IdopNetworks: How to infer the individualized genetic architecture of genomics for precision medicine

Genome-wide association studies suffer from two major limitations - reductionist statistics and population-level averaging - both of which hinder actionable insights for precision medicine. To bridge these gaps, we introduce a statistical mechanics model that integrates all genetic variants into informative, dynamic, omnidirectional, and personalized networks (idopNetworks), formalized through quasi-dynamic mixed ordinary differential equations. By synthesizing functional mapping, allometric scaling, evolutionary game theory, and modularity theory, this framework shifts the paradigm from reductionist statistics to an omnigenic interactome reconstructed from multi-omics data. The model translates population statistics into patient-specific genomic maps, empowering clinicians to predict individual drug responses and guide multi-site polygenic editing. Ultimately, idopNetworks move therapeutics beyond population averages, offering a scalable tool to design targeted genetic interventions for personalized medicine.