Structured gene‐environment interaction analysis

Abstract

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.

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

“…In this paper, we are interested in the identification of lipid-treatment (or environment) interactions through penalization. The success of set based analysis, including those for the gene set [44] and SNP set [45,46], has tremendously motivated the development of statistical methods for G × E interactions from marginal analyses ( [47,48]) to penalization methods [17,18,49]. Our model can be potentially extended in the following aspects.…”

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

“…134 A probabilistic model, Bayesian aggregation of disease genomics and environment (BADGE) was introduced by Li et al 135 They defined the distance the genetic coefficient (C) for a disease, quantified by the Jensen-Shannon (JS) divergence. Wu et al 136 developed a structured gene-environment (G-E) interaction analysis, where such structures are accommodated using penalization for both the main gene effects and interactions.…”

Semiparametric Bayesian variable selection for gene‐environment interactions

“…The binary indicator variables can cause an absorbing state in the MCMC algorithm that violates the convergence condition. 31 To avoid this problem, we integrate out the indicator variables c , v , and e in (7), (9), and (10). We will show that, even though c , v , and e are not part of the MCMC chain, their values still can be easily computed at every iterations.…”

“…Recently, penalized variable selection methods have emerged as a promising tool to capture G×E interactions that might be only weak or moderate individually, but that are strong collectively. [7][8][9][10][11][12] Penalization methods have been first coined in the work of Tibshirani, 13 which has also pointed out the connection between penalization and the corresponding Bayesian variable selection methods. In particular, the LASSO estimate can be interpreted as the posterior mode estimate when identical and independent Laplace prior has been imposed on each component of the coefficient vector under penalized least square loss.…”