ArticleGenetics2015
Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes.PloS one · 2022Pooled it
- A unified method for rare variant analysis of gene-environment interactions.Statistics in medicine · 2020Pooled it
- A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.Genetic epidemiology · 2018Pooled it
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Genetic heterogeneity in cardiovascular disease across ancestries: Insights for mechanisms and therapeutic intervention.Cambridge prisms. Precision medicine · 2023Review
- A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.PLoS genetics · 2022Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- Review
- SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data.Frontiers in genetics · 2021Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- Oxytocin Receptor Gene (OXTR) and Deviant Peer Affiliation: A Gene-Environment Interaction in Adolescent Antisocial Behavior.Journal of youth and adolescence · 2019Article
- Elucidating Gene-by-Environment Interactions Associated with Differential Susceptibility to Chemical Exposure.Environmental health perspectives · 2018Article
- On the substructure controls in rare variant analysis: Principal components or variance components?Genetic epidemiology · 2018Article
- Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.American journal of epidemiology · 2017Article
- Joint genetic analysis using variant sets reveals polygenic gene-context interactions.PLoS genetics · 2017Article
- A New Method for Detecting Associations with Rare Copy-Number Variants.PLoS genetics · 2015Article
- A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Genetic epidemiology · 2015Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.
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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.