Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders - Di, Yazheng; Wang, Jingying; Liu, Xiaoqian; Zhu, Tingshao (Frontiers in Genetics, 2021)
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 3580 women with recurrent MDD and 4016 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-threshold of 0.005, the combined biomarker improved the Area Under Curve (AUC) by 9.09% compared to gene only and 6.73% compared to voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests show 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.