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

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.
Fichier principal
Vignette du fichier
gkac715.pdf (3.09 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

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

Licence

Attribution - NonCommercial

Identifiers

Cite

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⟩
25 View
63 Download

Altmetric

Share

Gmail Facebook X LinkedIn More