Robust Gene-Environment Interaction Analysis

For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), ; Mengyun Wu et al (2017), ; Yaqing Xu et al (2018), ; Yaqing Xu et al (2019), ; Mengyun Wu et al (2021), ).


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.3.1 by Xing Qin, 4 months ago

Browse source code at

Authors: Mengyun Wu [aut] , Xing Qin [aut, cre] , Shuangge Ma [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports survAUC, MASS, splines, pcaPP, Hmisc, survival, quantreg, reshape2, ggplot2, stats, graphics

See at CRAN