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Inferring Gene Regulatory Networks from Genetical Genomics Data
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Author(s): 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
Source title:
Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Source Author(s)/Editor(s): Sanjoy Das (Kansas State University, USA), Doina Caragea (Kansas State University, USA), Stephen Welch (Kansas State University, USA)and William H. Hsu (Kansas State University, USA)
DOI: 10.4018/978-1-60566-685-3.ch004
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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.
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