Dissertation/Thesis Abstract

Efficient Algorithms for Detecting Genetic Interactions in Genome-Wide Association Study
by Zhang, Xiang, Ph.D., The University of North Carolina at Chapel Hill, 2011, 110; 3477518
Abstract (Summary)

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.

Indexing (document details)
Advisor: Wang, Wei
Commitee: McMillan, Leonard, Prins, Jan F., Threadgill, David W., Zou, Fei
School: The University of North Carolina at Chapel Hill
Department: Computer Science
School Location: United States -- North Carolina
Source: DAI-B 73/01, Dissertation Abstracts International
Subjects: Computer science
Keywords: Epistasis, Genome-wide association study
Publication Number: 3477518
ISBN: 9781124941837
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