Detection of gene–environment interactions in a family‐based population using SCAD

Xue

et al. 2019

Genetic Epidemiology

Gene–environment (G–E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood‐based (and other “standard”) estimation with each marginal model, and then select significant G–E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the “main effects, interactions” hierarchy, which has been stressed in recent G–E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of “non‐normal” distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance‐based analysis and simple penalization‐based alternatives. Promising findings are made in the analysis of a single‐nucleotide polymorphism and a gene expression data.

b_{j, k} \neq 0

for any

k \geq 2

(i.e., an interaction term), then

b_{j, 1} \neq 0

(i.e., the main G effect).…”

Section: Methodsmentioning

confidence: 99%

Identification of gene–environment interactions with marginal penalization

Xue

et al. 2019

Genetic Epidemiology

“…Interactions and main G effects corresponding to the nonzero components of 𝑏 ′ 𝑗 s are considered as important. The estimation-based identification strategy, which has been extensively adopted in joint analysis (Kim et al, 2017;Liu et al, 2013) but only a few marginal analysis , is notably different from the significance-based one. It is also noted that selection of E variables is not conducted, as in many studies, E variables are "preselected" and are all of interest.…”

Section: Marginal Analysismentioning

confidence: 99%

Gene–environment interaction identification via penalized robust divergence

Ren

et al. 2021

Biometrical J

“…In many practical problems, however, the main effects alone may not be enough to capture the relationship between the response and predictors, and higher-order interactions are often of interest to scientific researchers. For example, many complex diseases, such as cancer, involve multiple genetic and environmental risk factors, and scientists are particularly interested in assessing gene-gene and gene-environment interactions [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A modified least angle regression algorithm for interaction selection with heredity

Kim

et al. 2022

Statistical Analysis

Self Cite

In many practical problems, the main effects alone may not be enough to capture the relationship between the response and predictors, and the interaction effects are often of interest to scientific researchers. In considering a regression model with main effects and all possible two‐way interaction effects, which we call the two‐way interaction model, there is an important challenge—computational burden. One way to reduce the aforementioned problems is to consider the heredity constraint between the main and interaction effects. The heredity constraint assumes that a given interaction effect is significant only when the corresponding main effects are significant. Various sparse penalized methods to reflect the heredity constraint have been proposed, but those algorithms are still computationally demanding and can be applied to data where the dimension of the main effects is only few hundreds. In this paper, we propose a modification of the LARS algorithm for selecting interaction effects under the heredity constraint, which can be applied to high‐dimensional data. Our numerical studies confirm that the proposed modified LARS algorithm is much faster and spends less memory than its competitors but has comparable prediction accuracies when the dimension of covariates is large.