With the increasing availability of genetic data, the detection of epistasis associated with diseases has become an important topic in the medical field. In this study, we have proposed a machine-learning-based epistasis detection method called GenEpi. GenEpi adopts two-stage modeling to identify both within-gene and cross-gene epistasis. In each stage, GenEpi adopts two-element combinatorial encoding when constructing features, and constructs prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study used AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.
As for the limitation, this study did not provide any evidence to support the biological meanings of the identified epistasis. With the support of the experimental verification, we will explore the biological meanings of the identified epistasis in our future studies. Furthermore, the construction of the rigorous epistasis detection model and the extension of this study to other complex diseases or genetic diseases will be our future work.
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