epiMEIF

epiMEIF detects high-order epistatic interactions among single nucleotide polymorphisms (SNPs) to elucidate genetic contributions to complex quantitative phenotypes.

Key Features:

• Higher-Order Epistasis Detection: Identifies n-way epistatic interactions beyond pairwise associations that are often missed by traditional Genome-wide association studies (GWAS).
• Mixed Effect Conditional Inference Forests: Employs mixed effect conditional inference forests that integrate mixed effects models with conditional inference trees.
• Tree Structure Utilization: Leverages the tree structure within the forest to detect intricate n-way interactions among SNPs.
• Robust Testing Strategies: Incorporates additional testing strategies to assess the statistical significance and robustness of detected interactions.
• Simulation Validation: Validated through simulations on cross-sectional and longitudinal synthetic datasets to confirm detection of true n-way interactions.
• Application to Real-World Data: Applied to natural variation data of cardiac traits in Drosophila to reveal epistatic interactions contributing to phenotypic diversity.

Scientific Applications:

• Genomic architecture analysis: Dissects the genetic architecture of complex traits by identifying interacting loci that influence phenotypic variation.
• GWAS augmentation: Enhances GWAS analyses by detecting high-order SNP interactions not captured by standard single-marker approaches.
• Multifactorial disease research: Supports studies of multifactorial diseases and traits by mapping complex epistatic relationships among loci.
• Drosophila cardiac trait studies: Applied to natural variation in Drosophila cardiac traits to uncover epistatic contributors to phenotype diversity.
• Translational insights: Informs research that may contribute to improved diagnostic, prognostic, and therapeutic strategies for complex conditions.

Methodology:

Implements mixed effect conditional inference forests by integrating mixed effects models with conditional inference trees; uses tree structures to identify n-way SNP interactions; applies additional testing strategies for significance assessment; validated via simulations on cross-sectional and longitudinal synthetic datasets and applied to Drosophila cardiac trait data.