UC Berkeley

It has become increasingly apparent that the set of exposures a person experiences can have profound ramifications for increasing their risks for various diseases. While genetics explains some portion of this risk, more complex diseases require understanding the interaction of genetics with this set of dynamic exposures, also referred to as the exposome. To address this, researchers have turned to various technologies focused on detecting indicators for these exposures. Due to the nature of the technologies used as well as the dynamic and continuous nature of the features measured, these exposomic datasets often suffer from a large amount of technical noise which must first be removed to allow for downstream detection of the biological signal of interest. In addition, it is important to consider the characteristics of these datasets and how they were generated when performing the analysis to answer the biological, and resulting statistical, questions at hand. In this dissertation we present analyses of three different types of exposomic datasets and associated questions of interest, in each case first discussing the necessary pre-processing. The first chapter applies a prediction framework to assess whether folate pathway disregulation is involved in increasing risk for pediatric ALL using a targeted metabolomics dataset. The second includes both hypothesis generation and a limited hypothesis testing analysis for transformed untargeted metabolomic features to reactomic features for paired mother-child data in a pediatric ALL case-control study. For hypothesis generation, marginal and joint analyses of the two datasets are performed using an ensemble feature selection method. Chapter three focuses on the previously mentioned ensemble feature selection method, using an untargeted adductomics lung cancer case-control dataset. Options for automating the method are proposed and evaluated using a simulation for bench-marking compared to standard hypothesis testing practices.