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.

INVESTIGATING THE IMPACTS OF STREAMBED HABITAT HETEROGENEITY ON ECOSYSTEM STRUCTURE AND PROCESSES USING BASIC AND APPLIED PERSPECTIVES

Stream restoration often focuses on increasing habitat heterogeneity to reverse ecosystem degradation. However, the connection between heterogeneity and ecosystem structure and processes is poorly understood. We looked to investigate this interaction from both applied and basic science perspectives. For the applied study, we examined two culvert replacements designed to mimic natural stream channels, to see if they were better at maintaining ecosystem processes within as well as upstream and downstream of culverts compared to non-replaced culverts. We measured three ecosystem processes (nutrient uptake, hydrologic characteristics, and coarse particulate organic matter retention) and found that stream simulation culvert restoration improved organic matter retention within culverts, and that there were no differences in processes measured upstream and downstream of both restoration designs. Our results suggest that measurements of ecosystem processes are more likely to show a response to restoration if they match the scale of the restoration activity. For the basic science study, we quantified the longitudinal spatial heterogeneity of physical and biofilm characteristics at microhabitat to segment scales on streams with different streambed variability. We found that all physical characteristics and biofilm characteristics were spatially independent at the macro-habitat scale and greater. Together, these studies demonstrate the importance of scale in ecological interactions and the value of incorporating considerations of scale into restoration activities.