所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：SCIENTIFIC REPORTS
关键字：NEUROIMAGING INITIATIVE ADNI QUANTITATIVE TRAIT LOCI COGNITIVE IMPAIRMENT COMPOSITE SCORE MENTAL-STATE PHENOTYPES REGRESSION ATROPHY MEMORY COHORT
摘要：Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.
ISSN号：2045-2322
是否译文：否
发表时间：2017-03-14
合写作者：Hao, Xiaoke,Li, Chanxiu,Du, Lei,Yao, Xiaohui,Yan, Jingwen,Risacher, Shannon L.,Saykin, Andrew J.,Shen, Li
通讯作者：张道强
发表时间：2017-03-14