Modular network construction using eQTL data: An analysis of computational costs and benefits

Yen Yi Ho, Leslie M. Cope, Giovanni Parmigiani

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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. Conclusion: In this study, we suggest that guided comprehensive searches for parsimonious networks should be considered as an alternative to greedy network searches.

Original languageEnglish (US)
Article numberArticle 40
JournalFrontiers in Genetics
Issue numberFEB
StatePublished - 2014


  • Bayesian networks
  • Chemotherapy resistance
  • EQTL
  • Network variable selection
  • Search algorithm


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