Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease
| Autor: | Xiaoke, Hao, Chanxiu, Li, Lei, Du, Xiaohui, Yao, Jingwen, Yan, Shannon L, Risacher, Andrew J, Saykin, Li, Shen, Daoqiang, Zhang, Kristin, Fargher |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Multivariate analysis Imaging genetics Datasets as Topic Gene Expression Neuroimaging Single-nucleotide polymorphism Computational biology Quantitative trait locus Biology computer.software_genre Polymorphism Single Nucleotide Severity of Illness Index Article Cohort Studies 03 medical and health sciences Apolipoproteins E 0302 clinical medicine Alzheimer Disease Data Mining Humans Cognitive Dysfunction Genetic Predisposition to Disease Genetic Association Studies Multidisciplinary Brain Magnetic Resonance Imaging Phenotype 030104 developmental biology Genetic marker Multivariate Analysis Data mining Canonical correlation computer Algorithms 030217 neurology & neurosurgery Alzheimer's Disease Neuroimaging Initiative |
| Zdroj: | Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/srep44272 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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