Analysis of SNP-expression association matrices

Anya Tsalenko*, Roded Sharan, Hege Edvardsen, Vessela Kristensen, Anne Lise Børresen-Dale, Amir Ben-Dor, Zohar Yakhini

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 49 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Original languageEnglish
Title of host publicationProceedings - 2005 IEEE Computational SystemsBioinformatics Conference, CSB 2005
Number of pages9
StatePublished - 2005
Event2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005 - Stanford, CA, United States
Duration: 8 Aug 200511 Aug 2005

Publication series

NameProceedings - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005


Conference2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
Country/TerritoryUnited States
CityStanford, CA


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