College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China.
Bioinformatics. 2022 Apr 12;38(8):2323-2332. doi: 10.1093/bioinformatics/btac074.
As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis.
In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer's Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation.
The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main.
Supplementary data are available at Bioinformatics online.
作为一个新兴的研究课题，脑影像遗传学旨在研究大脑结构和功能的潜在遗传结构。需要注意的是，在大脑中，并非所有的变异都应归因于遗传效应，而且通常不清楚哪些成像表型适合进行遗传分析。
在这项工作中，可以将遗传变异（即单核苷酸多态性，SNP）与脑网络（即定量特征，QT）相关联，从而识别来自功能磁共振成像数据的功能脑网络的连接组（包括脑区和连接特征）。具体来说，首先构建一个连接矩阵，其上三角元素被选为连接特征。然后，利用 PageRank 算法来估计不同脑区作为脑区特征的重要性。最后，开发了一种深度自重建稀疏典型相关分析（DS-SCCA）方法，用于识别与功能连接表型标记的遗传关联。这种方法是一种正则化的、深度扩展的、可扩展的多 SNP-多 QT 方法，非常适合将影像遗传关联分析应用于阿尔茨海默病神经影像学倡议数据集。通过采用参数方法、增广拉格朗日法和随机梯度下降法对其进行了进一步优化。通过广泛的实验验证了 DS-SCCA 方法实现了强关联，并发现了功能连接和脑区表型生物标志物，以指导疾病解释。
Matlab 代码可在 https://github.com/meimeiling/DS-SCCA/tree/main 获得。
补充数据可在生物信息学在线获得。
