Back to Search
Start Over
ANALYSIS OF SNP-EXPRESSION ASSOCIATION MATRICES
- Source :
- CSB
- Publication Year :
- 2006
- Publisher :
- World Scientific Pub Co Pte Lt, 2006.
-
Abstract
- 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 50 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.
- Subjects :
- High throughput expression
Genotype
Sequence analysis
Molecular Sequence Data
Gene Expression
Breast Neoplasms
Single-nucleotide polymorphism
Biology
Polymorphism, Single Nucleotide
Biochemistry
Pattern Recognition, Automated
User-Computer Interface
Transcription (biology)
Artificial Intelligence
Gene expression
Biomarkers, Tumor
Humans
SNP
Genetic Predisposition to Disease
Molecular Biology
Genotyping
Genetic association
Expressed Sequence Tags
Genetics
Expressed sequence tag
Base Sequence
Gene Expression Profiling
Population variation
Chromosome Mapping
Sequence Analysis, DNA
Neoplasm Proteins
Computer Science Applications
Sequence Alignment
Algorithms
Transcription Factors
Subjects
Details
- ISSN :
- 17576334 and 02197200
- Database :
- OpenAIRE
- Journal :
- Journal of Bioinformatics and Computational Biology
- Accession number :
- edsair.doi.dedup.....6bc3a0fe2c35f186ed62944f0122733e