An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer.
An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer.
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DOI：
10.4137/cin.s17290
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发表时间：
2015
影响因子：
2
通讯作者：
Biswas S
中科院分区：
文献类型：
--
作者：
Zhang Y;Biswas S
单倍型关联和基因-环境相互作用(GxE)在稀有变异背景下的重要性已在大量文献中得到强调。最近，一个基于Logistic贝叶斯套索(LBL)的软件被提出用于检测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。但该算法计算时间较长，且只能处理一个两水平的环境协变量。这里我们提出了一种改进的LBL-GxE，它不仅计算速度快，而且可以处理多个协变量，每个协变量有多个水平。我们还讨论了软件的细节，包括输入、输出和一些选项。我们将LBL-GxE应用于肺癌数据集，发现了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，特别是在这里提出的改进，是一个有用的和计算上可行的工具来研究罕见的单倍型相互作用。
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.