Modeling three-dimensional chromosome structures using gene expression data

Guanghua Xiao, Xinlei Wang, Arkady B. Khodursky

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, coexpression 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. Modeling such spatial correlation explicitly may lead to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this paper, we explore chromosomal spatial correlation induced by 3D chromosome structures, and propose a hierarchical Bayesian method based on helical structures 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 microarrays. Simulation studies show computing feasibility of the proposed method and that, under the assumption of helical chromosome structures, it can lead to precise estimation of structural parameters and gene expression levels. Real data applications demonstrate an intriguing biological phenomenon that functionally associated genes, which are far apart along the chromosome chain, are brought into physical proximity by chromosomal folding in 3D space to facilitate their coexpression. It leads to important biological insight into relationship between chromosome structure and function.

Original languageEnglish (US)
Pages (from-to)61-72
Number of pages12
JournalJournal of the American Statistical Association
Volume106
Issue number493
DOIs
StatePublished - Mar 1 2011

Keywords

  • Bayesian hierarchical models
  • Chromosome folding structures
  • Chromosome looping
  • Gene regulation
  • Helical structures
  • Spatial correlation
  • Spatial modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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