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Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder
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....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017727/ https://www.ncbi.nlm.nih.gov/pubmed/36937715 http://dx.doi.org/10.3389/fpsyt.2023.1139451 |
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author | Zhang, Li Pang, Mengqian Liu, Xiaoyun Hao, Xiaoke Wang, Meiling Xie, Chunming Zhang, Zhijun Yuan, Yonggui Zhang, Daoqiang |
author_facet | Zhang, Li Pang, Mengqian Liu, Xiaoyun Hao, Xiaoke Wang, Meiling Xie, Chunming Zhang, Zhijun Yuan, Yonggui Zhang, Daoqiang |
author_sort | Zhang, Li |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10017727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100177272023-03-17 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 Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2023-03-02 /pmc/articles/PMC10017727/ /pubmed/36937715 http://dx.doi.org/10.3389/fpsyt.2023.1139451 Text en Copyright © 2023 Zhang, Pang, Liu, Hao, Wang, Xie, Zhang, Yuan and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Zhang, Li Pang, Mengqian Liu, Xiaoyun Hao, Xiaoke Wang, Meiling Xie, Chunming Zhang, Zhijun Yuan, Yonggui Zhang, Daoqiang Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title | Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title_full | Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title_fullStr | Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title_full_unstemmed | Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title_short | Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
title_sort | incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017727/ https://www.ncbi.nlm.nih.gov/pubmed/36937715 http://dx.doi.org/10.3389/fpsyt.2023.1139451 |
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