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Bioinformatics 2009 25(12):i15-i20; doi:10.1093/bioinformatics/btp189
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum

Yang Huang 1, Stefan Wuchty 2,{dagger}, Michael T. Ferdig 3 and Teresa M. Przytycka 1,*

1National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, 2Northwestern Institute on Complexity, Northwestern University, 600 Foster Street, Evanston, IL 60201 and 3Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, 107 Galvin Life Sciences, Notre Dame, IN 46556, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Analysis of expression quantitative trait loci (eQTL) significantly contributes to the determination of gene regulation programs. However, the discovery and analysis of associations of gene expression levels and their underlying sequence polymorphisms continue to pose many challenges. Methods are limited in their ability to illuminate the full structure of the eQTL data. Most rely on an exhaustive, genome scale search that considers all possible locus–gene pairs and tests the linkage between each locus and gene.

Result: To analyze eQTLs in a more comprehensive and efficient way, we developed the Graph based eQTL Decomposition method (GeD) that allows us to model genotype and expression data using an eQTL association graph. Through graph-based heuristics, GeD identifies dense subgraphs in the eQTL association graph. By identifying eQTL association cliques that expose the hidden structure of genotype and expression data, GeD effectively filters out most locus–gene pairs that are unlikely to have significant linkage. We apply GeD on eQTL data from Plasmodium falciparum, the human malaria parasite, and show that GeD reveals the structure of the relationship between all loci and all genes on a whole genome level. Furthermore, GeD allows us to uncover additional eQTLs with lower FDR, providing an important complement to traditional eQTL analysis methods.

Contact: przytyck{at}ncbi.nlm.nih.gov

{dagger}Present address: NOB, NCI, NIH, 37 Convent Drive 1142E, Bethesda, MD 20892, USA.


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