ArticleGenetic epidemiology2017
Accommodating missingness in environmental measurements in gene-environment interaction analysis.
Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
8 citing papers in PubMed.
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Aligned deep neural network for integrative analysis with high-dimensional input.Journal of biomedical informatics · 2023Article
- Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking.Bioinformatics and biology insights · 2023Article
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
- Identifying gene-environment interactions incorporating prior information.Statistics in medicine · 2019Article
- Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
For the prognosis of complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many approaches have been developed for detecting important G-E interactions, most of which assume that measurements are complete. In practical data analysis, missingness in E measurements is not uncommon, and failing to properly accommodate such missingness leads to biased estimation and false marker identification. In this study, we conduct G-E interaction analysis with prognosis data under an accelerated failure time (AFT) model. To accommodate missingness in E measurements, we adopt a nonparametric kernel-based data augmentation approach. With a well-designed weighting scheme, a nice "byproduct" is that the proposed approach enjoys a certain robustness property. A penalization approach, which respects the "main effects, interactions" hierarchy, is adopted for selection (of important interactions and main effects) and regularized estimation. The proposed approach has sound interpretations and a solid statistical basis. It outperforms multiple alternatives in simulation. The analysis of TCGA data on lung cancer and melanoma leads to interesting findings and models with superior prediction.
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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.