Efficient algorithms for detecting genetic interactions in genome-wide association study Public Deposited

Analytics

Downloadable Content

Download PDF
Request Accessible Version
Last Modified
  • March 20, 2019
Creator
  • Zhang, Xiang
    • Affiliation: College of Arts and Sciences, Department of Computer Science
Abstract
  • 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, which 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.
Date of publication
DOI
Resource type
Rights statement
  • In Copyright
Note
  • "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science."
Advisor
  • Wang, Wei
Language
Publisher
Place of publication
  • Chapel Hill, NC
Access
  • Open access
Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
1j92g810n?file=thumbnail Efficient algorithms for detecting genetic interactions in genome-wide association study 2019-04-10 Public