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Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

Wolen AR, Miles MF - Alcohol Res (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia.

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.

No MeSH data available.


Related in: MedlinePlus

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.
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f4-arcr-34-3-306: 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.

Mentions: Figure 4 depicts the eQTL results for the same list of 307 ethanol-responsive genes identified in the B6/D2 study that earlier was used to construct coexpression networks. This analysis revealed that these coexpression networks share common eQTLs that drive this coordinated expression. Furthermore, the strongest eQTLs underlying many of these genes mapped to one end of chromosome 7, forming a trans eQTL cluster. These findings provide preliminary evidence that acute ethanol–responsive genes comprise a handful of gene coexpression networks in the pre-frontal cortex and that a key regulator of these networks resides on chromosome 7. A more extensive analysis of this type has recently been completed (Wolen et al., in press).


Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

Wolen AR, Miles MF - Alcohol Res (2012)

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.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860407&req=5

f4-arcr-34-3-306: 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.
Mentions: Figure 4 depicts the eQTL results for the same list of 307 ethanol-responsive genes identified in the B6/D2 study that earlier was used to construct coexpression networks. This analysis revealed that these coexpression networks share common eQTLs that drive this coordinated expression. Furthermore, the strongest eQTLs underlying many of these genes mapped to one end of chromosome 7, forming a trans eQTL cluster. These findings provide preliminary evidence that acute ethanol–responsive genes comprise a handful of gene coexpression networks in the pre-frontal cortex and that a key regulator of these networks resides on chromosome 7. A more extensive analysis of this type has recently been completed (Wolen et al., in press).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia.

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.

No MeSH data available.


Related in: MedlinePlus