Discovering gene-gene and gene-environment causal interactions from different types of genomic data using statistical methods in bioinformatics Book Chapter

Yoo, C. (2012). Discovering gene-gene and gene-environment causal interactions from different types of genomic data using statistical methods in bioinformatics . 61-72.

cited authors

  • Yoo, C

authors

abstract

  • In this chapter we will review bioinformatics approaches in modeling the gene-gene and gene-environment causal interactions of systems biology studies, such as Genomewide association studies (GWAS) and Microarray gene expression studies. Many genegene causal interactions models have been introduced recently. However, given many different types of genomic data - such as gene sequence, gene expression, etc., not many bioinformatics approaches are available in combining knowledge extracted from the data. To this end, we will review gene-gene and geneenvironment interaction in terms of Single Nucleotide Polymorphism (SNP) and gene expression studies. We introduce a promising probabilistic causal model, i.e., causal Bayesian networks, and discuss how to represent gene-environment causal interactions using the probabilistic causal model. We conclude this chapter with promising research directions of gene-gene and gene-environment causal interactions models using bioinformatics approaches. © 2012 Nova Science Publishers, Inc. All rights reserved.

publication date

  • December 1, 2012

International Standard Book Number (ISBN) 13

start page

  • 61

end page

  • 72