Abstract
Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain’s biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene–ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.
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MMSE, sMRI, SNPs, and gene expression data of patients with Alzheimer’s disease and controls were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/).
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This work was supported by the National Natural Science Foundation of China (No. 61803257) and the Natural Science Foundation of Shanghai (No. 18ZR1417200).
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Conception and design of the research: Shuaiqun Wang and Yafei Qian. Acquisition, analysis, and interpretation of data: Shuaiqun Wang, Yafei Qian, Kai Wei, and Wei Kong. Statistical analysis: Shuaiqun Wang and Yafei Qian. Drafting the manuscript: Shuaiqun Wang and Yafei Qian. Manuscript revision for important intellectual content: Shuaiqun Wang, Kai Wei, and Wei Kong. All authors have read and approved the manuscript.
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Wang, S., Qian, Y., Wei, K. et al. Identifying Biomarkers of Alzheimer’s Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach. J Mol Neurosci 72, 323–335 (2022). https://doi.org/10.1007/s12031-021-01915-6
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DOI: https://doi.org/10.1007/s12031-021-01915-6