Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr;220(4):219-228.
doi: 10.1192/bjp.2022.28. Epub 2022 Feb 28.

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

Micah Cearns  1 Azmeraw T Amare  2 Klaus Oliver Schubert  3 Anbupalam Thalamuthu  4 Joseph Frank  5 Fabian Streit  5 Mazda Adli  6 Nirmala Akula  7 Kazufumi Akiyama  8 Raffaella Ardau  9 Bárbara Arias  10 Jean-Michel Aubry  11 Lena Backlund  12 Abesh Kumar Bhattacharjee  13 Frank Bellivier  14 Antonio Benabarre  15 Susanne Bengesser  16 Joanna M Biernacka  17 Armin Birner  16 Clara Brichant-Petitjean  14 Pablo Cervantes  18 Hsi-Chung Chen  19 Caterina Chillotti  9 Sven Cichon  20 Cristiana Cruceanu  21 Piotr M Czerski  22 Nina Dalkner  16 Alexandre Dayer  11 Franziska Degenhardt  23 Maria Del Zompo  24 J Raymond DePaulo  25 Bruno Étain  14 Peter Falkai  26 Andreas J Forstner  27 Louise Frisen  12 Mark A Frye  28 Janice M Fullerton  29 Sébastien Gard  30 Julie S Garnham  31 Fernando S Goes  25 Maria Grigoroiu-Serbanescu  32 Paul Grof  33 Ryota Hashimoto  34 Joanna Hauser  22 Urs Heilbronner  35 Stefan Herms  36 Per Hoffmann  36 Andrea Hofmann  23 Liping Hou  7 Yi-Hsiang Hsu  37 Stephane Jamain  38 Esther Jiménez  15 Jean-Pierre Kahn  39 Layla Kassem  7 Po-Hsiu Kuo  40 Tadafumi Kato  41 John Kelsoe  13 Sarah Kittel-Schneider  42 Sebastian Kliwicki  43 Barbara König  44 Ichiro Kusumi  45 Gonzalo Laje  7 Mikael Landén  46 Catharina Lavebratt  12 Marion Leboyer  47 Susan G Leckband  48 Mario Maj  49 Major Depressive Disorder Working Group of the Psychiatric Genomics ConsortiumMirko Manchia  50 Lina Martinsson  51 Michael J McCarthy  52 Susan McElroy  53 Francesc Colom  54 Marina Mitjans  54 Francis M Mondimore  25 Palmiero Monteleone  55 Caroline M Nievergelt  13 Markus M Nöthen  23 Tomas Novák  56 Claire O'Donovan  31 Norio Ozaki  57 Vincent Millischer  58 Sergi Papiol  59 Andrea Pfennig  60 Claudia Pisanu  24 James B Potash  25 Andreas Reif  42 Eva Reininghaus  16 Guy A Rouleau  61 Janusz K Rybakowski  43 Martin Schalling  12 Peter R Schofield  29 Barbara W Schweizer  25 Giovanni Severino  24 Tatyana Shekhtman  13 Paul D Shilling  13 Katzutaka Shimoda  62 Christian Simhandl  63 Claire M Slaney  31 Alessio Squassina  24 Thomas Stamm  6 Pavla Stopkova  56 Fasil Tekola-Ayele  64 Alfonso Tortorella  65 Gustavo Turecki  21 Julia Veeh  42 Eduard Vieta  15 Stephanie H Witt  5 Gloria Roberts  66 Peter P Zandi  67 Martin Alda  31 Michael Bauer  60 Francis J McMahon  7 Philip B Mitchell  66 Thomas G Schulze  68 Marcella Rietschel  5 Scott R Clark  1 Bernhard T Baune  69
Affiliations

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

Micah Cearns et al. Br J Psychiatry. 2022 Apr.

Erratum in

  • Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach - CORRIGENDUM.
    Cearns M, Amare AT, Schubert KO, Thalamuthu A, Frank J, Streit F, Adli M, Akula N, Akiyama K, Ardau R, Arias B, Aubry J, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Brichant-Petitjean C, Cervantes P, Chen H, Chillotti C, Cichon S, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Degenhardt F, Zompo MD, DePaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hofmann A, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Millischer V, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney … See abstract for full author list ➔ Cearns M, et al. Br J Psychiatry. 2022 Aug;221(2):494. doi: 10.1192/bjp.2022.55. Br J Psychiatry. 2022. PMID: 35505515 No abstract available.

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.

Keywords: Mood stabilisers; bipolar affective disorders; depressive disorders; genetics; outcome studies.

PubMed Disclaimer

LinkOut - more resources