title: Modular network construction using eQTL data: an analysis of computational costs and benefits.
abstract: BACKGROUND
  In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest. Our focus is the systematic evaluation of the trade-off between network size and network search efficiency in the construction of these networks.
  
  
  RESULTS
  We developed a modular approach to network construction, building from smaller networks to larger ones, thereby reducing the search space while including more variables in the analysis. The goal is achieving a lower computational cost while maintaining high confidence in the resulting networks. As demonstrated in our simulation results, networks built in this way have low node/edge false discovery rate (FDR) and high edge sensitivity comparing to greedy search. We further demonstrate our method in a data set of cellular responses to two chemotherapeutic agents: docetaxel and 5-fluorouracil (5-FU), and identify biologically plausible networks that might describe resistances to these drugs.
  
  
  CONCLUSIONS
  In this study, we suggest that guided comprehensive searches for parsimonious networks should be considered as an alternative to greedy network searches.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota Minneapolis, MN, USA.
authors.forename: Yen-Yi
authors.initials: YY
authors.lastname: Ho
authors.affiliation: The Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine Baltimore, MD, USA.
authors.forename: Leslie M
authors.initials: LM
authors.lastname: Cope
authors.affiliation: Dana-Farber Cancer Institute and Harvard School of Public Health Boston, MA, USA.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
keywords: Bayesian networks; chemotherapy resistance; eQTL; network variable selection; search algorithm