GEInfo：一个 R 包，用于进行包含先验信息的基因-环境交互作用分析。
GEInfo: an R package for gene-environment interaction analysis incorporating prior information.
机构信息
College of Finance and Statistics, Hunan University, Changsha 410079, Hunan, China.
Department of Biostatistics, Yale University, New Haven, CT 06520, USA.
出版信息
Bioinformatics. 2022 May 26;38(11):3139-3140. doi: 10.1093/bioinformatics/btac301.
SUMMARY
Gene-environment (G-E) interactions have important implications for many complex diseases. With higher dimensionality and weaker signals, G-E interaction analysis is more challenged than the analysis of main G (and E) effects. The accumulation of published literature makes it possible to borrow strength from prior information and improve analysis. In a recent study, a 'quasi-likelihood + penalization' approach was developed to effectively incorporate prior information. Here, we first extend it to linear, logistic and Poisson regressions. Such models are much more popular in practice. More importantly, we develop the R package GEInfo, which realizes this approach in a user-friendly manner. To facilitate direct comparison and routine data analysis, the package also includes functions for alternative methods and visualization.
AVAILABILITY AND IMPLEMENTATION
The package is available at https://CRAN.R-project.org/package=GEInfo.
SUPPLEMENTARY INFORMATION
Supplementary materials are available at Bioinformatics online.
摘要
基因-环境（G-E）相互作用对许多复杂疾病具有重要意义。与主要 G（和 E）效应的分析相比，G-E 相互作用分析具有更高的维度和更弱的信号，因此更具挑战性。已发表文献的积累使得从先前信息中借用力量并改进分析成为可能。在最近的一项研究中，开发了一种“拟似然+惩罚”方法来有效地纳入先前信息。在这里，我们首先将其扩展到线性、逻辑和泊松回归。在实践中，这些模型更为流行。更重要的是，我们开发了 R 包 GEInfo，它以用户友好的方式实现了这种方法。为了便于直接比较和常规数据分析，该包还包括替代方法和可视化的功能。
可用性和实现
该软件包可在 https://CRAN.R-project.org/package=GEInfo 获得。
补充信息
补充材料可在生物信息学在线获得。
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