Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders
文献类型：期刊论文
| 作者 | Di, Yazheng2,3; Wang, Jingying1; Liu, Xiaoqian2,3 ; Zhu, Tingshao2,3
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| 刊名 | FRONTIERS IN GENETICS
 |
| 出版日期 | 2021-12-20 |
| 卷号 | 12页码:9 |
| 关键词 | biomarkers polygenic risk score (PRS) computer technology major depressive disorder (MDD) voice biomarkers depression |
| DOI | 10.3389/fgene.2021.761141 |
| 通讯作者 | Zhu, Tingshao(tszhu@psych.ac.cn) |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| WOS关键词 | MENTAL-DISORDERS ; GENETIC RISK ; DISEASE ; EPIDEMIOLOGY ; PREDICTION ; MODELS ; TWIN |
| 资助项目 | Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2019-4] |
| WOS研究方向 | Genetics & Heredity |
| 语种 | 英语 |
| WOS记录号 | WOS:000738857100001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | Key Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.psych.ac.cn/handle/311026/41783]  |
| 专题 | 心理研究所_中国科学院行为科学重点实验室 |
| 通讯作者 | Zhu, Tingshao |
| 作者单位 | 1.Hong Kong Polytech Univ, Fac Hlth & Social Sci, Sch Optometry, Hong Kong, Peoples R China 2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Di, Yazheng,Wang, Jingying,Liu, Xiaoqian,et al. Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders[J]. FRONTIERS IN GENETICS,2021,12:9. |
| APA | Di, Yazheng,Wang, Jingying,Liu, Xiaoqian,&Zhu, Tingshao.(2021).Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders.FRONTIERS IN GENETICS,12,9. |
| MLA | Di, Yazheng,et al."Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders".FRONTIERS IN GENETICS 12(2021):9. |
入库方式： OAI收割
来源：心理研究所
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