Causal Gene Network Inference from Genetical Genomics Experiments via Structural Equation Modeling

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dc.contributor.authorLiu, Bingen
dc.contributor.committeechairHoeschele, Inaen
dc.contributor.committeememberSaghai-Maroof, Mohammad A.en
dc.contributor.committeememberBirch, Jeffrey B.en
dc.contributor.committeememberMendes, Pedro J. P.en
dc.contributor.committeememberYe, Keyingen
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T20:16:34Zen
dc.date.adate2006-11-20en
dc.date.available2014-03-14T20:16:34Zen
dc.date.issued2006-09-11en
dc.date.rdate2009-11-20en
dc.date.sdate2006-09-22en
dc.description.abstractThe 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.en
dc.description.degreePh. D.en
dc.identifier.otheretd-09222006-161819en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09222006-161819/en
dc.identifier.urihttp://hdl.handle.net/10919/29060en
dc.publisherVirginia Techen
dc.relation.haspartetd.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStructural Equation Modelingen
dc.subjectGene Networken
dc.subjectGenetical Genomicsen
dc.subjectMicroarrayen
dc.subjectGene Expressionen
dc.titleCausal Gene Network Inference from Genetical Genomics Experiments via Structural Equation Modelingen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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