Abstract

Info

Title:: Sparse representation-based variable selection algorithm for analysis of imaging genetics data

Author(s):: XIE Zhongxiang; WU Jie; XIANG Huazhong; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Keywords:: schizophrenia sparse representation variable selection algorithm single nucleotide polymorphisms functional magnetic resonance imaging

Abstract:: Objective To explore the imaging genetic characteristics of schizophrenia using imaging genetics method. Methods A sparse representation-based variable selection algorithm with improved ability of variable selection under different norm conditions based on traditional sparse regression model is proposed. The proposed algorithm was applied for the comprehensive analysis of 41 236 functional magnetic resonance imaging data and 722 177 single nucleotide polymorphisms data of 208 subjects. By applying different weight factors to the two types of data and using different Lp (p=0, 0.5, 1) norms for solving the models, the significant features of the two types of data were extracted. Results DAOA and HTR2A genes were extracted under 3 different Lp norms. In addition, the results of imaging data suggested that precentral, occipital_sup, parietal_inf, angular, cingulum_mid, cingulum_post were associated with schizophrenia, which was consistent with previous clinical studies on schizophrenia. Conclusion Sparse representation-based variable selection algorithm is an effective and feasible approach for the analysis of image genetics data, providing a new direction for the image genetics study on schizophrenia.