Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder - Zhang, Li; Pang, Mengqian; Liu, Xiaoyun; Hao, Xiaoke; Wang, Meiling; Xie, Chunming; Zhang, Zhijun; Yuan, Yonggui; Zhang, Daoqiang (Frontiers in Psychiatry, 2023)
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 becomes a powerful strategy to discovery the associations between genetic variants( e.g. single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of existing MDD imaging genetic researches proposed by clinicians usually utilize simple statistical analysis methods and only consider single modality brain imaging, which are limited in 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 develop a novel imaging genetic association framework to mine 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). Then, an association model based on multi-task learning strategy is adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status is introduced to further mine the relation among multiple modalities of different subjects. A multi-modality brain imaging data and genotype data are 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 node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD are discovered.