ArticleStatistics in medicine2014
Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.Statistics in medicine · 2026Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023Article
- Locally sparse quantile estimation for a partially functional interaction model.Computational statistics & data analysis · 2023Article
- Article
- Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits.Statistics in medicine · 2022Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Integrative functional linear model for genome-wide association studies with multiple traits.Biostatistics (Oxford, England) · 2022Article
- Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.Genes · 2022Article
- Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.Frontiers in genetics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
- Structured gene-environment interaction analysis.Biometrics · 2020Article
- Semiparametric Bayesian variable selection for gene-environment interactions.Statistics in medicine · 2020Article
- Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study.Genes · 2019Article
- Penalized integrative semiparametric interaction analysis for multiple genetic datasets.Statistics in medicine · 2019Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.BMC genetics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Consider the integrative analysis of genetic data with multiple correlated response variables. The goal is to identify important gene-environment (G × E) interactions along with main gene and environment effects that are associated with the responses. The homogeneity and heterogeneity models can be adopted to describe the genetic basis of multiple responses. To accommodate possible nonlinear effects of some environment effects, a multi-response partially linear varying coefficient model is assumed. Penalization is adopted for marker selection. The proposed penalization method can select genetic variants with G × E interactions, no G × E interactions, and no main effects simultaneously. It adopts different penalties to accommodate the homogeneity and heterogeneity models. The proposed method can be effectively computed using a coordinate descent algorithm. Simulation study and the analysis of Health Professionals Follow-up Study, which has two correlated continuous traits, SNP measurements and multiple environment effects, show superior performance of the proposed method over its competitors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.