Year of Publication

2016

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department

Statistics

First Advisor

Dr. Richard Charnigo

Abstract

We consider the problem of making predictions for quantitative phenotypes based on gene-to-gene interactions among selected Single Nucleotide Polymorphisms (SNPs). Previously, Quantitative Multifactor Dimensionality Reduction (QMDR) has been applied to detect gene-to-gene interactions associated with elevated quantitative phenotypes, by creating a dichotomous predictor from one interaction which has been deemed optimal. We propose an Aggregated Quantitative Multifactor Dimensionality Reduction (AQMDR), which exhaustively considers all k-way interactions among a set of SNPs and replaces the dichotomous predictor from QMDR with a continuous aggregated score. We evaluate this new AQMDR method in a series of simulations for two-way and three-way interactions, comparing the new method with the original QMDR. In simulation, AQMDR yields consistently smaller prediction error than QMDR when more than one significant interaction is present in the simulation model. Theoretical support is provided for the method, and the method is applied on Alzheimer's Disease (AD) data to identify significant interactions between APOE4 and other AD associated SNPs.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2016.525

Share

COinS