Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics

Open Access Proceedings

Mapping transcription mechanisms from multimodal genomic data

Hsun-Hsien Chang1*, Michael McGeachie12*, Gil Alterovitz1 and Marco F Ramoni12

Author Affiliations

1 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA

2 Channing Lab, Brigham and Women’s Hospital, Boston, Massachusetts, USA

For all author emails, please log on.

BMC Bioinformatics 2010, 11(Suppl 9):S2  doi:10.1186/1471-2105-11-S9-S2

Published: 28 October 2010

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.