Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over a single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test at the genome-wide scale due to the large number of single nucleotide polymorphism (SNP) pairs to be tested. We have developed and implemented a series of efficient algorithms, i.e., FastANOVA, FastChi, COE, and TEAM, that support epistasis tests in a wide range of problem settings. These algorithms utilize a permutation test for proper error control. Unlike heuristic approaches, they guarantee to find the optimal solutions. It has been shown theoretically and experimentally that these algorithms significantly speed up the process of epistasis detection.
|Commitee:||McMillan, Leonard, Prins, Jan F., Threadgill, David W., Zou, Fei|
|School:||The University of North Carolina at Chapel Hill|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 73/01, Dissertation Abstracts International|
|Keywords:||Epistasis, Genome-wide association study|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be