Linked Life Data

20583286

Source:http://linkedlifedata.com/resource/pubmed/id/20583286

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-7-1
pubmed:abstractText
Due to the complex nature of common diseases, their etiology is likely to involve "uncommon but strong" (UBS) interactive effects--i.e. allelic combinations that are each present in only a small fraction of the patients but associated with high disease risk. However, the identification of such effects using standard methods for testing association can be difficult. In this work, we introduce a method for testing interactions that is particularly powerful in detecting UBS effects. The method consists of two modules--one is a pattern counting algorithm designed for efficiently evaluating the risk significance of each marker combination, and the other is a sequential permutation scheme for multiple testing correction. We demonstrate the work of our method using a candidate gene data set for cardiovascular and coronary diseases with an injected UBS three-locus interaction. In addition, we investigate the power and false rejection properties of our method using data sets simulated from a joint dominance three-locus model that gives rise to UBS interactive effects. The results show that our method can be much more powerful than standard approaches such as trend test and multifactor dimensionality reduction for detecting UBS interactions.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1098-2272
pubmed:author
pubmed:copyrightInfo
(c) 2010 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
434-43
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
An "almost exhaustive" search-based sequential permutation method for detecting epistasis in disease association studies.
pubmed:affiliation
Department of Statistics, Stanford University, Stanford, California, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural