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Early identification of high-risk individuals through the analysis of their unique disease trajectories has a strong potential to support efficient prevention and clinical management across a range of chronic conditions. In this paper we present a novel approach for dynamic modeling of the evolution of chronic disease risks over time, incorporating individual genetic predispositions. Our approach uses a hierarchical Bayesian topic model including Gaussian Processes to capture age effects. It accounts for genetic predisposition through a time-warping function and topic-dependent genetic scores, enabling both simultaneous learning and updated predictions of complex comorbidity patterns, inclusive of genomic and non-genomic effects. We systematically compare to previous approaches and provide detailed simulations at and .