Linked Life Data

20529910

Source:http://linkedlifedata.com/resource/pubmed/id/20529910

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2010-6-9
pubmed:abstractText
As a promising tool for identifying genetic markers underlying phenotypic differences, genome-wide association study (GWAS) has been extensively investigated in recent years. In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus study since many diseases are known to be complex traits. A brute force search is infeasible for epistasis detection in the genome-wide scale because of the intensive computational burden. Existing epistasis detection algorithms are designed for dataset consisting of homozygous markers and small sample size. In human study, however, the genotype may be heterozygous, and number of individuals can be up to thousands. Thus, existing methods are not readily applicable to human datasets. In this article, we propose an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS. Our algorithm is exhaustive, i.e. it does not ignore any epistatic interaction. Utilizing the minimum spanning tree structure, the algorithm incrementally updates the contingency tables for epistatic tests without scanning all individuals. Our algorithm has broader applicability and is more efficient than existing methods for large sample study. It supports any statistical test that is based on contingency tables, and enables both family-wise error rate and false discovery rate controlling. Extensive experiments show that our algorithm only needs to examine a small portion of the individuals to update the contingency tables, and it achieves at least an order of magnitude speed up over the brute force approach.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-10924492, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-11230170, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-11281279, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-11404819, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-12951571, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-15716906, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-16254563, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-16983374, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17002500, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17283436, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17463246, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17554300, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17767157, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-17785348, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-19098029, http://linkedlifedata.com/resource/pubmed/commentcorrection/20529910-7851788
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
i217-27
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
TEAM: efficient two-locus epistasis tests in human genome-wide association study.
pubmed:affiliation
Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.