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Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders
Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721147/ https://www.ncbi.nlm.nih.gov/pubmed/34987547 http://dx.doi.org/10.3389/fgene.2021.761141 |
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author | Di, Yazheng Wang, Jingying Liu, Xiaoqian Zhu, Tingshao |
author_facet | Di, Yazheng Wang, Jingying Liu, Xiaoqian Zhu, Tingshao |
author_sort | Di, Yazheng |
collection | PubMed |
description | Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples. Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability. Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance. Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD. |
format | Online Article Text |
id | pubmed-8721147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87211472022-01-04 Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders Di, Yazheng Wang, Jingying Liu, Xiaoqian Zhu, Tingshao Front Genet Genetics Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples. Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability. Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance. Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD. Frontiers Media S.A. 2021-12-20 /pmc/articles/PMC8721147/ /pubmed/34987547 http://dx.doi.org/10.3389/fgene.2021.761141 Text en Copyright © 2021 Di, Wang, Liu and Zhu. 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 | Genetics Di, Yazheng Wang, Jingying Liu, Xiaoqian Zhu, Tingshao Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title | Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title_full | Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title_fullStr | Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title_full_unstemmed | Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title_short | Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders |
title_sort | combining polygenic risk score and voice features to detect major depressive disorders |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721147/ https://www.ncbi.nlm.nih.gov/pubmed/34987547 http://dx.doi.org/10.3389/fgene.2021.761141 |
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