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

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. iii Acknowledgements I would like to thank my advisor, Dr. Ina Hoeschele, for her time, patience, guidance, and encouragement during my doctoral study. Without her effort and support I would not have been able to finish this. I am very fortunate to have had the opportunity to work with her. I also would like to thank Dr. serving on my committee, sharing their knowledge, and providing guidance and support. Thank them for taking the time to read my dissertation and for their critical assessment of my research. I appreciate the opportunity of having studied in their classrooms. A special thank goes to my colleague Dr. Alberto de la Fuente. We have worked closely on this project, and we have some nice discussions almost everyday. I also extend my gratitude to my other colleagues in Dr. Hoeschele's group: Drs. Bing. I am very grateful to them for their friendship, valuable technique discussions, and support. I would also like to thank my collaborators on the microarray expression analysis, Dr. It is a great honor to work with them in the past years. Finally to my parents, Zhenhua …