BACKGROUND Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). RESULTS In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the 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 uses 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. CONCLUSIONS The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

中文翻译：

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GenEpi：使用机器学习发现基于基因的上位性。

背景技术全基因组关联研究（GWAS）提供了一种强有力的手段来鉴定遗传变异与表型之间的关联。然而，用于检测上位性，与表型相关的遗传变异之间的相互作用的GWAS技术仍然受到限制。我们认为，开发一种有效且有效的GWAS方法来检测上皮癌将是发现复杂发病机制的关键，这对于诸如阿尔茨海默氏病（AD）的复杂疾病尤其重要。结果在这方面，本研究提出了GenEpi，这是一种通过拟议的机器学习方法来揭示与表型相关的上位性的计算程序包。GenEpi通过两阶段建模工作流程识别基因内和跨基因上位。在两个阶段中 GenEpi在生成特征时采用两元素组合编码，并通过具有稳定性选择的L1正则化回归来构建预测模型。仿真数据表明，GenEpi在检测地面真相上的性能优于其他广泛使用的方法。就实际数据而言，本研究以AD为例，揭示了GenEpi在发现与疾病相关的变体和变体相互作用方面具有生物学意义和预测能力的能力。结论在模拟数据和AD上的结果表明GenEpi具有有效和高效地检测与表型相关的上皮性的能力。可以广泛使用已发布的软件包，以在很大程度上促进近期内对许多复杂疾病的研究。