PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Complex Trait Genetics

Background:

• Large-scale genomic datasets are increasingly available.
• Existing infrastructure inadequately supports complex analyses beyond Genome-Wide Association Studies (GWAS).
• Need for standardized, centralized tools for multi-omic data analysis in large cohorts.

Purpose of the Study:

• Introduce PLATO (Platform for the Analysis, Translation, and Organization of large-scale data).
• Provide a software tool to handle multi-omic data for hundreds of thousands of samples.
• Enable exploration of complex trait etiology through advanced genetic analyses.

Main Methods:

• Developed PLATO software for multi-omic data integration and analysis.
• Applied PLATO to analyze genetic interactions, environment-wide association studies (EWAS), gene-environment interactions, phenome-wide association studies (PheWAS), and rare variant analyses.
• Utilized data from the Marshfield Personalized Medicine Research Project (eMERGE Network).

Main Results:

• Demonstrated PLATO's capability to manage and analyze large-scale multi-omic data.
• Successfully applied PLATO features to study type 2 diabetes.
• Showcased PLATO's utility in uncovering complex disease etiologies.

Conclusions:

• PLATO provides a standardized and centralized infrastructure for complex trait analysis.
• The software facilitates advanced analyses including genetic interactions and gene-environment interactions.
• PLATO is a valuable tool for understanding the complex etiology of common diseases using large-scale multi-omic data.