An information-gain approach to detecting three-way epistatic interactions in genetic association studies

作者：Hu Ting; Chen Yuanzhu; Kiralis Jeff W; Collins Ryan L; Wejse Christian; Sirugo Giorgio; Williams Scott M; Moore Jason H^*

来源：Journal of the American Medical Informatics Association, 2013, 20(4): 630-636.

DOI：10.1136/amiajnl-2012-001525

Background Epistasis 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. %26lt;br%26gt;Objectives In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis. %26lt;br%26gt;Methods Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis. %26lt;br%26gt;Results Our 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. %26lt;br%26gt;Conclusion Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.

• 出版日期2013-7