Research and applications: An information-gain approach to detecting three-way epistatic interactions in genetic association studies |
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Authors: | Ting Hu Yuanzhu Chen Jeff W Kiralis Ryan L Collins Christian Wejse Giorgio Sirugo Scott M Williams Jason H Moore |
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Institution: | 1.Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA;2.Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, USA;3.Department of Computer Science, Memorial University, St. John''s, Newfoundland, Canada;4.Center for Global Health, School of Public Health, Aarhus University, Skejby, Denmark;5.Centro di Genetica, Centro di Ricerca Scientifica, Ospedale San Pietro FBF, Rome, Italy |
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Abstract: | BackgroundEpistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.ObjectivesIn this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.MethodsSuch a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.ResultsOur method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.ConclusionOur study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies. |
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Keywords: | epistasis information gain gene-gene interaction high-order interaction genetic association studies |
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