Literature DB >> 29897421

Robust genetic interaction analysis.

Mengyun Wu1, Shuangge Ma1.   

Abstract

For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  genetic interaction; model mis-specification; outlier/contamination; robustness

Mesh:

Year:  2019        PMID: 29897421      PMCID: PMC6556899          DOI: 10.1093/bib/bby033

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  11 in total

1.  Structured gene-environment interaction analysis.

Authors:  Mengyun Wu; Qingzhao Zhang; Shuangge Ma
Journal:  Biometrics       Date:  2019-10-09       Impact factor: 2.571

2.  Robust gene-environment interaction analysis using penalized trimmed regression.

Authors:  Yaqing Xu; Mengyun Wu; Shuangge Ma; Syed Ejaz Ahmed
Journal:  J Stat Comput Simul       Date:  2018-09-19       Impact factor: 1.424

3.  Robust semiparametric gene-environment interaction analysis using sparse boosting.

Authors:  Mengyun Wu; Shuangge Ma
Journal:  Stat Med       Date:  2019-07-29       Impact factor: 2.373

4.  Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.

Authors:  Xing Qin; Shuangge Ma; Mengyun Wu
Journal:  Stat Med       Date:  2021-09-20       Impact factor: 2.373

5.  Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction.

Authors:  Nourollah Ahmadi
Journal:  Methods Mol Biol       Date:  2022

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

Authors:  Xiaoyan Wang; Hongduo Liu; Shuangge Ma
Journal:  Bioinformatics       Date:  2022-04-29       Impact factor: 6.931

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

Authors:  Mengyun Wu; Xing Qin; Shuangge Ma
Journal:  Bioinformatics       Date:  2021-05-07       Impact factor: 6.937

8.  Gene-environment interaction identification via penalized robust divergence.

Authors:  Mingyang Ren; Sanguo Zhang; Shuangge Ma; Qingzhao Zhang
Journal:  Biom J       Date:  2021-11-01       Impact factor: 1.715

9.  Histopathological Imaging⁻Environment Interactions in Cancer Modeling.

Authors:  Yaqing Xu; Tingyan Zhong; Mengyun Wu; Shuangge Ma
Journal:  Cancers (Basel)       Date:  2019-04-24       Impact factor: 6.639

10.  Effects of GSTA1 and GPX3 Polymorphisms on the Risk of Schizophrenia in Chinese Han Population.

Authors:  Chao Liu; Sijia Song; Junkai Zhang; Xiao Li; Huijie Gao
Journal:  Neuropsychiatr Dis Treat       Date:  2020-01-09       Impact factor: 2.570

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