The Collaborative Study on the Genetics of Alcoholism: An Update

Article excerpt

Researchers participating in the Collaborative Study on the Genetics of Alcoholism (COGA) are systematically screening all human chromosomes for evidence of DNA regions carrying genes that influence the risk of alcoholism and other related traits. This article by Dr. Howard J. Edenberg provides an update on COGA's findings, including the fact that certain regions on chromosomes 1, 2, 3, and 7 have been identified which may increase a person's risk of alcoholism. Conversely, genes located on chromosome 4 may have a protective effect. COGA researchers also have identified DNA regions that influence symptoms related to alcoholism, that are associated with disorders commonly co-occurring with alcoholism, or which are linked with certain electrophysiological measures commonly detected in alcoholics. (pp. 214-218)

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism (i.e., alcohol dependence) and alcohol-related characteristics and behaviors (i.e., phenotypes (1)). This collaborative project is funded by the National Institute on Alcohol Abuse and Alcoholism. Data collection, analysis, and/or storage for this study take place at nine sites across the United States. Because alcoholism is a complex genetic disorder, the COGA researchers expected that multiple genes would contribute to the 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. This genetic and environmental variability (i.e., heterogeneity) makes the task of identifying individual genes difficult. However, the COGA project was designed with these difficulties in mind and incorporated strategies to meet the challenges. This article briefly reviews these strategies and summarizes some of the results already obtained in the ongoing COGA study.

Study Design

Because of the expected complexity of factors contributing to alcoholism risk, COGA required a large sample size to allow detection of the genetic "signal" through the "noise." Of particular concern was the likely variability within the sample of both the number and type of genetic and environmental factors contributing to alcoholism risk; therefore, the contribution of any one factor would only account for a small fraction of the variation in risk. The investigators chose a family study design to allow the use of multiple methods of genetic analysis. Systematic recruitment from outpatient and inpatient alcoholism treatment facilities and assessment of families initially was carried out at six sites across the United States, with a seventh site more recently. 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.

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. Although standard diagnostic systems for alcoholism can reliably determine who needs treatment, the diagnostic criteria used in these systems comprise problems in many domains of functioning. This means that two people with the same diagnosis (e.g., alcohol dependence as defined in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised [DSM-III-R] of the American Psychiatric Association [APA] [1987]) may have different sets of symptoms, greatly complicating genetic analyses. Therefore, COGA researchers gathered a detailed psychiatric history of each participant, along with electrophysiological data (electroencephalograms [EEGs] and event-related potentials [ERPs]). …