Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder
Abstract
Background The predictive power of polygenic scores (PGSs) for lithium treatment response in bipolar disorder remains limited. Aims To enhance the prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or bipolar disorder aetiology. Method We analysed data collected from bipolar disorder patients who had received lithium treatment for at least 6 months and participated in the International Consortium on Lithium Genetics study ( N = 2367). The Alda scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous total Alda score (0–10) and categorical outcome (favourable ≥7 versus unfavourable response). PGSs were calculated for 60 phenotypes grouped into 5 clinical–biological clusters: psychiatric and behavioural (#23 phenotypes), cardiometabolic (#17), autoimmune/inflammatory (#5), neurocognitive (#8) and renal function (#7). We applied cross-validated machine learning regression approaches in both outcomes within each cluster, with the selected features from each cluster subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance ( R 2 ) for the continuous outcome, and both McFadden’s pseudo- R 2 and standard classification model parameters for the categorical outcome. Results mt-PGS explained between 5.70% (continuous outcome) and 9.11% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (area under the curve) for the categorical outcome was 68.24% (95% CI: 64.98–71.66), with a Brier score of 0.257. Of the 5 clusters, the PGSs for psychiatric and behavioural phenotypes were most strongly associated with lithium responsiveness, accounting for 3.67–6.52% of its variability. Conclusions By integrating PGSs for multiple relevant phenotypes, predictive accuracy for lithium response improved substantially compared with single-trait methods. Future research incorporating larger, more diverse populations and combining genetic scores with clinical data holds promise for further enhancing prediction and advancing clinical implementation.
Related
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder
- Association of neurotransmitter-based polygenic scores with antidepressant treatment outcomes in patients with major depressive disorder
- Polygenic Risk Profiles and the Manifestations of Alcohol Use Disorder and Related Outcomes
- Attention‐deficit/hyperactivity disorder polygenic risk score and incidence of cognitive impairment in older adults: A prospective multicohort study
- Diabetes Polygenic Scores Predict Glycemic Indices and Insulin Use in Individuals with Atypical Diabetes
BibTeX
@article{sharew2026using,
title = {{Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder}},
author = {Nigussie Sharew and Scott Clark and Simon Hartmann and Sergi Papiol and Urs Heilbronner and Franziska Degenhardt and Janice Fullerton and Liping Hou and Tatyana Shekhtman and Mazda Adli and Nirmala Akula and Kazufumi Akiyama and Raffaella Ardau and Bárbara Arias and Fasil Tekola-Ayele and Carla Gallo and Roland Hasler and Hélène Richard-Lepouriel and Nader Perroud and Lena Backlund and Abesh Bhattacharjee and Frank Bellivier and Antonio Benabarre and Susanne Bengesser and Joanna Biernacka and Armin Birner and Cynthia Marie-Claire and Pablo Cervantes and Hsi-Chung Chen and Caterina Chillotti and Sven Cichon and Cristiana Cruceana and Piotr Czerski and Nina Dalkner and Maria Del Zompo and J Raymond Depaulo and Bruno Étain and Stéphane Jamain and Peter Falkai and Andreas Forstner and Louise Frisen and Mark Frye and Sebastien Gard and Julie Garnham and Fernando Goes and Maria Grigoroiu-Serbanescu and Andreas Fallgatter and Sophia Stegmaier and Thomas Ethofer and Silvia Biere and Kristiyana Petrova and Ceylan Schuster and Kristina Adorjan and Monika Budde and Maria Heilbronner and Janos Kalman and Mojtaba Kohshour and Daniela Reich-Erkelenz and Sabrina Schaupp and Eva Schulte},
journal = {British Journal of Psychiatry},
year = {2026},
month = oct,
doi = {10.1192/bjp.2026.10776},
url = {https://hal.science/hal-05797726v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder
AU - Nigussie Sharew
AU - Scott Clark
AU - Simon Hartmann
AU - Sergi Papiol
AU - Urs Heilbronner
AU - Franziska Degenhardt
AU - Janice Fullerton
AU - Liping Hou
AU - Tatyana Shekhtman
AU - Mazda Adli
AU - Nirmala Akula
AU - Kazufumi Akiyama
AU - Raffaella Ardau
AU - Bárbara Arias
AU - Fasil Tekola-Ayele
AU - Carla Gallo
AU - Roland Hasler
AU - Hélène Richard-Lepouriel
AU - Nader Perroud
AU - Lena Backlund
AU - Abesh Bhattacharjee
AU - Frank Bellivier
AU - Antonio Benabarre
AU - Susanne Bengesser
AU - Joanna Biernacka
AU - Armin Birner
AU - Cynthia Marie-Claire
AU - Pablo Cervantes
AU - Hsi-Chung Chen
AU - Caterina Chillotti
AU - Sven Cichon
AU - Cristiana Cruceana
AU - Piotr Czerski
AU - Nina Dalkner
AU - Maria Del Zompo
AU - J Raymond Depaulo
AU - Bruno Étain
AU - Stéphane Jamain
AU - Peter Falkai
AU - Andreas Forstner
AU - Louise Frisen
AU - Mark Frye
AU - Sebastien Gard
AU - Julie Garnham
AU - Fernando Goes
AU - Maria Grigoroiu-Serbanescu
AU - Andreas Fallgatter
AU - Sophia Stegmaier
AU - Thomas Ethofer
AU - Silvia Biere
AU - Kristiyana Petrova
AU - Ceylan Schuster
AU - Kristina Adorjan
AU - Monika Budde
AU - Maria Heilbronner
AU - Janos Kalman
AU - Mojtaba Kohshour
AU - Daniela Reich-Erkelenz
AU - Sabrina Schaupp
AU - Eva Schulte
PY - 2026
DA - 2026/10/07
JO - British Journal of Psychiatry
DO - 10.1192/bjp.2026.10776
UR - https://hal.science/hal-05797726v1
AB - Background The predictive power of polygenic scores (PGSs) for lithium treatment response in bipolar disorder remains limited. Aims To enhance the prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or bipolar disorder aetiology. Method We analysed data collected from bipolar disorder patients who had received lithium treatment for at least 6 months and participated in the International Consortium on Lithium Genetics study ( N = 2367). The Alda scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous total Alda score (0–10) and categorical outcome (favourable ≥7 versus unfavourable response). PGSs were calculated for 60 phenotypes grouped into 5 clinical–biological clusters: psychiatric and behavioural (#23 phenotypes), cardiometabolic (#17), autoimmune/inflammatory (#5), neurocognitive (#8) and renal function (#7). We applied cross-validated machine learning regression approaches in both outcomes within each cluster, with the selected features from each cluster subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance ( R 2 ) for the continuous outcome, and both McFadden’s pseudo- R 2 and standard classification model parameters for the categorical outcome. Results mt-PGS explained between 5.70% (continuous outcome) and 9.11% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (area under the curve) for the categorical outcome was 68.24% (95% CI: 64.98–71.66), with a Brier score of 0.257. Of the 5 clusters, the PGSs for psychiatric and behavioural phenotypes were most strongly associated with lithium responsiveness, accounting for 3.67–6.52% of its variability. Conclusions By integrating PGSs for multiple relevant phenotypes, predictive accuracy for lithium response improved substantially compared with single-trait methods. Future research incorporating larger, more diverse populations and combining genetic scores with clinical data holds promise for further enhancing prediction and advancing clinical implementation.
SN - 0007-1250
SN - 1472-1465
ER -