Providing context and interpretability to genetic association analysis results using the KGraph

Area of Science:

• Genetics
• Bioinformatics
• Data Visualization

Background:

• Understanding complex genetic architectures requires visualizing associations between outcomes, covariates, and genetic variations like single-nucleotide polymorphisms (SNPs).
• Existing methods may not efficiently display the multidimensional correlation structures crucial for genetic association studies.

Purpose of the Study:

• To introduce KGraph, a novel data visualization system designed to display intricate relationships in genetic association studies.
• To facilitate the simultaneous interpretation of genetic associations, covariate correlations, and validation information.

Main Methods:

• Development of the KGraph system for visualizing univariate and bivariate associations.
• Integration of genetic variations (SNPs), covariates, and outcome data within the visualization framework.
• Emphasis on visualizing multicollinearity, confounding, gene-environment, and gene-gene interactions.

Main Results:

• KGraph enables easy simultaneous viewing and interpretation of genetic associations and their underlying structures.
• The system aids in investigating multicollinearity and confounding through visualization of multidimensional correlation.
• Facilitates exploration of gene-environment and gene-gene interactions critical for human traits.

Conclusions:

• KGraph is a valuable tool for gene-centric studies and can be integrated into broader association analysis workflows.
• The visualization system simplifies the interpretation of complex genetic data, supporting research into the genetic architecture of human traits.
• The software promotes a deeper understanding of genetic associations and their influencing factors.