Identifying gene networks underlying the neurobiology of ethanol and alcoholism

Abstract

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.

Figure 1

Correlation heatmap depicting patterns of co-expression among genes previously identified as being regulated by acute ethanol (Kerns et al. 2005). Each colored square represents the Pearson correlation (r) between a pair of genes, calculated using microarray expression data of prefrontal cortex tissue collected from B6, D2, and 27 BXD mouse strains. The blue and red colors indicate the strength and direction of the gene–gene correlation. Hierarchical clustering was applied to group genes based on the similarity of their expression profile across this dataset. In doing so, modules of co-expressed genes are revealed as cohesive blocks along the diagonal.

Figure 2

A) Gene networks that are regulated by acute alcohol exposure were identified in the same prefrontal cortex dataset used in Figure 1. Gene networks were generated by applying a hard threshold of 0.75 to the gene correlation matrix. The inset box contains a cognate of the myelin network (red) that was generated with expression data from the same strains following ethanol treatment. The ethanol-induced modifications of this network include the addition of a novel connection between Plp1 and Lpar1. B) Scatterplots illustrating the correlation between Plp1 and Lpar1 at baseline and following ethanol treatment. The effective absence of any correlation between these genes at baseline suggests that this relationship is driven by ethanol exposure.

Figure 3

Illustration of the concept of cis and trans expression quantitative trait loci (eQTLs). A) The left-most gene (red) codes for a transcription factor (TF) protein that activates the transcription of genes A (green) and B (blue) by binding to their respective promoters (gray). In the wildtype, or “normal,” scenario all genes are transcribed at their full potential, as indicated by the bar graph on the right. B) A variant (i.e., polymorphism) (triangle) in gene A’s promoter region hinders TF binding, causing a reduction in the rate at which gene A is transcribed, while gene B is unaffected. Thus, gene A is being regulated by a cis eQTL because its level of expression is associated with a nearby polymorphism located on the same chromosome. C) A polymorphism in the TF gene’s DNA binding region (hexagon)—the region of the TF protein that binds to gene promoters—hinders binding with all downstream promoters, regardless of whether the regulated gene is located near the TF gene, like gene A, or located on an entirely different chromosome, like gene B. In fact, all genes regulated by this TF would be linked to a trans eQTL at the site of this TF polymorphism.

Figure 4

Genome–genome plot of peak expression quantitative trait loci (eQTLs) for the same dataset used in figure 1. Each point indicates the chromosomal position of a gene versus the position of its peak eQTL. Point color is used to communicate the strength of association between a gene and its eQTL, measured by logarithm of the odds (LOD) score. A LOD score is a ratio that measures that probability that a gene is linked to genetic markers, versus the probability that it is not. Thus, the higher a LOD score, the more likely a gene’s expression level genuinely is regulated by an eQTL. Points plotted along the diagonal likely represent cis eQTLs, which also tend to have stronger LOD scores. Perpendicular tick-marks along both axes show the distribution of data points. Along the x-axis the dense clustering of tick-marks toward the proximal tip of chromosome 7 indicates the presence of a trans eQTL cluster, suggesting this region may harbor an important regulator of the gene co-expression modules seen in figure 1.

Figure 5

Diagram of how the genomic approaches discussed here can be integrated to identify gene networks and candidate genes for complex traits such as alcoholism. The information flow indicates how gene networks, expression quantitative trait locus (eQTL) and behavioral QTL analyses can be used together to identify candidate genes as potential targets for intervention. Note that resulting networks or candidate genes are entirely derived from experiments rather than relying on prior knowledge. In some cases, use of biomedical literature on gene–gene interactions can be used to augment such experimentally-derived networks.