CAPE

CAPE infers directed epistatic interactions and pleiotropy by integrating multiple related phenotypes to construct predictive genetic networks for quantitative traits.

Key Features:

• Integration of Multiple Phenotypes: Leverages information from multiple related phenotypes to construct models that elucidate directed epistatic interactions between genetic markers and constrain epistatic hypotheses across phenotypic contexts.
• Predictive Genetic Networks: Generates predictive, interpretable genetic networks for quantitative traits that represent directed influences, including suppressive and enhancing effects between genetic variants.
• Application Across Diverse Populations: Applies to data from engineered and natural populations, including backcrosses, intercrosses, and segregating populations.
• Utility Demonstrated in Research: Has been applied to a mouse backcross study where it identified novel epistatic interactions affecting obesity- and diabetes-related phenotypes.

Scientific Applications:

• Dissection of Complex Trait Architecture: Models epistasis and pleiotropy across multiple phenotypes to dissect the genetic architecture of complex traits.
• Mapping Variant Interactions: Explores dependencies between genetic variants and their directed effects on multiple phenotypes to identify suppressive or enhancing interactions.
• Domain-Specific Analyses: Supports analyses in genomics, evolutionary biology, and medical genetics linking genetic interactions to phenotypic variation.

Methodology:

Leverages multiple related phenotype measurements to constrain epistatic models and constructs models that infer directed epistatic interactions between genetic markers, producing predictive genetic networks.