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Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

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Abstract

Imaging genetics reveals the connection between microscopic genetics and macroscopic imaging, enabling the identification of disease biomarkers. In this work, we make full use of prior knowledge that has significant reference value for investigating the correlation between the brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose joint-connectivity-based sparse nonnegative matrix factorization (JCB-SNMF). The algorithm simultaneously projects structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information for each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROIs), SNPs, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a protein–protein interaction (PPI) network, we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also found some significant SNP-ROI and gene–ROI pairs. Among them, two SNPs rs4472239 and rs11918049 and three genes KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

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sMRI, SNPs, and gene expression data of patients with Alzheimer disease and controls were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/).

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Shanghai (No. 18ZR1417200) and National Natural Science Foundation of China (No. 61803257).

Funding

The Natural Science Foundation of Shanghai (No. 18ZR1417200) and the National Natural Science Foundation of China (No. 61803257).

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  1. Kai Wei and Wei Kong should be regarded as joint first authors.

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  1. College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, P. R. China

    Kai Wei, Wei Kong & Shuaiqun Wang

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Conception and design of the research: Kai Wei and Wei Kong. Acquisition, analysis, and interpretation of data: Kai Wei, Shuaiqun Wang, and Wei Kong. Statistical analysis: Kai Wei. Drafting of the manuscript: Kai Wei. Manuscript revision for important intellectual content: Wei Kong. All authors have read and approved the manuscript.

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Correspondence to Wei Kong.

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Wei, K., Kong, W. & Wang, S. Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization. J Mol Neurosci 72, 255–272 (2022). https://doi.org/10.1007/s12031-021-01888-6

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