Mapping transcription mechanisms from multimodal genomic data.
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.
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
Hsun-Hsien
HH
Chang
Michael
M
McGeachie
Gil
G
Alterovitz
Marco F
MF
Ramoni
D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling; D014644:Genetic Variation; D016678:Genome; D006801:Humans; D007938:Leukemia / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D014158:Transcription, Genetic / Q000235:genetics*