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Structured gene‐environment interaction analysis
Biometrics ( IF 1.9 ) Pub Date : 2019-10-09 , DOI: 10.1111/biom.13139
Mengyun Wu 1, 2 , Qingzhao Zhang 3 , Shuangge Ma 2
Affiliation  

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 the higher dimensionality and need for accommodating the "main effects, interactions" hierarchy. In the 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 SNPs (attributable to their physical adjacency on the chromosomes) and network structure of gene expressions (attributable to their coordinated biological functions and correlated measurements), have not been well accommodated. In this study, we develop the 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 the consistency properties under high dimensional settings. Simulations and the analysis of GENEVA diabetes data with SNP measurements and TCGA melanoma data with gene expression measurements demonstrate its competitive practical performance. This article is protected by copyright. All rights reserved.

中文翻译:

结构化基因-环境相互作用分析

对于复杂疾病的病因、进展和治疗,基因-环境 (GE) 相互作用具有超出主要 G 和 E 效应的重要意义。GE 交互分析可能更具挑战性,因为维度更高,并且需要适应“主效应、交互”层次结构。在最近的文献中,已经开发了一系列新方法,其中许多基于惩罚技术。然而,在大多数这些研究中,G 测量的结构,例如 SNP 的邻接结构(归因于它们在染色体上的物理邻接)和基因表达的网络结构(归因于它们协调的生物学功能和相关测量),具有没有被很好地容纳。在这项研究中,我们开发了结构化的 GE 交互分析,其中对主要 G 效应和相互作用使用惩罚来适应这种结构。惩罚也适用于正则化的估计和选择。可以有效地实现所提出的结构化交互分析。它被证明在高维设置下具有一致性属性。GENEVA 糖尿病数据与 SNP 测量和 TCGA 黑色素瘤数据与基因表达测量的模拟和分析证明了其具有竞争力的实际性能。本文受版权保护。版权所有。它被证明在高维设置下具有一致性属性。GENEVA 糖尿病数据与 SNP 测量和 TCGA 黑色素瘤数据与基因表达测量的模拟和分析证明了其具有竞争力的实际性能。本文受版权保护。版权所有。它被证明在高维设置下具有一致性属性。对 GENEVA 糖尿病数据与 SNP 测量和 TCGA 黑色素瘤数据与基因表达测量的模拟和分析证明了其具有竞争力的实际性能。本文受版权保护。版权所有。
更新日期:2019-10-09
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