IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Wind plant interaction with series-compensated power systems

Wind power based generation has been rapidly growing world-wide during the recent past. In order to transmit large amounts of wind power over long distances, system planners may often add series compensation to existing transmission lines owing to several benefits such as improved steady-state power transfer limit, improved transient stability, and efficient utilization of transmission infrastructure. Application of series capacitors has posed resonant interaction concerns such as through subsynchronous resonance (SSR) with conventional turbine-generators. Wind turbine-generators may also be susceptible to such resonant interactions. However, not much information is available in literature and even engineering standards are yet to address these issues. The motivation problem for this research is based on an actual system switching event that resulted in undamped oscillations in a 345-kV series-compensated, typical ring-bus power system configuration. Based on time-domain ATP (Alternative Transients Program) modeling, simulations and analysis of system event records, the occurrence of subsynchronous interactions within the existing 345-kV series-compensated power system has been investigated. Effects of various small-signal and large-signal power system disturbances with both identical and non-identical wind turbine parameters (such as with a statistical-spread) has been evaluated. Effect of parameter variations on subsynchronous oscillations has been quantified using 3D-DFT plots and the oscillations have been identified as due to electrical self-excitation effects, rather than torsional interaction. Further, the generator no-load reactance and the rotor-side converter inner-loop controller gains have been identified as bearing maximum sensitivity to either damping or exacerbating the self-excited oscillations. A higher-order spectral analysis method based on modified Prony estimation has been successfully applied to the field records identifying dominant 9.79 Hz subsynchronous oscillations. Recommendations have been made for exploring countermeasures.

Statistical methods for multi-marker testing in genetic association studies

As the development of genotyping and next-generation sequencing technologies, multi-marker testing in genome-wide association study and rare variant association study became active research areas in statistical genetics. This dissertation contains three methodologies for association study by exploring different genetic data features and demonstrates how to use those methods to test genetic association hypothesis. The methods can be categorized into in three scenarios: 1) multi-marker testing for strong Linkage Disequilibrium regions, 2) multi-marker testing for family-based association studies, 3) multi-marker testing for rare variant association study. I also discussed the advantage of using these methods and demonstrated its power by simulation studies and applications to real genetic data.