ArticleGenetic epidemiology2013
SBERIA: set-based gene-environment interaction test for rare and common variants in complex diseases.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes.PloS one · 2022Pooled it
- Variance-component-based meta-analysis of gene-environment interactions for rare variants.G3 (Bethesda, Md.) · 2021Pooled it
- Gene-environment interactions and colorectal cancer risk: An umbrella review of systematic reviews and meta-analyses of observational studies.International journal of cancer · 2019Pooled it
- A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.Genetic epidemiology · 2018Pooled it
- Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.G3 (Bethesda, Md.) · 2025Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Gene-environment interactions within a precision environmental health framework.Cell genomics · 2024Review
- Efficient gene-environment interaction testing through bootstrap aggregating.Scientific reports · 2023Article
- A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.PLoS genetics · 2022Article
- A two-stage testing strategy for detecting genes×environment interactions in association studies.G3 (Bethesda, Md.) · 2021Article
- Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks.Genes · 2021Article
- Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene.Frontiers in genetics · 2020Article
- Polygenic approaches to detect gene-environment interactions when external information is unavailable.Briefings in bioinformatics · 2019Review
- Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants.PLoS genetics · 2019Article
- Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.Frontiers in genetics · 2018Article
- Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.Biometrika · 2017Article
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.American journal of epidemiology · 2017Article
- A unified powerful set-based test for sequencing data analysis of GxE interactions.Biostatistics (Oxford, England) · 2017Article
- A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes.Frontiers in neuroscience · 2017Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Identification of gene-environment interaction (G × E) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated G × E findings compared to the success in marginal association studies. The existing G × E testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a set-based gene-environment interaction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to tell signals from noise and how to determine the direction of the signals. SBERIA takes advantage of the established correlation screening for G × E to guide the aggregation of genotypes within a marker set. The correlation screening has been shown to be an efficient way of selecting potential G × E candidate SNPs in case-control studies for complex diseases. Importantly, the correlation screening in case-control combined samples is independent of the interaction test. With this desirable feature, SBERIA maintains the correct type I error level and can be easily implemented in a regular logistic regression setting. We showed that SBERIA had higher power than benchmark methods in various simulation scenarios, both for common and rare variants. We also applied SBERIA to real genome-wide association studies (GWAS) data of 10,729 colorectal cancer cases and 13,328 controls and found evidence of interaction between the set of known colorectal cancer susceptibility loci and smoking.
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