Browsing by Subject "Epistasis"

The main contribution of this thesis is to examine the utility of kernel regression ap- proaches and variance component models for solving complex problems in statistical genetics and molecular biology. Many of these types of statistical methods have been developed specifically to be applied to solve similar biological problems. For example, kernel regression models have a long history in statistics, applied mathematics, and machine learning. More recently, variance component models have been extensively utilized as tools to broaden understanding of the genetic basis of phenotypic varia- tion. However, because of large combinatorial search spaces and other confounding factors, many of these current methods face enormous computational challenges and often suffer from low statistical power --- particularly when phenotypic variation is driven by complicated underlying genetic architectures (e.g. the presence of epistatic effects involving higher order genetic interactions). This thesis highlights two novel methods which provide innovative solutions to better address the important statis- tical and computational hurdles faced within complex biological data sets. The first is a Bayesian non-parametric statistical framework that allows for efficient variable selection in nonlinear regression which we refer to as "Bayesian approximate kernel regression", or BAKR. The second is a novel algorithm for identifying genetic vari- ants that are involved in epistasis without the need to identify the exact partners with which the variants interact. We refer to this method as the "MArginal ePIstasis Test", or MAPIT. Here, we develop the theory of these two approaches, and demonstrate their power, interpretability, and computational efficiency for analyz- ing complex phenotypes. We also illustrate their ability to facilitate novel biological discoveries in several real data sets, each of them representing a particular class of analyses: genome-wide association studies (GWASs), molecular trait quantitative trait loci (QTL) mapping studies, and cancer biology association studies. Lastly, we will also explore the potential of these approaches in radiogenomics, a brand new subfield of genetics and genomics that focuses on the study of correlations between imaging or network features and genetic variation.

Leukotrienes are arachidonic acid derivatives long known for their inflammatory properties and their involvement with a number of human diseases, most notably asthma. Recently, leukotriene-based inflammation has also been implicated in atherosclerosis: ALOX5AP and LTA4H, two genes in the leukotriene biosynthesis pathway, have been associated with various cardiovascular disease (CVD) phenotypes. To assess the role of the leukotriene pathway in CVD pathogenesis, we performed genetic association studies of ALOX5AP and LTA4H in a non-familial data set of early onset coronary artery disease. Our results support a modest role for the leukotriene pathway in atherosclerosis pathogenesis, reveal important genomic interactions within the pathway, and suggest the importance of using pathway-based modeling for evaluating the genomics of atherosclerosis susceptibility. Motivated by this need, we investigated the statistical properties of a class of matrix-based statistics to assess epistasis. We simulated multiple two-variant disease models with haplotypes to gain an understanding of pathway interactions in terms of correlation patterns. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequlibrium (LD) patterns with other haplotype markers. The simulated models can be summarized into three categories: 1. No epistasis in the presence of marginal effects and LD; 2. Epistasis in the presence of LD and no marginal effects; and 3. Epistasis in the presence marginal effects and LD. We then assessed previously introduced single-gene methods that compare whole matrices of Single Nucleotide Polymorphism (SNP) LD between two samples. These methods include comparing two sets of principal components, a sum-of-squared-differences comparing pairwise LD, and a contrast test that controls for background LD. We also considered a partial least-square (PLS) approach for modeling gene-gene interactions. Our results indicate that these measures can be used to assess epistasis as well as marginal effects under certain disease models. Understanding and quantifying whole-gene variation and association to disease using multiple SNPs remains a difficult task. Providing a single statistical measure per gene will facilitate combining multiple types of genomic data at a gene-level and will serve as an alternative approach to assess epistasis in genome-wide association studies. The matrix-based measures can also be used in pathway ascertainment tools that require scores on a gene-level.