Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.

Pencil -beam redefinition algorithm dose calculations for electron therapy treatment planning

The electron pencil-beam redefinition algorithm (PBRA) of Shiu and Hogstrom has been developed for use in radiotherapy treatment planning (RTP). Earlier studies of Boyd and Hogstrom showed that the PBRA lacked an adequate incident beam model, that PBRA might require improved electron physics, and that no data existed which allowed adequate assessment of the PBRA-calculated dose accuracy in a heterogeneous medium such as one presented by patient anatomy. The hypothesis of this research was that by addressing the above issues the PBRA-calculated dose would be accurate to within 4% or 2 mm in regions of high dose gradients. A secondary electron source was added to the PBRA to account for collimation-scattered electrons in the incident beam. Parameters of the dual-source model were determined from a minimal data set to allow ease of beam commissioning. Comparisons with measured data showed 3% or better dose accuracy in water within the field for cases where 4% accuracy was not previously achievable. A measured data set was developed that allowed an evaluation of PBRA in regions distal to localized heterogeneities. Geometries in the data set included irregular surfaces and high- and low-density internal heterogeneities. The data was estimated to have 1% precision and 2% agreement with accurate, benchmarked Monte Carlo (MC) code. PBRA electron transport was enhanced by modeling local pencil beam divergence. This required fundamental changes to the mathematics of electron transport (divPBRA). Evaluation of divPBRA with the measured data set showed marginal improvement in dose accuracy when compared to PBRA; however, 4% or 2mm accuracy was not achieved by either PBRA version for all data points. Finally, PBRA was evaluated clinically by comparing PBRA- and MC-calculated dose distributions using site-specific patient RTP data. Results show PBRA did not agree with MC to within 4% or 2mm in a small fraction (<3%) of the irradiated volume. Although the hypothesis of the research was shown to be false, the minor dose inaccuracies should have little or no impact on RTP decisions or patient outcome. Therefore, given ease of beam commissioning, documentation of accuracy, and calculational speed, the PBRA should be considered a practical tool for clinical use. ^