Literature DB >> 33961050

GEInter: an R package for robust gene-environment interaction analysis.

Mengyun Wu1, Xing Qin1, Shuangge Ma2.   

Abstract

SUMMARY: For understanding complex diseases, gene-environment (G-E) interactions have important implications beyond main G and E effects. Most of the existing analysis approaches and software packages cannot accommodate data contamination/long-tailed distribution. We develop GEInter, a comprehensive R package tailored to robust G-E interaction analysis. For both marginal and joint analysis, for data without and with missingness, for continuous and censored survival responses, it comprehensively conducts identification, estimation, visualization, and prediction. It can fill an important gap in the existing literature and enjoy broad applicability.
AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/web/packages/GEInter/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) (2021). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Year:  2021        PMID: 33961050      PMCID: PMC8545291          DOI: 10.1093/bioinformatics/btab318

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

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9.  Accommodating missingness in environmental measurements in gene-environment interaction analysis.

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10.  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

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  2 in total

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2.  Feature screening for survival trait with application to TCGA high-dimensional genomic data.

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