Prediction of probable major depressive disorder in the taiwan biobank: An integrated machine learning and genome-wide analysis approach

Eugene Lin*, Po Hsiu Kuo, Wan Yu Lin, Yu Li Liu, Albert C. Yang, Shih Jen Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

Original languageEnglish
Article number597
JournalJournal of Personalized Medicine
Volume11
Issue number7
DOIs
StatePublished - Jul 2021

Keywords

  • Genome-wide association study
  • Machine learning
  • Major depressive disorder
  • Personalized medicine
  • Single nucleotide polymorphisms

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