Abstract
Detecting gene–gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case–control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene–gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.
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Acknowledgements
JM Mahachie John is a doctoral student funded by the Belgian Network BioMAGNet (Bioinformatics and Modeling: from Genomes to Networks), within the Interuniversity Attraction Poles Program (Phase VI/4), initiated by the Belgian State, Science Policy Office. We acknowledge research opportunities offered by the Belgian Network BioMAGNet and partial support by the IST Program of the European Community, under the PASCAL2 Network of Excellence (Pattern Analysis, Statistical Modeling and Computational Learning), IST-2007-216886. We also acknowledge the valuable discussions with Tom Cattaert (a Postdoctoral Researcher of the Fonds de la Recherche Scientifique – FNRS) on data generation and variance decomposition sections. In addition, F Van Lishout acknowledges support by Alma in silico, funded by the European Commission and Walloon Region through the Interreg IV Program.
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Mahachie John, J., Van Lishout, F. & Van Steen, K. Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. Eur J Hum Genet 19, 696–703 (2011). https://doi.org/10.1038/ejhg.2011.17
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DOI: https://doi.org/10.1038/ejhg.2011.17
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