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Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

Authors
Journal
BMC Bioinformatics
1471-2105
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
9
Issue
1
Identifiers
DOI: 10.1186/1471-2105-9-238
Keywords
  • Research Article
Disciplines
  • Biology
  • Computer Science
  • Medicine

Abstract

1471-2105-9-238.fm ral ss BioMed CentBMC Bioinformatics Open AcceResearch article Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction William S Bush1, Todd L Edwards1, Scott M Dudek1, Brett A McKinney2 and Marylyn D Ritchie*1 Address: 1Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA and 2Department of Genetics, University of Alabama School of Medicine, Birmingham, Alabama, USA Email: William S Bush - [email protected]; Todd L Edwards - [email protected]; Scott M Dudek - [email protected]; Brett A McKinney - [email protected]; Marylyn D Ritchie* - [email protected] * Corresponding author Abstract Background: Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality. Results: In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad- sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and speci

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