Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.
Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.
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DOI：
10.3389/fpsyt.2023.1139451
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发表时间：
2023
影响因子：
4.7
通讯作者：
Zhang, Daoqiang
中科院分区：
文献类型：
--
作者：
Zhang, Li;Pang, Mengqian;Liu, Xiaoyun;Hao, Xiaoke;Wang, Meiling;Xie, Chunming;Zhang, Zhijun;Yuan, Yonggui;Zhang, Daoqiang
关键词：
抑郁症（重度抑郁症，MDD）是一种常见的严重医学疾病。据估计，全球有5%的成年人患有抑郁症。最近，成像遗传学受到越来越多的关注，并成为一个强大的策略，用于研究遗传变异之间的关联（例如，单核苷酸多态性，SNP）和多模态脑成像数据。然而，现有的大多数由临床医生进行的MDD成像遗传学研究通常使用简单的统计分析方法，并且仅考虑单一模态的脑成像，这限制了对MDD机制理解的更深层次的发现。因此，必须利用强大而有效的技术来充分探索遗传变异与多模态脑成像之间的关联。在这项研究中，我们开发了一种新的成像遗传关联框架，以挖掘遗传风险变体和多阶段诊断状态之间的多模态表型网络。具体而言，多模态表型网络由来自结构磁共振成像（sMRI）和静息状态功能磁共振成像（rs-fMRI）的体素节点特征和连接边缘特征组成。随后，采用基于多任务学习策略的关联模型，充分探索MDD风险SNP与多模态表型网络之间的关系。引入多阶段诊断状态，进一步挖掘不同学科多模态之间的关联。我们从两家医院收集了多模态脑成像数据和基因型数据。实验结果不仅证明了该方法的有效性，而且从多模态表型网络的节点和边缘特征中识别出了一些一致且稳定的脑感兴趣区域（ROI）生物标志物。此外，还发现了4个与MDD相关的新的潜在风险SNP。
Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.
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影响因子：
3
作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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影响因子：
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