Robust genetic interaction analysis

Wu, Mengyun; Ma, Shuangge

doi:10.1093/bib/bby033

Cited by 28 publications

(19 citation statements)

References 68 publications

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“…It has been demonstrated that LAD is suitable for heavy‐tailed distributions, especially double‐exponential distributions, and the Huber loss has good performance for contaminated normal distributions . However, no method can perform universally better than the other . The boosting technique with the Huber loss and LAD for low‐dimensional main effect analysis has been studied in the literature .…”

Section: Discussionmentioning

confidence: 99%

“…12,50 However, no method can perform universally better than the other. 13 The boosting technique with the Huber loss and LAD for low-dimensional main effect analysis has been studied in the literature. 19 The Huber loss is observed to perform well with both normal and slash errors, whereas LAD has limitations with normal errors.…”

Section: Discussionmentioning

confidence: 99%

“…For high‐dimensional genetic studies, there are some, but still limited, developments . The existing high‐dimensional robust studies are mostly limited to main effects, and there is still insufficient attention to genetic interactions . The second limitation of the existing approaches is that they usually assume linear effects.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12] The existing high-dimensional robust studies are mostly limited to main effects, and there is still insufficient attention to genetic interactions. 13 The second limitation of the existing approaches is that they usually assume linear effects. In low-dimensional biomedical studies, it has been shown that, in many occasions, nonlinear covariate effects are present.…”

Section: Introductionmentioning

confidence: 99%

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Robust semiparametric gene‐environment interaction analysis using sparse boosting

2019

Statistics in Medicine

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For the pathogenesis of complex diseases, gene‐environment (G‐E) interactions have been shown to have important implications. G‐E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the “main effects, interactions” hierarchical constraint. Extensive methodological developments on G‐E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long‐tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects, and the Huber loss function and Qn estimator are adopted to accommodate long‐tailed distributions/data contamination. A regression‐based multiple imputation approach is developed to accommodate missingness in E variables. For model estimation and selection of relevant variables, we adopt an effective sparse boosting approach. The proposed approach is practically well motivated, has intuitive formulations, and can be effectively realized. In extensive simulations, it significantly outperforms multiple direct competitors. The analysis of The Cancer Genome Atlas data on stomach adenocarcinoma and cutaneous melanoma shows that the proposed approach makes sensible discoveries with satisfactory prediction and stability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust semiparametric gene‐environment interaction analysis using sparse boosting

2019

Statistics in Medicine

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“…Among the available techniques, penalization has been popular in recent studies. See Wu and Ma () and the references therein. Another challenge comes from the need to respect the “main effects, interactions” hierarchy (Bien et al ., ; Hao et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Structured gene‐environment interaction analysis

Zhang

2019

Biometrics

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For the etiology, progression, and treatment of complex diseases, gene‐environment (G‐E) interactions have important implications beyond the main G and E effects. G‐E interaction analysis can be more challenging with higher dimensionality and need for accommodating the “main effects, interactions” hierarchy. In recent literature, an array of novel methods, many of which are based on the penalization technique, have been developed. In most of these studies, however, the structures of G measurements, for example, the adjacency structure of single nucleotide polymorphisms (SNPs; attributable to their physical adjacency on the chromosomes) and the network structure of gene expressions (attributable to their coordinated biological functions and correlated measurements) have not been well accommodated. In this study, we develop structured G‐E interaction analysis, where such structures are accommodated using penalization for both the main G effects and interactions. Penalization is also applied for regularized estimation and selection. The proposed structured interaction analysis can be effectively realized. It is shown to have consistency properties under high‐dimensional settings. Simulations and analysis of GENEVA diabetes data with SNP measurements and TCGA melanoma data with gene expression measurements demonstrate its competitive practical performance.

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