Detecting the presence and absence of causal relationships between expression of yeast genes with very few samples

Eun Yong Kang, Chun Ye, Ilya Shpitser, Eleazar Eskin

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)533-546
Number of pages14
JournalJournal of Computational Biology
Volume17
Issue number3
DOIs
StatePublished - Mar 1 2010

Keywords

  • Algorithms
  • Gene networks
  • Machine learning
  • Regulatory regions

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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