Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Yazheng Di; Jingying Wang; Xiaoqian Liu; Tingshao Zhu

doi:10.3389/fgene.2021.761141

Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Front Genet. 2021 Dec 20:12:761141. doi: 10.3389/fgene.2021.761141. eCollection 2021.

Authors

Yazheng Di^{1

2}, Jingying Wang³, Xiaoqian Liu^{1

2}, Tingshao Zhu^{1

2}

Affiliations

¹ Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
² Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
³ School of Optometry, Faculty of Health and Social Sciences, Hong Kong Polytechnic University, Hong Kong, China.

Abstract

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.

Keywords: biomarkers; computer technology; depression; major depressive disorder (MDD); polygenic risk score (PRS); voice biomarkers.