Six Degrees of Epistasis: Statistical Network Models for GWAS

Area of Science:

• Genetics
• Bioinformatics
• Systems Biology

Background:

• Growing evidence suggests a larger portion of the genome influences complex trait heritability than previously understood.
• Common variants across the genome explain some genetic variability, but significant heritability remains unexplained.
• Pathway and gene set enrichment techniques show promise in detecting coordinated gene action at the population level.

Purpose of the Study:

• To explore the potential of network paradigms to explain additional genetic variation.
• To investigate gene-gene interactions and main effects of common variants within a network framework.
• To discuss opportunities and challenges of network methods for genome-wide association studies (GWAS).

Main Methods:

• Reviewing existing literature on genetic heritability and polygenic structures.
• Applying a network paradigm to analyze gene-gene interactions and common variant effects.
• Considering the impact of network connectivity structures like hubs and motifs.

Main Results:

• Gene-gene interactions, like single-locus contributions, are expected to have small effects individually.
• Network hubs and connectivity structures can reinforce the effects of multiple gene interactions.
• Network approaches offer potential for uncovering hidden variation in GWAS data.

Conclusions:

• Network methods can potentially explain additional genetic variation contributing to complex phenotypes.
• Analyzing gene networks may improve our understanding of disease mechanisms.
• Network paradigms could offer new insights into evolutionary genetics.