FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.

Area of Science:

• Genetics
• Systems Biology
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for understanding the genetic basis of complex traits.
• Existing methods often struggle to capture the intricate regulatory interactions within large-scale biological networks.
• Dynamic phenotypic traits, influenced by time-varying genetic and environmental factors, present unique analytical challenges.

Purpose of the Study:

• To introduce FunGraph, a novel statistical protocol for reconstructing and dissecting functional omnigenic multilayer interactome networks.
• To enable the analysis of how individual genetic loci influence phenotypic variation through both direct and indirect regulatory effects within complex networks.
• To provide a versatile tool applicable to any GWAS focused on characterizing the genetic architecture of dynamic phenotypic traits.

Main Methods:

• Development of a statistical protocol named FunGraph.
• Reconstruction of multilayer interactome networks.
• Dissection of functional networks to identify direct and indirect genetic effects.
• Application to genome-wide association studies (GWAS) for dynamic traits.

Main Results:

• FunGraph enables the comprehensive analysis of genetic architecture in GWAS.
• The protocol effectively dissects direct and indirect effects of loci on phenotypic variation.
• Demonstrates applicability to complex dynamic traits mediated by large-scale networks.

Conclusions:

• FunGraph offers a robust statistical framework for dissecting complex genetic architectures in GWAS.
• The protocol advances the understanding of how omnigenic networks mediate dynamic phenotypic traits.
• Facilitates deeper insights into the genetic underpinnings of complex diseases and traits.