ArticleBiometrics2020
Structured gene-environment interaction analysis.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Joint modeling of mixed outcomes using a rank-based sparse neural network.Journal of biomedical informatics · 2025Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.Statistics in medicine · 2025Article
- Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.Human genomics · 2025Review
- Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects.Plants (Basel, Switzerland) · 2025Review
- Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.Computational statistics & data analysis · 2024Article
- Gene-environment interaction analysis under the Cox model.Annals of the Institute of Statistical Mathematics · 2023Article
- Article
- Locally sparse quantile estimation for a partially functional interaction model.Computational statistics & data analysis · 2023Article
- Gene-environment interaction analysis via deep learning.Genetic epidemiology · 2023Article
- Multidimensional molecular measurements-environment interaction analysis for disease outcomes.Biometrics · 2022Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data.BMC bioinformatics · 2022Article
- The early-life exposome modulates the effect of polymorphic inversions on DNA methylation.Communications biology · 2022Article
- A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data.Genes · 2022Article
- Integrative functional linear model for genome-wide association studies with multiple traits.Biostatistics (Oxford, England) · 2022Article
- A scalable hierarchical lasso for gene-environment interactions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- SNPxE: SNP-environment interaction pattern identifier.BMC bioinformatics · 2021Article
- Semiparametric Bayesian variable selection for gene-environment interactions.Statistics in medicine · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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.