文献详情
Identification of gene-environment interactions with marginal penalization
文献类型：期刊
通讯作者：Ma, SG (reprint author), Yale Univ, Dept Biostat, New Haven, CT 06520 USA.
期刊名称：GENETIC EPIDEMIOLOGY影响因子和分区
年：2020
卷：44
期：2
页码：159-196
ISSN：0741-0395
关键词：gene-environment interaction; marginal analysis; penalization
所属部门：统计学院
摘要：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 stress ...More
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. ...Hide

DOI：10.1002/gepi.22270
百度学术：Identification of gene-environment interactions with marginal penalization
语言：外文
基金：Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [20720171064, 20720181003]; University of Chinese Academy of SciencesChinese Academy of Sciences [Y95401TXX2]; Humanity and Social Science Youth Foundation of Ministry of Education of China [19YJC910010]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P50CA121974, R01CA204120]
作者其他论文



Identification of cancer omics commonality and difference via community fusion.Sun, Yifan, Jiang, Yu, Li, Yang, et al. .STATISTICS IN MEDICINE. 2019, 38(7), 1200-1212.
Integrative interaction analysis using threshold gradient directed regularization.Li, Yang, Li, Rong, Qin, Yichen, et al. .APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. 2019, 35(2,SI), 354-375.
Integrative interaction analysis using threshold gradient directed regularization.Li, Yang, Li, Rong, Qin, Yichen, et al. .APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. 2019, 35(2,SI), 354-375.
Medical expenditure for middle-aged and elderly in Beijing.Ma, Chenjin, Jiang, Yan, Li, Yang, et al. .BMC HEALTH SERVICES RESEARCH. 2019, 19.
A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis.He, Shengmei, Ma, Shuangge, Xu, Wangli,.COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2019, 137, 155-169.