Identification of Genetic Risk Factors Based on Disease Progression Derived From Modeling Longitudinal Phenotype Latent Pattern Representation

The characteristic of neurodegenerative disorders is the progressive impairment of memory and other cognitive functions. However, these existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the latent pattern of the longitudinal data in the progression process. The phenotypes across multiple time-points may exhibit the latent pattern that can be used to facilitate the understanding of the progression process. Accordingly, in this paper, we explore underlying complementary information from multiple time-points and simultaneously seek the underlying latent representation. With the complementarity of multiple time-points, the latent representation depicts data more comprehensively than each individual time-point, therefore mining effective longitudinal phenotype latent pattern representation. Specifically, we first propose two latent pattern representation (LPR) for longitudinal imaging phenotypes: linear LPR (lLPR), based on linear relationships between latent representation and each time-point, and nonlinear LPR (nonlLPR), based on neural networks to deal with nonlinear relationships. Then, we calculate the imaging genetic association based on the latent pattern representation. Finally, we conduct the experiments on both synthetic and real longitudinal imaging genetic data. Related experimental results validate that our proposed approach outperforms several competing algorithms, establishes strong associations, and discovers consistent longitudinal imaging genetic biomarkers, thereby guiding disease interpretation.