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Mapping transcription mechanisms from multimodal genomic data.

Chang HH, McGeachie M, Alterovitz G, Ramoni MF - BMC Bioinformatics (2010)

Bottom Line: We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms.The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu

ABSTRACT

Background: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.

Results: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.

Conclusions: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

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Related in: MedlinePlus

The TIM mapping SNPs on chromosome 21q11 and genes on chromosome 21q11-q22. The red squares denote SNPs, and blue circles denote genes. This map displays existing transcriptional channels, represented by the straight lines. The color of each line represents the signal strength of each channel as measured by mutual information.
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Figure 1: The TIM mapping SNPs on chromosome 21q11 and genes on chromosome 21q11-q22. The red squares denote SNPs, and blue circles denote genes. This map displays existing transcriptional channels, represented by the straight lines. The color of each line represents the signal strength of each channel as measured by mutual information.

Mentions: The transcriptional information of SNP-gene pairs was quantified by mutual information. To account for noise in the data, we used a permutation test to determine the noise level, and found that a mutual information score of 0.4 or below in the ALL data could be attributed to noise. Therefore, we consider a transcriptional channel to exist between a SNP and a gene when their mutual information is above 0.4. Figure 1 shows a portion of the TIM between SNPs on chromosome 21q11 and genes on chromosome 21q11-q22; in the figure, the red squares denote SNPs, and blue circles denote genes. The map displays existing transcriptional channels, represented by the straight lines, where the color of each line represents the signal strength of each channel as mutual information.


Mapping transcription mechanisms from multimodal genomic data.

Chang HH, McGeachie M, Alterovitz G, Ramoni MF - BMC Bioinformatics (2010)

The TIM mapping SNPs on chromosome 21q11 and genes on chromosome 21q11-q22. The red squares denote SNPs, and blue circles denote genes. This map displays existing transcriptional channels, represented by the straight lines. The color of each line represents the signal strength of each channel as measured by mutual information.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967743&req=5

Figure 1: The TIM mapping SNPs on chromosome 21q11 and genes on chromosome 21q11-q22. The red squares denote SNPs, and blue circles denote genes. This map displays existing transcriptional channels, represented by the straight lines. The color of each line represents the signal strength of each channel as measured by mutual information.
Mentions: The transcriptional information of SNP-gene pairs was quantified by mutual information. To account for noise in the data, we used a permutation test to determine the noise level, and found that a mutual information score of 0.4 or below in the ALL data could be attributed to noise. Therefore, we consider a transcriptional channel to exist between a SNP and a gene when their mutual information is above 0.4. Figure 1 shows a portion of the TIM between SNPs on chromosome 21q11 and genes on chromosome 21q11-q22; in the figure, the red squares denote SNPs, and blue circles denote genes. The map displays existing transcriptional channels, represented by the straight lines, where the color of each line represents the signal strength of each channel as mutual information.

Bottom Line: We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms.The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu

ABSTRACT

Background: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.

Results: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.

Conclusions: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Show MeSH
Related in: MedlinePlus