Modeling the diagnostic criteria for alcohol dependence with genetic animal models.

John Jr Crabbe, Kenneth S. Kendler, Robert Hitzemann

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

A diagnosis of alcohol dependence (AD) using the DSM-IV-R is categorical, based on an individual's manifestation of three or more symptoms from a list of seven. AD risk can be traced to both genetic and environmental sources. Most genetic studies of AD risk implicitly assume that an AD diagnosis represents a single underlying genetic factor. We recently found that the criteria for an AD diagnosis represent three somewhat distinct genetic paths to individual risk. Specifically, heavy use and tolerance versus withdrawal and continued use despite problems reflected separate genetic factors. However, some data suggest that genetic risk for AD is adequately described with a single underlying genetic risk factor. Rodent animal models for alcohol-related phenotypes typically target discrete aspects of the complex human AD diagnosis. Here, we review the literature derived from genetic animal models in an attempt to determine whether they support a single-factor or multiple-factor genetic structure. We conclude that there is modest support in the animal literature that alcohol tolerance and withdrawal reflect distinct genetic risk factors, in agreement with our human data. We suggest areas where more research could clarify this attempt to align the rodent and human data.

Original languageEnglish (US)
Pages (from-to)187-221
Number of pages35
JournalCurrent Topics in Behavioral Neurosciences
Volume13
DOIs
StatePublished - 2013

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Genetic Models
Alcoholism
Animal Models
Rodentia
Alcohols
Genetic Structures
Diagnostic and Statistical Manual of Mental Disorders
Phenotype
Research

ASJC Scopus subject areas

  • Behavioral Neuroscience

Cite this

Modeling the diagnostic criteria for alcohol dependence with genetic animal models. / Crabbe, John Jr; Kendler, Kenneth S.; Hitzemann, Robert.

In: Current Topics in Behavioral Neurosciences, Vol. 13, 2013, p. 187-221.

Research output: Contribution to journalArticle

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