Title page for ETD etd-09222006-161819

Type of Document Dissertation
Author Liu, Bing
URN etd-09222006-161819
Title Causal Gene Network Inference from Genetical Genomics Experiments via Structural Equation Modeling
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Hoeschele, Ina Committee Chair
Birch, Jeffrey B. Committee Member
Maroof, M. A. Saghai Committee Member
Mendes, Pedro J. P. Committee Member
Ye, Keying Committee Member
  • Structural Equation Modeling
  • Gene Network
  • Genetical Genomics
  • Microarray
  • Gene Expression
Date of Defense 2006-09-11
Availability unrestricted
The goal of this research is to construct causal gene networks for genetical genomics experiments using expression Quantitative Trait Loci (eQTL) mapping and Structural Equation Modeling (SEM). Unlike Bayesian Networks, this approach is able to construct cyclic networks, while cyclic relationships are expected to be common in gene networks. Reconstruction of gene networks provides important knowledge about the molecular basis of complex human diseases and generally about living systems.

In genetical genomics, a segregating population is expression profiled and DNA marker genotyped. An Encompassing Directed Network (EDN) of causal regulatory relationships among genes can be constructed with eQTL mapping and selection of candidate causal regulators. Several eQTL mapping approaches and local structural models were evaluated in their ability to construct an EDN. The edges in an EDN correspond to either direct or indirect causal relationships, and the EDN is likely to contain cycles or feedback loops. We implemented SEM with genetics algorithms to produce sub-models of the EDN containing fewer edges and being well supported by the data. The EDN construction and sparsification methods were tested on a yeast genetical genomics data set, as well as the simulated data. For the simulated networks, the SEM approach has an average detection power of around ninety percent, and an average false discovery rate of around ten percent.

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