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Open Access Highly Accessed Methodology article

Using genetic markers to orient the edges in quantitative trait networks: The NEO software

Jason E Aten12, Tova F Fuller1, Aldons J Lusis13 and Steve Horvath14*

Author Affiliations

1 Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA

2 Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, USA

3 Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, USA

4 Biostatistics, School of Public Health, University of California, Los Angeles, USA

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BMC Systems Biology 2008, 2:34  doi:10.1186/1752-0509-2-34

Published: 15 April 2008

Abstract

Background

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers.

Results

We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue.

Conclusion

The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: http://www.genetics.ucla.edu/labs/horvath/aten/NEO webcite.