This paper reframes genetics from a static risk model to a dynamic stability architecture within the Universal Resonance Model (URM). Rather than asking how genes increase disease probability, it asks how genetic architecture shapes system resilience, recovery dynamics, and sensitivity to perturbation. The paper argues that genetic variants primarily modify the depth and geometry of biological stability states, thereby influencing how easily a system transitions from health to disease. It introduces testable predictions showing that genetic effects should be expressed more strongly in variability, recovery slopes, and nonlinear responses than in average biomarker levels. By integrating genetics with dynamic systems theory, the paper offers a new framework for understanding why similar genetic risk can lead to radically different disease trajectories. This paper is Part I of the series Dynamic Foundations of Disease, which develops a dynamic systems framework for understanding disease across biological levels. This part focuses on genetics as stability architecture within the Universal Resonance Model (URM).
Anita Domargård (Sat,) studied this question.