ArticleNucleic acids research2022
Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evaluation of epistasis detection methods for quantitative phenotypes.Bioinformatics (Oxford, England) · 2026Article
- Genome-wide associations of leaf spectral variation in MAGIC lines of Nicotiana attenuata.Ecology · 2026Article
- Epistasis regulates genetic control of cardiac hypertrophy.Nature cardiovascular research · 2025Article
- Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Genetic Association Studies in Host-Pathogen Interaction Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
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5 authors.
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Abstract
Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.
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