Academic journal article Alcohol Research

Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism

Academic journal article Alcohol Research

Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism

Article excerpt

For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development. KEY WORDS: Alcoholism; alcohol use disorders (AUDs); genetics; genetic basis of alcoholism; genetic technology; genetic association studies; quantitative trait loci (QTLs); genetic mapping; gene networks; genomes; genetical genomics; human studies; animal models

The multiple genetic, environmental, and behavioral factors that play a role in the development of alcohol use disorders (AUDs) make it difficult to identify individual genes linked to these disorders. Nevertheless, some genetic risk factors (i.e., specific variants) associated with AUDs have been identified within many genes, some of which code for proteins involved in known biological pathways. Despite this progress, it has been exceedingly difficult to determine which genes may be the most relevant to developing therapeutic interventions for alcoholism. The major obstacles in treatment development are that gene-disease associations reveal very little about the underlying biology and that any implicated gene variant explains only a tiny proportion of an individuals overall risk for an AUD. Recent work focusing on the study of alcohol-related gene networks is helping to shed light on the molecular factors affecting alcoholism and other complex diseases. This article will provide an overview of approaches used to identify or construct gene networks and describe how systems biology approaches are helping to better understand complex traits such as behavioral responses to beverage alcohol (i.e., ethanol) and alcoholism.

Traditional Approaches to Dissecting Complex Traits

The predominant experimental strategy used by contemporary geneticists to identify the genetic factors involved in complex traits, such as behavioral responses to alcohol, essentially is an expansion of the gene mapping approach proposed by Botstein and colleagues (1980) over 30 years ago. For this approach, investigators scan their samples for genetic variations (i.e., polymorphisms) that segregate with the trait- that is, which are found in samples with the trait more often than would be expected by chance and therefore might contribute to the development of that trait. In recent human studies, this approach typically has been applied in genome-wide association studies (GWASs) of large, population-based samples that comprise both case subjects (i.e., individuals expressing the trait, or phenotype, under investigation) and unaffected control subjects. Hundreds of complex diseases and traits, including susceptibility to AUDs, have been analyzed using GWASs, resulting in the identification of several important links between genetic variants and these diseases (Bierut et al. 2010). Overall, however, the success of this approach has been mixed, and greater progress has been hindered by insufficient sample sizes, stratified populations, the involvement of rare gene variants (i. …

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