Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
Authors: Cearns Micah , Amare Azmeraw T. , Schubert Klaus Oliver , Thalamuthu Anbupalam , Frank Joseph , Streit Fabian , Adli Mazda , Akula Nirmala , Akiyama Kazufumi , Ardau Raffaella , Arias Bárbara , Aubry Jean-Michel , Backlund Lena , Bhattacharjee Abesh Kumar , Bellivier Frank , Benabarre Antonio , Bengesser Susanne , Biernacka Joanna M. , Birner Armin , Brichant-Petitjean Clara , Cervantes Pablo , Chen Hsi-Chung , Chillotti Caterina , Cichon Sven , Cruceanu Cristiana , Czerski Piotr M. , Dalkner Nina , Dayer Alexandre , Degenhardt Franziska , Zompo Maria Del , DePaulo J. Raymond , Étain Bruno , Falkai Peter , Forstner Andreas J. , Frisen Louise , Frye Mark A. , Fullerton Janice M. , Gard Sébastien , Garnham Julie S. , Goes Fernando S. , Grigoroiu-Serbanescu Maria , Grof Paul , Hashimoto Ryota , Hauser Joanna , Heilbronner Urs , Herms Stefan , Hoffmann Per , Hofmann Andrea , Hou Liping , Hsu Yi-Hsiang , Jamain Stephane , Jiménez Esther , Kahn Jean-Pierre , Kassem Layla , Kuo Po-Hsiu , Kato Tadafumi , Kelsoe John , Kittel-Schneider Sarah , Kliwicki Sebastian , König Barbara , Kusumi Ichiro , Laje Gonzalo , Landén Mikael , Lavebratt Catharina , Leboyer Marion , Leckband Susan G. , Maj Mario ,   the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Manchia Mirko , Martinsson Lina , McCarthy Michael J. , McElroy Susan , Colom Francesc , Mitjans Marina , Mondimore Francis M. , Monteleone Palmiero , Nievergelt Caroline M. , Nöthen Markus M. , Novák Tomas , O'Donovan Claire , Ozaki Norio , Millischer Vincent , Papiol Sergi , Pfennig Andrea , Pisanu Claudia , Potash James B. , Reif Andreas , Reininghaus Eva , Rouleau Guy A. , Rybakowski Janusz K. , Schalling Martin , Schofield Peter R. , Schweizer Barbara W. , Severino Giovanni , Shekhtman Tatyana , Shilling Paul D. , Shimoda Katzutaka , Simhandl Christian , Slaney Claire M. , Squassina Alessio , Stamm Thomas , Stopkova Pavla , Tekola-Ayele Fasil , Tortorella Alfonso , Turecki Gustavo , Veeh Julia , Vieta Eduard , Witt Stephanie H. , Roberts Gloria , Zandi Peter P. , Alda Martin , Bauer Michael , McMahon Francis J. , Mitchell Philip B. , Schulze Thomas G. , Rietschel Marcella , Clark Scott R. , Baune Bernhard T. 
Published: February 2022

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.