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.

成像遗传学揭示了微观遗传学和宏观成像之间的联系，从而能够识别疾病生物标志物。在这项工作中，我们充分利用对研究大脑与遗传学之间的相关性具有重要参考价值的先验知识来探索更多具有生物学意义的生物标志物。在本文中，我们提出了基于联合连通性的稀疏非负矩阵分解（JCB-SNMF）。该算法将结构磁共振成像（sMRI）、单核苷酸多态性位点（SNPs）和基因表达数据同时投影到一个共同的特征空间上，在同一投影方向上具有大系数的异质变量形成一个共同的模块。此外，大脑每个区域的连接信息和遗传数据作为先验知识添加，以识别与阿尔茨海默病 (AD) 患者相关的感兴趣区域 (ROI)、SNP 和基因相关风险。GraphNet 正则化提高了算法的抗噪性能和结果的生物可解释性。仿真结果表明，与其他基于NMF的算法（JNMF、JSNMNMF）相比，JCB-SNMF具有更好的抗噪声性能，可以从重要模块中识别和预测与AD密切相关的生物标志物。通过构建蛋白质-蛋白质相互作用 (PPI) 网络，我们将 SF3B1、RPS20 和 RBM14 确定为 AD 的潜在生物标志物。我们还发现了一些重要的 SNP-ROI 和基因-ROI 对。其中，两个SNP rs4472239和rs11918049和三个基因KLHL8、ZC3H11A，OSGEPL1 可能对多个大脑区域的灰质体积产生影响。该模型为进一步整合多模式影响遗传数据以识别复杂的疾病关联模式提供了一种新方法。