ArticleFrontiers in genetics2021
SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- 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
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Editorial: Current Status and Future Challenges of Biobank Data Analysis.Frontiers in genetics · 2022Article
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Authors and funding
8 authors.
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Abstract
The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a
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