Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast

The availability of expression quantitative trait loci (eQTL) data can help understanding the genetic basis of variation in gene expression. However, it has proven difficult to accurately predict functional genetic changes due to low statistical power. To address this challenge, we developed a novel computational approach for combining eQTL data with complementary regulatory network to identify modules of genes, their underlying genetic polymorphism and their shared regulatory proteins activity. The resulting eQTL model implicates novel central protein complexes that share not only a regulatory protein but also an underlying genetic variation. Our method manifests higher sensitivity than prior computational efforts.

Authors and Affiliations

1. Broad Institute of MIT and Harvard, USA

   Irit Gat-Viks

2. Tel-Aviv University, Israel

   Renana Meller, Martin Kupiec & Ron Shamir

Editors and Affiliations

1. EBU3b, University of California San Diego, #4218, 9500 Gilman Drive, 92093-0404, La Jolla, CA, USA

   Vineet Bafna

2. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

   S. Cenk Sahinalp

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© 2011 Springer-Verlag Berlin Heidelberg

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