Gene–environment interaction identification via penalized robust divergence

Ren, Mingyang; Zhang, Sanguo; Ma, Shuangge; Zhang, Qingzhao

doi:10.1002/bimj.202000157

Cited by 2 publications

(3 citation statements)

References 59 publications

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“…Robustness to outliers and model misspecification is critical for the success of G × E studies. Although adopting robust loss/likelihood functions can safeguard long-tailed distributions in phenotypes [ 10 , 12 ], the varying coefficient models and their extended family have been systematically examined to capture G × E interactions beyond the linear assumption [ 50 , 51 , 52 ]. In repeated-measurement studies, the identification of nonlinear interactions within the GEE and QIF frameworks is also robust to misspecifications of working correlations.…”

Section: Discussionmentioning

confidence: 99%

“…The identification of important genetic main effects and G × E interactions are of primary interest since the E factors are usually determined based on existing studies and are not subject to selection. This essentially requires sparse group or bi-level selection [ 10 , 11 , 12 ]. With the repeated measure response, although PGEE and PQIF can still be adopted for G × E interactions by treating interactions as the main effects, they no longer lead to the optimal estimation and identification results [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

Zhou

Ren

Liu

et al. 2022

Genes

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We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

Zhou

Ren

Liu

et al. 2022

Genes

View full text Add to dashboard Cite

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“…Further detection of the main and/or interaction effects demands selection within the group. Such bi-level variable selection methods have been extensively studies under continuous, binary, and survival outcomes in G×E studies ( Wu et al, 2018a ; Ren et al, 2022a ; Ren et al, 2022b ; Liu et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data

et al. 2023

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In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene–environment interaction studies. This study introduces “springer,” an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package “springer” has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the “springer” package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package “springer” is available at https://cran.r-project.org/package=springer.

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Gene–environment interaction identification via penalized robust divergence

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to their computational complexity.

Cited by 2 publications

References 59 publications

Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data

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