Abstract
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.
Highlights
Enabled by recent advances in high-throughput genotyping and multimodal neuroimaging technologies, imaging genetics is becoming an emerging research field for discovering the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from structural or functional neuroimaging data[3,4]
We evaluate the effectiveness of the proposed method by identifying three-way associations among 85 candidate SNPs from the top Alzheimer’s disease (AD) risk gene APOE, 116 imaging QTs extracted from structural magnetic resonance imaging (MRI) scans, and relevant cognitive and diagnostic outcomes, using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data as a test bed
The well-known locus rs429358 is identified to be associated with gray matter loss in multiple AD-relevant regions of interest (ROIs), which is in accordance with the previous studies[24]
Summary
Enabled by recent advances in high-throughput genotyping and multimodal neuroimaging technologies, imaging genetics is becoming an emerging research field for discovering the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from structural or functional neuroimaging data[3,4]. Identification of imaging genetic associations relevant to these diagnostic and cognitive outcomes may yield important information for a better understanding of disease-specific mechanisms With these observations, we consider the outcome-relevant imaging genetic association study as a multi-view multivariate correlation problem, which can be solved by CCA and partial least squares (PLS), as well as their sparse versions (including SCCA and SPLS)[11,12,13,22,23]. We evaluate the effectiveness of the proposed method by identifying three-way associations among 85 candidate SNPs from the top AD risk gene APOE, 116 imaging QTs extracted from structural magnetic resonance imaging (MRI) scans, and relevant cognitive and diagnostic outcomes, using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data as a test bed. The experimental results demonstrate that the proposed T-SCCA model outperforms the standard two-way SCCA method in terms of identifying strong associations, and discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding
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