TY - GEN
T1 - Analysis of SNP-expression association matrices
AU - Tsalenko, Anya
AU - Sharan, Roded
AU - Edvardsen, Hege
AU - Kristensen, Vessela
AU - Børresen-Dale, Anne Lise
AU - Ben-Dor, Amir
AU - Yakhini, Zohar
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33745505569&partnerID=8YFLogxK
U2 - 10.1109/CSB.2005.14
DO - 10.1109/CSB.2005.14
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
C2 - 16447971
AN - SCOPUS:33745505569
SN - 0769523447
SN - 9780769523446
T3 - Proceedings - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
SP - 135
EP - 143
BT - Proceedings - 2005 IEEE Computational SystemsBioinformatics Conference, CSB 2005
T2 - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
Y2 - 8 August 2005 through 11 August 2005
ER -
