Linked Life Data

20377453

Source:http://linkedlifedata.com/resource/pubmed/id/20377453

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-4-9
pubmed:abstractText
The availability of high-density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this article, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1557-8666
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
401-15
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study.
pubmed:affiliation
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. xiang@cs.unc.edu
pubmed:publicationType
Journal Article