Semiparametric integrative interaction analysis for non-small-cell lung cancer

Li, Yang; Wang, Fan; Li, Rong; Sun, Yifan

doi:10.1177/0962280220909969

Cited by 10 publications

(10 citation statements)

References 26 publications

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“…A common limitation in these studies is that the within‐subject correlation has not been taken into account. From the perspective of G

\times

E interactions, the time‐varying effects investigated in these studies can be viewed as nonlinear G

\times

E interactions (Y. Li et al, 2020; Ma & Xu, 2015; Ma et al, 2011; Wu & Cui, 2013; Wu et al, 2015, Wu, Zhou, et al, 2018). In our study, the interaction effects are modeled as the product between G and E factors, which is under the linear G

\times

E interaction assumption (Zhou, Ren, et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Sparse group variable selection for gene–environment interactions in the longitudinal study

Zhou

Ren

et al. 2022

Genetic Epidemiology

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Penalized variable selection for high‐dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi‐level gene–environment (G × $\times $ E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G × $\times $ E study by using high‐dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

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“…A common limitation in these studies is that the within‐subject correlation has not been taken into account. From the perspective of G

\times

E interactions, the time‐varying effects investigated in these studies can be viewed as nonlinear G

\times

\times

E interaction assumption (Zhou, Ren, et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Sparse group variable selection for gene–environment interactions in the longitudinal study

Zhou

Ren

et al. 2022

Genetic Epidemiology

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show abstract

“…A common limitation in these studies is that the within-subject correlation has not been taken into account. From the perspective of G×E interactions, the time varying effects investigated in these studies can be viewed as nonlinear G×E interactions [42][43][44][45][46][47] . In our study, the interaction effects is modeled as the product between G and E factors, which is under the linear G×E interaction assumption 9 .…”

Section: Discussionmentioning

confidence: 99%

Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study

Zhou

Ren

et al. 2019

Genes

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Lipid species are critical components of eukaryotic membranes. They play key roles in many biological processes such as signal transduction, cell homeostasis, and energy storage. Investigations of lipid–environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related changes in phenotype. In this study, we developed a novel penalized variable selection method to identify important lipid–environment interactions in a longitudinal lipidomics study. An efficient Newton–Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. We conducted extensive simulation studies to demonstrate the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. As weight control via dietary calorie restriction and exercise has been demonstrated to prevent cancer in a variety of studies, analysis of the high-dimensional lipid datasets collected using 60 mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects.

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“…To satisfy the identification restriction, the basis functions are constrained to have mean values of zero, that is,

\sum_{i = 1}^{n^{m}} ϕ (X_{i j}^{m}) = 0

for

j = 1, \dots, p

and

m = 1, \dots, M

. In the numerical study, we adopt the normalized B‐spline basis function, which has been widely used in published studies, 21,22 and note that other basis functions are also be applicable. Then model () can be rewritten as

y_{i}^{m} \sim g (\sum_{j = 1}^{p} ϕ (X_{i j}^{m}) β_{j}^{m}) .

…”

Section: Methodsmentioning

confidence: 99%

“…In the numerical study, we adopt the normalized B-spline basis function, which has been widely used in published studies, 21,22 and note that other basis functions are also be applicable. Then model (1) can be rewritten as…”

Section: Data and Model Settingsmentioning

confidence: 99%

Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting

Luo

Sun

2022

Statistics in Medicine

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In many biomedical problems, data are often heterogeneous, with samples spanning multiple patient subgroups, where different subgroups may have different disease subtypes, stages, or other medical contexts. These subgroups may be related, but they are also expected to have differences with respect to the underlying biology. The heterogeneous data presents a precious opportunity to explore the heterogeneities and commonalities between related subgroups. Unfortunately, effective statistical analysis methods are still lacking. Recently, several novel methods based on integrative analysis have been proposed to tackle this challenging problem. Despite promising results, the existing studies are still limited by ignoring data contamination and making strict assumptions of linear effects of covariates on response. As such, we develop a robust nonparametric integrative analysis approach to identify heterogeneity and commonality, as well as select important covariates and estimate covariate effects. Possible data contamination is accommodated by adopting the Cauchy loss function, and a nonparametric model is built to accommodate nonlinear effects. The proposed approach is based on a sparse boosting technique. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on glioblastoma multiforme and lung adenocarcinoma shows that the proposed approach makes biologically meaningful findings with satisfactory prediction.

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Semiparametric integrative interaction analysis for non-small-cell lung cancer

Cited by 10 publications

References 26 publications

Sparse group variable selection for gene–environment interactions in the longitudinal study

Sparse group variable selection for gene–environment interactions in the longitudinal study

Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study

Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting

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