Zhang Yuan, Biswas Swati
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.
Cancer Inform. 2015 Feb 9;14(Suppl 2):11-6. doi: 10.4137/CIN.S17290. eCollection 2015.
The importance of haplotype association and gene-environment interactions (GxE) in the context of rare variants has been underlined in voluminous literature. Recently, a software based on logistic Bayesian LASSO (LBL) was proposed for detecting GxE, where G is a rare (or common) haplotype variant (rHTV)-it is called LBL-GxE. However, it required relatively long computation time and could handle only one environmental covariate with two levels. Here we propose an improved version of LBL-GxE, which is not only computationally faster but can also handle multiple covariates, each with multiple levels. We also discuss details of the software, including input, output, and some options. We apply LBL-GxE to a lung cancer dataset and find a rare haplotype with protective effect for current smokers. Our results indicate that LBL-GxE, especially with the improvements proposed here, is a useful and computationally viable tool for investigating rare haplotype interactions.
在大量文献中，单倍型关联以及基因-环境相互作用（GxE）在罕见变异背景下的重要性已得到强调。最近，有人提出了一种基于逻辑贝叶斯套索（LBL）的软件来检测GxE，其中G是一种罕见（或常见）的单倍型变异（rHTV）——它被称为LBL-GxE。然而，它需要相对较长的计算时间，并且只能处理具有两个水平的一个环境协变量。在此，我们提出了LBL-GxE的一个改进版本，它不仅计算速度更快，而且还能处理多个协变量，每个协变量都有多个水平。我们还讨论了该软件的细节，包括输入、输出和一些选项。我们将LBL-GxE应用于一个肺癌数据集，并发现一种对当前吸烟者具有保护作用的罕见单倍型。我们的结果表明，LBL-GxE，尤其是结合此处提出的改进，是研究罕见单倍型相互作用的一个有用且在计算上可行的工具。
