New methods for studying complex diseases via genetic association studies

Genome-wide association studies (GWAS) have delivered many novel insights about the etiology of many common heritable diseases. However, in most disorders studied by GWAS, the known single nucleotide polymorphisms (SNPs) associated with the disease do not account for a large portion of the genetic factors underlying the condition. This suggests that many of the undiscovered variants contributing to the risk of common diseases have weak effects or are relatively rare. This thesis introduces novel adaptations of techniques for improving detection power for both of these types of risk variants, and reports the results of analyses applying these methods to real datasets for common diseases. Chapter 2 describes a novel approach to improve the detection of weak-effect risk variants that is based on an adaptive sampling technique known as Distilled Sensing (DS). This procedure entails utilization of a portion of the total sample to exclude from consideration regions of the genome where there is no evidence of genetic association, and then testing for association with a greatly reduced number of variants in the remaining sample. Application of the method to simulated data sets and GWAS data from studies of age-related macular degeneration (AMD) demonstrated that, in many situations, DS can have superior power over traditional meta-analysis techniques to detect weak-effect loci. Chapter 3 describes an innovative pipeline to screen for rare variants in next generation sequencing (NGS) data. Since rare variants, by definition, are likely to be present in only a few individuals even in large samples, efficient methods to screen for rare causal variants are critical for advancing the utility of NGS technology. Application of our approach, which uses family-based data to identify candidate rare variants that could explain aggregation of disease in some pedigrees, resulted in the discovery of novel protein-coding variants linked to increased risk for Alzheimer's disease (AD) in African Americans. The techniques presented in this thesis address different aspects of the "missing heritability" problem and offer efficient approaches to discover novel risk variants, and thereby facilitate development of a more complete picture of genetic risk for common diseases

Predicting Irregularities in Population Cycles

Oscillating population data often exhibit cycle irregularities such as episodes of damped oscillation and abrupt changes of cycle phase. The prediction of such irregularities is of interest in applications ranging from food production to wildlife management. We use concepts from dynamical systems theory to present a model-based method for quantifying the risk of impending cycle irregularity