Linked Life Data

17425620

Source:http://linkedlifedata.com/resource/pubmed/id/17425620

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 5
pubmed:dateCreated
2007-7-24
pubmed:abstractText
To identify interacting loci in genetic epidemiological studies the application of multi-locus methods of analysis is warranted. Several more advanced classification methods have been developed in the past years, including multiple logistic regression, sum statistics, logic regression, and the multifactor dimensionality reduction method. The objective of our study was to apply these four multi-locus methods to simulated case-control datasets that included a variety of underlying statistical two-locus interaction models, in order to compare the methods and evaluate their strengths and weaknesses. The results showed that the ability to identify the interacting loci was generally good for the sum statistic method, the logic regression and MDR. The performance of the logistic regression was more dependent on the underlying model and multiple comparison adjustment procedure. However, identification of the interacting loci in a model with two two-locus interactions of common disease alleles with relatively small effects was impaired in all methods. Several practical and methodological issues that can be considered in the application of these methods, and that may warrant further research, are identified and discussed.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0003-4800
pubmed:author
pubmed:issnType
Print
pubmed:volume
71
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
689-700
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Application of multi-locus analytical methods to identify interacting loci in case-control studies.
pubmed:affiliation
Department of Endocrinology, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. h.vermeulen@endo.umcn.nl
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural