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A0351
Title: A regression framework of integrating genetic, imaging, and clinical data with applications Authors:  Ting Li - Shanghai University of Finance and Economics (China) [presenting]
Abstract: The motivation comes from the joint analysis of genetic, imaging, and clinical (GIC) data collected in many large-scale biomedical studies, such as the UK Biobank study and the Alzheimer's disease neuroimaging initiative (ADNI) study. We propose a regression framework based on partially functional linear regression models to map high-dimensional GIC-related pathways for many phenotypes of interest. We develop a joint model selection and estimation procedure by embedding imaging data in the reproducing kernel Hilbert space and imposing the $\ell_0$ penalty for the coefficients of scalar variables. We systematically investigate the theoretical properties of scalar and functional efficient estimators, including non-asymptotic error bound, minimax error bound, and asymptotic normality. We apply the proposed method to the ADNI dataset to identify important features from a large number of genetic polymorphisms and study the effects of a certain set of informative genetic variants and the surface data on the clinical outcomes.