Protein misfolding and aggregation resulting from latent genome design in response to variation across the human population gives rise to complex systemic and neurodegenerative genotype enabled diseases that require management by proteostasis. We apply interpretable Gaussian process (GP) based covariant agents using machine learning (ML) to address the fundamental spatial covariance (SCV) relationships dictating protein folding and misfolding arising from sequence variation across the world-wide population in rare disease. GP-SCV based reasoning is used to define the impact of the unfolded protein response (UPR) and redox status in response to inflammatory stress triggered by misfolding on a residue-by-residue basis at atomic resolution for genotype challenged disease targets. Capturing relationships through GP-based ML reasoning allows us to generate covariance defined language models using deep phenotype landscapes/barcodes that capture the role of proteostasis in protein structure dynamics and the local environment- providing insight into the differential roles of UPR components managing the onset and progression of disease over a lifespan. Covariance principled modeling addresses causality in response to genetic variation arising from natural selection across the world-wide population and is foundational for understanding origins and evolution.