Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests - Archive ouverte HAL Access content directly
Journal Articles Nucleic Acids Research Year : 2022

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests

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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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Dates and versions

hal-03800774 , version 1 (06-10-2022)

Licence

Attribution - NonCommercial - CC BY 4.0

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Saswati Saha, Laurent Perrin, Laurence Röder, Christine Brun, Lionel Spinelli. Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests. Nucleic Acids Research, 2022, pp.gkac715. ⟨10.1093/nar/gkac715⟩. ⟨hal-03800774⟩
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