Genetical Genomics, also referred to as ‘the genetics of gene expression’, uses naturally occurring, multi-factorial perturbations in segregating or genetically randomized populations. Genetical Genomics approaches integratively analyze gene expression data and genotype data (measurable DNA sequence polymorphisms) and make use of DNA sequence information when available.
Published in Chapter:
Inferring Gene Regulatory Networks from Genetical Genomics Data
Bing Liu (Monsanto Co., USA), Ina Hoeschele (Virginia Polytechnic Institute and State University, USA), and Alberto de la Fuente (CRS4 Bioinformatica, Italy)
Copyright: © 2010
|Pages: 29
DOI: 10.4018/978-1-60566-685-3.ch004
Abstract
In this chapter, we review the current state of Gene Regulatory Network inference based on ‘Genetical Genomics’ experiments (Brem & Kruglyak, 2005; Brem, Yvert, Clinton & Kruglyak, 2002; Jansen, 2003; Jansen & Nap, 2001; Schadt et al., 2003) as a special case of causal network inference in ‘Systems Genetics’ (Threadgill, 2006). In a Genetical Genomics experiment, a segregating or genetically randomized population is DNA marker genotyped and gene-expression profiled on a genomewide scale. The genotypes are regarded as natural, multifactorial perturbations resulting in different gene-expression ‘phenotypes’, and causal relationships can therefore be established between the measured genotypes and the gene-expression phenotypes. In this chapter, we review different computational approaches to Gene Regulatory Network inference based on the joint analysis of DNA marker and expression data and additionally of DNA sequence information if available. This includes different methods for expression QTL mapping, selection of regulator-target pairs, construction of an encompassing network, which strongly constrains the network search space, and pairwise and multivariate methods for Gene Regulatory Network inference, such as Bayesian Networks and Structural Equation Modeling.