Title
An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation.
Abstract
BACKGROUND: To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. RESULTS: The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. CONCLUSIONS: The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.
Year
DOI
Venue
2004
10.1186/1471-2105-5-49
BMC Bioinformatics
Keywords
Field
DocType
prediction error,type i error,multifactor dimensionality reduction,genetics,risk assessment,angiotensin converting enzyme,gene polymorphism,genotype,myocardial infarction,microarrays,myocardial infarct,gene expression regulation,model validation,polymorphism,bioinformatics,cross validation,algorithms
Biology,Multifactor dimensionality reduction,Tissue plasminogen activator,Epistasis,Risk assessment,Plasminogen activator,Angiotensin-converting enzyme,Regulation of gene expression,Bioinformatics,Genetics,Cross-validation
Journal
Volume
Issue
ISSN
5
1
1471-2105
Citations 
PageRank 
References 
17
2.22
1
Authors
9