Literature DB >> 35485739

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.

Xiaoyan Wang1, Hongduo Liu1, Shuangge Ma2.   

Abstract

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: The package is available at https://CRAN.R-project.org/package=GEInfo. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.
© The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Year:  2022        PMID: 35485739      PMCID: PMC9154264          DOI: 10.1093/bioinformatics/btac301

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.931


  6 in total

1.  Identification of gene-environment interactions in cancer studies using penalization.

Authors:  Jin Liu; Jian Huang; Yawei Zhang; Qing Lan; Nathaniel Rothman; Tongzhang Zheng; Shuangge Ma
Journal:  Genomics       Date:  2013-08-29       Impact factor: 5.736

Review 2.  Robust genetic interaction analysis.

Authors:  Mengyun Wu; Shuangge Ma
Journal:  Brief Bioinform       Date:  2019-03-25       Impact factor: 11.622

3.  Identifying gene-environment interactions incorporating prior information.

Authors:  Xiaoyan Wang; Yonghong Xu; Shuangge Ma
Journal:  Stat Med       Date:  2019-01-13       Impact factor: 2.373

4.  A LASSO FOR HIERARCHICAL INTERACTIONS.

Authors:  Jacob Bien; Jonathan Taylor; Robert Tibshirani
Journal:  Ann Stat       Date:  2013-06       Impact factor: 4.028

5.  Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

Authors:  Yuan Jiang; Yunxiao He; Heping Zhang
Journal:  J Am Stat Assoc       Date:  2016-05-05       Impact factor: 5.033

6.  Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.

Authors:  Kimberly McAllister; Leah E Mechanic; Christopher Amos; Hugues Aschard; Ian A Blair; Nilanjan Chatterjee; David Conti; W James Gauderman; Li Hsu; Carolyn M Hutter; Marta M Jankowska; Jacqueline Kerr; Peter Kraft; Stephen B Montgomery; Bhramar Mukherjee; George J Papanicolaou; Chirag J Patel; Marylyn D Ritchie; Beate R Ritz; Duncan C Thomas; Peng Wei; John S Witte
Journal:  Am J Epidemiol       Date:  2017-10-01       Impact factor: 5.363

  6 in total

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