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.

Testing Wisconsin asphalt mixtures for the 2002 AASHTO mechanistic design procedure

There has been a continuous evolutionary process in asphalt pavement design. In the beginning it was crude and based on past experience. Through research, empirical methods were developed based on materials response to specific loading at the AASHO Road Test. Today, pavement design has progressed to a mechanistic-empirical method. This methodology takes into account the mechanical properties of the individual layers and uses empirical relationships to relate them to performance. The mechanical tests that are used as part of this methodology include dynamic modulus and flow number, which have been shown to correlate with field pavement performance. This thesis was based on a portion of a research project being conducted at Michigan Technological University (MTU) for the Wisconsin Department of Transportation (WisDOT). The global scope of this project dealt with the development of a library of values as they pertain to the mechanical properties of the asphalt pavement mixtures paved in Wisconsin. Additionally, a comparison with the current associated pavement design to that of the new AASHTO Design Guide was conducted. This thesis describes the development of the current pavement design methodology as well as the associated tests as part of a literature review. This report also details the materials that were sampled from field operations around the state of Wisconsin and their testing preparation and procedures. Testing was conducted on available round robin and three Wisconsin mixtures and the main results of the research were: The test history of the Superpave SPT (fatigue and permanent deformation dynamic modulus) does not affect the mean response for both dynamic modulus and flow number, but does increase the variability in the test results of the flow number. The method of specimen preparation, compacting to test geometry versus sawing/coring to test geometry, does not statistically appear to affect the intermediate and high temperature dynamic modulus and flow number test results. The 2002 AASHTO Design Guide simulations support the findings of the statistical analyses that the method of specimen preparation did not impact the performance of the HMA as a structural layer as predicted by the Design Guide software. The methodologies for determining the temperature-viscosity relationship as stipulated by Witczak are sensitive to the viscosity test temperatures employed. The increase in asphalt binder content by 0.3% was found to actually increase the dynamic modulus at the intermediate and high test temperature as well as flow number. This result was based the testing that was conducted and was contradictory to previous research and the hypothesis that was put forth for this thesis. This result should be used with caution and requires further review. Based on the limited results presented herein, the asphalt binder grade appears to have a greater impact on performance in the Superpave SPT than aggregate angularity. Dynamic modulus and flow number was shown to increase with traffic level (requiring an increase in aggregate angularity) and with a decrease in air voids and confirm the hypotheses regarding these two factors. Accumulated micro-strain at flow number as opposed to the use of flow number appeared to be a promising measure for comparing the quality of specimens within a specific mixture. At the current time the Design Guide and its associate software needs to be further improved prior to implementation by owner/agencies.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.