The MDR method is based on the idea that reducing the dimensionality of the data will make the detection of attribute dependencies (e.g., the SNP interactions that determine the classification of case/control) easier for a classifier such as a decision tree or a naïve Bayes learner. The drawbacks to this method include computational time for large datasets or interactions beyond four-way. Also, MDR software currently cannot be applied to continuous end points (such as blood pressure) but is very powerful for discrete end points such as “case” or “control” classifications even with missing data and genotyping error (Ritchie et al. =https://ecosoberhouse.com/ 2003b). For family-based studies, a version of the software known as the MDR–PDT is available that is based on a merging of MDR and the Pedigree Disequilibrium Test, which measures the transmission of disease alleles through pedigrees. This method has excellent power for detecting epistasis in studies of nuclear families with low phenocopy errors8 (Martin et al. 2006).

GENETICS DATA

Although it is important that such studies have revealed potential gene–environment interactions, a more thorough understanding of those interactions is required to aid in the development of potential treatment. The availability of high-dimensional SNP data has opened the door to genome-wide association studies. The COGA studies have made good genetics of alcoholism progress in using large-scale genotyping studies for alcoholism.

Genome-Wide Analysis of Epistasis

• NIAAA’s “Core Resource,” although intended for health care professionals, has helpful information for the public as well.
• COGA’s asset is its family‐based longitudinal design that supports an intensive clinical, behavioral, genetic, genomic and brain function data collection.
• Two influential linkage scans, one in a Southwestern American Indian tribe, a population isolate 41, and the other in the large, predominantly Caucasian Collaborative Study on the Genetics of Alcoholism (COGA) dataset 42 found evidence for linkage of AUD near the chromosome 4 GABAA receptor subunit gene cluster.

Additionally, DRD2, CACNA1C, DPYD, PDE4B, KLB, BRD3, NCAM1, FTO and MAPT were identified as druggable genes. NIAAA’s “Core Resource,” although intended for health care professionals, has helpful information for the public as well. One NIAAA-supported study, the Collaborative Study on the Genetics of Alcoholism Project (COGA),  explores how genes affect vulnerability to AUD, and has an easy-to-understand web resource about alcohol and genetics. This is an illustration of an Illumina GoldenGate array that was custom designed to include 1350 haplotype tagging single nucleotide polymorphisms (SNPs) within 127 stress- and addictions-related genes. This array was designed for Caucasian and African ancestry, hence the limited number of alcohol metabolism genes. “Using genomics, we can create a data-driven pipeline to prioritize existing medications for further study and improve chances of discovering new treatments.

EARLY RESULTS: CANDIDATE GENE STUDIES

Methods investigating systems genetics must be applied to the area of bioinformatics and expression analysis. Large-scale genetic analyses of mice showing alcoholism-like behavior should, in the future, be studied from the viewpoint of complex interactions and should apply methods such as MDR and eQTL mapping, as previously described. Because interactions can be studied in mice in a controllable and defined environment, they will be especially useful in examining how the rules governing interactions change in different environmental contexts.

• Alcohol is metabolized primarily in the liver, although thereis some metabolism in the upper GI tract and stomach.
• Third, there was the desire to collaborate with other groups by sharing COGA samples, thereby introducing more uniformity into research on the genetics of alcohol use disorder.
• The accompanying review (3. Brain Function) covers the available brain function data and resulting findings in detail.

This new and emerging field is the result of the synergy of disciplines such as bioinformatics, biotechnology, epidemiology, genetics, molecular biology, physiology, psychology, and statistics, all of which contribute to a more complete understanding of the interactions and functions of the entire genome with given ecological and sociological contexts. Detecting, characterizing, and interpreting gene–gene and gene–environment interactions as risk factors for alcoholism is an important first step in a systems genetics approach that combines genomics2 and proteomics3 data with methods to understand how biological processes work together to determine human health. This approach does not, however, negate the need to look for variants that directly impact disease independent of interaction effects (main effects) within the data. While the polygenic nature of complex traits has made individual risk variant and gene identification efforts challenging, this polygenicity can be leveraged with tools such as genome‐wide polygenic scoring115 (PGS or PRS, Figure 1). 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. 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.

DNA Regions Associated with Co-Occurring Disorders

The study also included a large sample of control families that were randomly selected from the community. For the analyses, the researchers chose a split-sample design—two groups of subjects (i.e., an initial sample and a replication sample) were analyzed independently; this approach allows investigators to examine the reproducibility of the initial study findings. The accompanying review (3. Brain Function) covers the available brain function data and resulting findings in detail. Systems genetics approaches to studying the genetic architecture of common Drug rehabilitation 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.

PRS for phenome-wide associations

The genetic architecture of susceptibility to a disease such as alcoholism can be defined as (1) the number of genes directly or indirectly involved, (2) the interindividual variation in those genes, and (3) the magnitude and nature of their specific genetic effects. Alcoholism develops in susceptible individuals as a result of genetic, environmental (e.g., alcohol consumption), and social influences, as well as their propensity for risk-taking behaviors (Ramoz et al. 2006). Because of this complex etiology, multiple levels of information must be integrated to more completely understand the genetic architecture of alcoholism. In the progression of multifactorial diseases such as alcoholism, gene–gene interactions result in a variety of differentially expressed proteins. These proteins also interact, resulting in certain biochemical and physiological characteristics that, in the presence of certain environmental influences, result in alcoholism.

RECRUITMENT: A FOCUS ON FAMILIES

• In analyzing the proteins they had identified, the researchers found what they describe as minimal overlap between genes and the proteins they code for and the different types of AUD.
• Because of the complexity of the risk factors for alcoholism and of the disorder itself, the COGA project was designed to gather extensive data from the participants.
• This review discusses the major genetic factors and some small variants in other genes that contribute to alcoholism, as well as considers the gene-environmental interactions.
• A complete review of all results from genetic, genomic, proteomic, and metabolic studies of alcoholism is beyond the scope of this review.
• The genes with the clearest contribution to the risk for alcoholism andalcohol consumption are alcohol dehydrogenase 1B (ADH1B) andaldehyde dehydrogenase 2 (ALDH2; mitochondrial aldehydedehydrogenase), two genes central to the metabolism of alcohol (Figure 1)20.

In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome‐wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in‐depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene‐brain‐behavior research into AUD. Alcoholism is a common disease resulting from the complex interaction of genetic, social, and environmental factors. Interest in the high heritability of alcoholism has resulted in many studies of how single genes, as well as an individual’s entire genetic content (i.e., genome) and the proteins expressed by the genome, influence alcoholism risk.

PAU PRS for phenome-wide associations

Some of these genes have been identified, including twogenes of alcohol metabolism, ADH1B and ALDH2,that have the strongest known affects on risk for alcoholism. Studies arerevealing other genes in which variants impact risk for alcoholism or relatedtraits, including GABRA2, CHRM2,KCNJ6, and AUTS2. As larger samples areassembled and more variants analyzed, a much fuller picture of the many genesand pathways that impact risk will be discovered. The COGA investigators also evaluated electrophysiological variables, such as EEGs and ERPs, from study participants. EEGs measure overall brain activity, whereas ERPs are brain waves elicited in response to specific stimuli (e.g., a light or sound).