Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium - (Author)
  • Department of Psychiatry and Psychotherapy
  • University of Adelaide
  • South Australian Health And Medical Research Institute
  • University of New South Wales
  • Charité – Universitätsmedizin Berlin
  • Dokkyo Medical University
  • University Hospital of Cagliari
  • International University of Catalonia
  • Geneva University Hospitals
  • Karolinska University Hospital
  • University of California San Diego Health
  • Groupe Hospitalier Saint-Louis-Lariboisière-F.Widal
  • Klinikum Klagenfurt am Wörthersee
  • McGill University Health Centre
  • National Taiwan University Hospital
  • University Hospital Basel
  • McGill University
  • University of Medical Sciences Poznan
  • University of Bonn
  • Johns Hopkins University
  • Hospital of the Ludwig-Maximilians-University (LMU) Munich
  • Dalhousie University
  • Alexandru Obregia Clinical Psychiatric Hospital
  • Mood Disorders Center of Ottawa
  • Osaka University
  • University Vascular Centre
  • National Institute of Mental Health (NIMH)
  • Heidelberg University 
  • University of Göttingen
  • National Center for Tumor Diseases (NCT) Heidelberg
  • National Health Research Institutes Taiwan
  • Vrije Universiteit Amsterdam (VU)
  • Medical University of Graz
  • Centre Hospitalier Charles Perrens

Abstract

BACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Details

Original languageEnglish
Pages (from-to)219-228
Number of pages10
JournalBritish Journal of Psychiatry
Volume220
Issue number4
Publication statusE-pub ahead of print - 28 Feb 2022
Peer-reviewedYes

External IDs

Scopus 85126276148
ORCID /0000-0002-3415-5583/work/150329749
ORCID /0000-0002-2666-859X/work/150329167
Mendeley bc63a09d-075d-3a37-8e2d-9ad664d4752f

Keywords