Article

Harnessing naturally randomized transcription to infer regulatory relationships among genes

Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA 98195, USA.
Genome biology (Impact Factor: 10.47). 02/2007; 8(10):R219. DOI: 10.1186/gb-2007-8-10-r219
Source: PubMed

ABSTRACT We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.

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Available from: Frank Emmert-Streib, Feb 11, 2015
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    • "Theoretical evidence in the form of " Causality Equivalence Theorem " has been proposed by Chen et al. [21] to establish causal relationship. According to the theorem, under the assumption that í µí±‹ is randomized, the following conditions are needed to establish a causal relation: C1: í µí±‹ and í µí±€ are associated, C2: í µí±‹ and í µí±Œ are associated, C3: í µí±‹ is independent of í µí±Œ | í µí±€. "
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    10/2013; 2013:493019. DOI:10.1155/2013/493019
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    • "Regulators with an asterisk (*) were found by Zhu et al. (2008). Regulators marked with a plus (þ) were found in Chen et al. (2007) study and unlabeled regulators are novel predictions. In parentheses after the name of the regulator is the number of targets that we found. "
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    ABSTRACT: Inference of biological networks from high-throughput data is a central problem in bioinformatics. Particularly powerful for network reconstruction is data collected by recent studies that contain both genetic variation information and gene expression profiles from genetically distinct strains of an organism. Various statistical approaches have been applied to these data to tease out the underlying biological networks that govern how individual genetic variation mediates gene expression and how genes regulate and interact with each other. Extracting meaningful causal relationships from these networks remains a challenging but important problem. In this article, we use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. We evaluate our method using a well studied dataset consisting of both genetic variations and gene expressions collected over randomly segregated yeast strains. Our predictions of causal regulators, genes that control the expression of a large number of target genes, are consistent with previously known experimental evidence. In addition, our method can detect the absence of causal relationships and can distinguish between direct and indirect effects of variation on a gene expression level.
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    • "Genetic variation information in a segregating population has been used to reconstruct causal phenotype networks [3] [4] [5] and to infer causal relationships among pairs of phenotypes [6] [7] [8] [9] [10]. Approaches based on structural equation models [11] [12] [13] and causal discovery algorithms [14] [15] have also been proposed. "
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