of experiencing a condition, protective factors lower your risk.
- In other words, there will be no single “gene for alcoholism” but rather variations in many different genes that together, interacting with the environment, place some people at significantly higher risk for the disease.
- Alcohol addiction is a complex disease that results from a variety of genetic, social, and environmental influences.
- Qualified investigators can access freely available GWAS datasets via the database of Genotypes and Phenotypes (dbGaP) 83 and several studies have used this resource for replication samples.
- “Using genomics, we can create a data-driven pipeline to prioritize existing medications for further study and improve chances of discovering new treatments.
The genetics of alcohol dependence
The use of large-scale methods to identify and characterize genetic material (i.e., high-throughput technologies) for data gathering and analysis recently has made it possible to investigate the complexity of the genetic architecture of susceptibility to common diseases such as alcoholism on a systems level. The goal of systems genetics is to provide Alcoholics Anonymous an understanding of the complex relationship between the genome and disease by investigating intermediate biological processes. After investigating main effects, the first step in a systems genetics approach, as described here, is to search for gene–gene (i.e., epistatic) reactions. There are several other genes that have been shown to contribute to the riskof alcohol dependence as well as key endophenotypes. In most cases, studiesrecruited families having multiple members with alcohol dependence; such familiesare likely to segregate variants that affect the risk of alcohol dependence. Themost common initial approach was linkage analysis, in which markers throughout thegenome were measured to identify chromosomal regions that appeared to segregate withdisease across many families.
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. 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 genetics of alcoholism 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.
Another phenotype that may reflect a protective influence against alcoholism is the maximum number of drinks a person has consumed in a 24-hour period (MAXDRINKS). This phenotype is quantitative and heritable, and a low number of drinks consumed in a 24-hour period may reflect a reduced tolerance for high levels of alcohol. An advantage of a quantitative phenotype is that everyone in a study can contribute to the genetic analysis, not just people who meet diagnostic criteria.
- A drug repurposing analysis identified potential medications that have the potential to inform further pharmacological studies.
- 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).
- The COGA initiative is focused on optimizing the use of the past COGA data and completing data collection across the lifespan.
- The remainder of this review delves into GWAS‐based analyses of our richly characterized pedigrees.
- Previous studies had found that a reduced amplitude of the P300 wave is a heritable phenotype that correlates with alcohol dependence and other psychiatric disorders (Porjesz et al. 1998).
- We also conducted PheWAS in Yale–Penn, a deeply phenotyped cohort with comprehensive psychiatric assessments (SUDs and psychiatric disorders) and assessments for physical and psychosocial traits28.
- Studies continue to reveal other genes in which variants affect the risk of alcoholism or related traits, including GABRA2, CHRM2, KCNJ6 and AUTS2.
- Systems genetics approaches to studying the genetic architecture of common human diseases will not be possible without first being applied to model organisms in which the underlying biology is more simple and perturbation experiments are possible.
- Some of the genes identifiedthrough this approach have been replicated across a number of studies and appear tobe robust genetic findings.
- These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons.
The biological epistasis of alcoholism also has been studied in reference to neurological genes. As demonstrated by Job and colleagues (2007), in a study of a type of ethanol-stimulated opioid receptor (i.e., the μ-opioid receptor MOPr) in mice, epistatic interactions may be sexually dimorphic. The researchers found a sex–genotype interaction regarding the level of dopamine released in mice with the MOPr gene deleted (i.e., MOPr knockout mice) when they were stimulated with ethanol in the ventral striatum, with females showing a larger reduction. Throughout this manuscript, we use the terminology of “alcohol use disorder” to discuss individuals meeting diagnostic criteria for case status, but we note that this has been variously defined in the COGA sample depending on the diagnostic system at the time of sample recruitment. The Australian twin family study of AUD (TWINS, including Australian Alcohol and Nicotine Studies) participants were recruited from adult twins and their relatives who had participated in questionnaire- and interview-based studies on alcohol and nicotine use and alcohol-related events or symptoms (as described in ref. 70).
