Bioinformatics Seminar

 

Wednesday, November 11, 2009

3:00 - 4:00

Room 457 Blocker

 

Modeling Three-dimensional Chromosome Structures Using Gene Expression Data

 

Guanghua Xiao

Biostatistics

UT Southwestern Medical Center

 

 

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, co-expression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Ignoring such correlation in statistical modeling can reduce the efficiency of estimation and the power of statistical inference. Further, modeling the spatial correlation explicitly leads to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this study, we explored chromosomal spatial correlation induced by 3D chromosome structures, and proposed a hierarchical Bayesian method to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarray. Our simulation studies show that the proposed method leads to more precise estimation of gene expression levels. A real data application demonstrated an intriguing biological phenomenon that functional associated genes, which are far apart along a chromosome chain, are brought into physical proximity by 3D chromosomal structures to facilitate their co-expression. It leads to biological insights of relationship between the structures and functions of a chromosome.