Eagle

Eagle performs multi-locus association mapping on genome-wide genotype-phenotype data to detect SNP-trait associations using model selection, linear mixed models, and random effects.

Key Features:

• R implementation: Provided as an R package for computational analysis of genetic association data.
• Multi-locus association mapping: Tests associations across multiple loci genome-wide rather than relying on single-locus tests.
• Linear mixed models: Applies linear mixed models to account for population structure and relatedness among individuals.
• Model selection: Integrates model selection techniques to identify significant SNP-trait associations.
• Random effects modeling: Incorporates random effects to capture additional genetic variance and refine association signals.
• Enhanced detection power: Improves detection of SNP-trait associations compared to conventional single-locus methods.
• GWAS scale: Designed for genome-wide association study (GWAS) analyses.

Scientific Applications:

• Genome-wide association studies (GWAS): Identification of SNP-trait associations while accounting for confounding due to structure and relatedness.
• Complex trait mapping: Detection of multi-locus genetic interactions and subtle genetic influences on phenotypes.
• Mouse genetics: Analysis of real mouse genotype-phenotype datasets to refine and extend single-locus findings.
• Refinement of single-locus results: Complementing single-locus analyses by identifying additional loci and interactions.

Methodology:

Eagle integrates model selection with linear mixed models and random-effects modeling to account for population structure and relatedness when identifying significant SNP-trait associations.