Detecting Association of Gene-Environment Interactions in Common and Rare Variants for Hypertension

Abstract

Subsequent malignant neoplasms (SMNs) or secondary cancers are one of the most negative effects resulting from cancer treatment such as chemotherapy or radiation. Given the severity and high incidence of mortality faced by cancer survivors, it is critical that we understand the cause of SMNs so that preventive measures or intervention can be done for individuals facing a higher risk of SMN incidence. The purpose of this thesis is to test the efficacy of newly developed statistical methods used to identify gene-environment interactions that are associated with a specific disease, in this case, SMNs, considering both common and rare variants, using optimally weighted combinations and generalized linear models. \\ The models proposed are a variation of the model to Test the effect of an Optimally Weighted combination of variants (TOW) and the Variable Weight TOW (VW-TOW). Two newly proposed weighting schemes, Inverse Standard Deviation (ISD) and the Correlation Coefficient Method (CCM) are tested. In order to test the models, real life data from previous studies is analyzed to target and identify genetic variants that have been shown to have an association with a disease, in this case, hypertension, comparing the analyses and results to a study done in testing rare variants for hypertension using family-based tests with different weighting schemes. The study focuses on data from Chromosome 3 genotyped during the Genetic Analysis Workshop 18 (GAW18), obtaining similar results to those in the hypertension study and the GAW18 study. Partial results from simulated studies are shown to support the methods' development and preliminary analyses. Comparisons are then done with existing methods to show when they exceed current standards.