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Comparative analysis of methods for detecting interacting loci

Li Chen1, Guoqiang Yu1, Carl D Langefeld2, David J Miller3, Richard T Guy2, Jayaram Raghuram3, Xiguo Yuan1, David M Herrington4 and Yue Wang1*

Author Affiliations

1 Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA

2 Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

3 Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA

4 Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA

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BMC Genomics 2011, 12:344  doi:10.1186/1471-2164-12-344

Published: 5 July 2011

Additional files

Additional file 1:

Supplementary information: comparative analysis of methods for detecting interactive SNPs. This supplementary information consists of 6 sections: S1. Section S1 presents our theoretical analysis of the relationship between association strength, joint effect, main effect, penetrance function, and MAF. This section also provides some theoretical explanations about our experimental results. S2. Section S2 presents comprehensive power evaluation results of the methods for different interaction models and parameter settings, related to power definition 1. The reproducibility of the methods is also shown by the standard deviation of power. As an extension of the main text, we also summarize our findings and analytical explanations for these results. S3. Section S3 provides ROC curves of the methods based on the whole ground-truth SNP set. These ROC curves illustrate the sensitivity and specificity for the methods. The reproducibility of the methods is also shown by the standard deviation of sensitivity. S4. Section S4 describes in detail how the effect size (odds ratio) is calculated for each interaction model. S5. Section S5 analyzes the conservativeness of χ2 statistics applied by SH and FIM. This analysis partly explains why SH and FIM are conservative. S6. Section S6 gives the empirical relationship between power and the false positive SNP count under a given significance threshold.

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