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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, JeanMichel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, HsiChung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil TekolaAyele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 221 / Issue 2 / August 2022
- Published online by Cambridge University Press:
- 04 May 2022, p. 494
- Print publication:
- August 2022
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean-Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola-Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 28 February 2022, pp. 219-228
- Print publication:
- April 2022
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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.
AimsTo use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
MethodThis 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.
ResultsThe 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.
ConclusionsUsing 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.
Characterisation of age and polarity at onset in bipolar disorder
- Janos L. Kalman, Loes M. Olde Loohuis, Annabel Vreeker, Andrew McQuillin, Eli A. Stahl, Douglas Ruderfer, Maria Grigoroiu-Serbanescu, Georgia Panagiotaropoulou, Stephan Ripke, Tim B. Bigdeli, Frederike Stein, Tina Meller, Susanne Meinert, Helena Pelin, Fabian Streit, Sergi Papiol, Mark J. Adams, Rolf Adolfsson, Kristina Adorjan, Ingrid Agartz, Sofie R. Aminoff, Heike Anderson-Schmidt, Ole A. Andreassen, Raffaella Ardau, Jean-Michel Aubry, Ceylan Balaban, Nicholas Bass, Bernhard T. Baune, Frank Bellivier, Antoni Benabarre, Susanne Bengesser, Wade H Berrettini, Marco P. Boks, Evelyn J. Bromet, Katharina Brosch, Monika Budde, William Byerley, Pablo Cervantes, Catina Chillotti, Sven Cichon, Scott R. Clark, Ashley L. Comes, Aiden Corvin, William Coryell, Nick Craddock, David W. Craig, Paul E. Croarkin, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Udo Dannlowski, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Srdjan Djurovic, Howard J. Edenberg, Mariam Al Eissa, Torbjørn Elvsåshagen, Bruno Etain, Ayman H. Fanous, Frederike Fellendorf, Alessia Fiorentino, Andreas J. Forstner, Mark A. Frye, Janice M. Fullerton, Katrin Gade, Julie Garnham, Elliot Gershon, Michael Gill, Fernando S. Goes, Katherine Gordon-Smith, Paul Grof, Jose Guzman-Parra, Tim Hahn, Roland Hasler, Maria Heilbronner, Urs Heilbronner, Stephane Jamain, Esther Jimenez, Ian Jones, Lisa Jones, Lina Jonsson, Rene S. Kahn, John R. Kelsoe, James L. Kennedy, Tilo Kircher, George Kirov, Sarah Kittel-Schneider, Farah Klöhn-Saghatolislam, James A. Knowles, Thorsten M. Kranz, Trine Vik Lagerberg, Mikael Landen, William B. Lawson, Marion Leboyer, Qingqin S. Li, Mario Maj, Dolores Malaspina, Mirko Manchia, Fermin Mayoral, Susan L. McElroy, Melvin G. McInnis, Andrew M. McIntosh, Helena Medeiros, Ingrid Melle, Vihra Milanova, Philip B. Mitchell, Palmiero Monteleone, Alessio Maria Monteleone, Markus M. Nöthen, Tomas Novak, John I. Nurnberger, Niamh O'Brien, Kevin S. O'Connell, Claire O'Donovan, Michael C. O'Donovan, Nils Opel, Abigail Ortiz, Michael J. Owen, Erik Pålsson, Carlos Pato, Michele T. Pato, Joanna Pawlak, Julia-Katharina Pfarr, Claudia Pisanu, James B. Potash, Mark H Rapaport, Daniela Reich-Erkelenz, Andreas Reif, Eva Reininghaus, Jonathan Repple, Hélène Richard-Lepouriel, Marcella Rietschel, Kai Ringwald, Gloria Roberts, Guy Rouleau, Sabrina Schaupp, William A Scheftner, Simon Schmitt, Peter R. Schofield, K. Oliver Schubert, Eva C. Schulte, Barbara Schweizer, Fanny Senner, Giovanni Severino, Sally Sharp, Claire Slaney, Olav B. Smeland, Janet L. Sobell, Alessio Squassina, Pavla Stopkova, John Strauss, Alfonso Tortorella, Gustavo Turecki, Joanna Twarowska-Hauser, Marin Veldic, Eduard Vieta, John B. Vincent, Wei Xu, Clement C. Zai, Peter P. Zandi, Psychiatric Genomics Consortium (PGC) Bipolar Disorder Working Group, International Consortium on Lithium Genetics (ConLiGen), Colombia-US Cross Disorder Collaboration in Psychiatric Genetics, Arianna Di Florio, Jordan W. Smoller, Joanna M. Biernacka, Francis J. McMahon, Martin Alda, Bertram Müller-Myhsok, Nikolaos Koutsouleris, Peter Falkai, Nelson B. Freimer, Till F.M. Andlauer, Thomas G. Schulze, Roel A. Ophoff
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- Journal:
- The British Journal of Psychiatry / Volume 219 / Issue 6 / December 2021
- Published online by Cambridge University Press:
- 25 August 2021, pp. 659-669
- Print publication:
- December 2021
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Background
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
AimsTo examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
MethodGenome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
ResultsEarlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
ConclusionsAAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Distribution of 1298A > C polymorphism of methylenetetrahydrofolate reductase gene in patients with bipolar disorder and schizophrenia
- Bartosz Kempisty, Anna Bober, Marta Łuczak, Piotr Czerski, Aleksandra Szczepankiewicz, Joanna Hauser, Paweł P. Jagodziński
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- Journal:
- European Psychiatry / Volume 22 / Issue 1 / January 2007
- Published online by Cambridge University Press:
- 16 April 2020, pp. 39-43
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We investigated the genotype frequency of methylenetetrahydrofolate reductase (MTHFR) 1298A > C polymorphism in the group of patients with bipolar disorder type I (BDI) (n = 200) and schizophrenia (n = 200) and in the control group (n = 300). Odds ratio (OR) for patients with BD and schizophrenia in 1298CC homozygous state was 3.768 (95% CI = 1.752–8.104); P = 0.0003; (P = 0.0006 after Bonferroni correction) and 2.694; (95% CI = 1.207–6.013); P = 0.0123 (P = 0.0246 after Bonferroni correction), respectively. The stratification of patients based on gender revealed significant association of 1298CC genotype with female patients only with BDI (OR = 7.293; 95% CI = 2.017–26.363; P = 0.0005).
Our results confirm association of BD and schizophrenia with the 1p36.3 MTHFR locus and with the methyl group transfer using folate-dependent one-carbon pathway.
Association analysis of the insertion/deletion polymorphism in serotonin transporter gene in patients with affective disorder
- Joanna Hauser, Anna Leszczyńska, Jerzy Samochowiec, Piotr M. Czerski, Agata Ostapowicz, Maria Chlopocka, Jan Horodnicki, Janusz K. Rybakowski
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- Journal:
- European Psychiatry / Volume 18 / Issue 3 / May 2003
- Published online by Cambridge University Press:
- 16 April 2020, pp. 129-132
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A polymorphism of serotonin transporter was studied in 226 patients with affective disorders (n = 132 for bipolar, n = 94 for unipolar affective disorder) and in 213 healthy subjects. Consensus diagnosis by at least two psychiatrists, according to the ICD-10 and DSM-IV criteria was made for each patient using SCID (Structured Clinical Interview for DSM-IV Axis I Disorders). A functional polymorphism in the promoter region of serotonin transporter gene, where 44 bp are either inserted (long allele) or deleted (short allele) was analysed. Genotype s/s was significantly more frequent in patients comparing to the control group (P = 0.011 for bipolar and P = 0.003 for unipolar affective disorder) - the most marked association was found in males with bipolar and unipolar illness. The allele frequencies also differ significantly between patients and controls (P = 0.003 for bipolar and P = 0.001 for unipolar affective disorder). The frequency of the low activity (short) allele was higher in patients than in controls (51.1% in bipolar, and 54.3 in unipolar vs 39.4% in controls). We suggest that the presence of allele s may increase the susceptibility to occurrence of affective disorder.
TNF-α and intPLA2 genes' polymorphism in anorexia nervosa
- Agnieszka Slopien, Filip Rybakowski, Monika Dmitrzak-Weglarz, Piotr Czerski, Joanna Hauser, Renata Komorowska-Pietrzykowska, Andrzej Rajewski
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- Journal:
- Acta Neuropsychiatrica / Volume 16 / Issue 6 / December 2004
- Published online by Cambridge University Press:
- 24 June 2014, pp. 290-294
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Objective:
The aim of this study was the assessment of −308G/A tumor necrosis factor (TNF)-α gene polymorphism and intPLA2 gene polymorphism in patients with anorexia nervosa (AN) and healthy controls.
Subjects:We studied 91 non-related patients with AN and 144 healthy women (blood donors and students). The mean age of women from study group was 18.22 years (SD ± 3.13 years) and from control group was 31.71 years (SD ± 8.22).
Methods:Gene polymorphisms were studied with the use of polymerase chain reaction-restriction fragment length polymorphism method. TNF-α gene polymorphism consists of G/A substitution in −308 promoter region. IntPLA2 gene polymorphism is related to intron 1, in which restrictive region is found and recognized by BanI enzyme.
Results:We did not obtain statistically significant differences in the frequency of genotypes and alleles of −308G/A TNF-α polymorphism between the study and control groups (genotypes: P = 0.106, alleles: P = 0.076). We did analogous analysis in the restrictive and bulimic subgroups. We did not observe statistically relevant differences in the frequency of genotypes (P = 0.700) and alleles (P = 0.305). We did not obtain statistically relevant difference in the frequency of genotypes and alleles of intPLA2 gene between the study group and controls (genotypes: P = 0.300, alleles: P = 0.331). We did analogous analysis in both subgroups of AN. We did not observe statistically relevant differences in the frequency of genotypes (P = 0.344) and alleles (P = 0.230).
Conclusions:There was no statistically relevant trend for the association between TNF-α polymorphism and AN. We did not find association between studied polymorphism of intPLA2 gene and risk of AN.
Moderation of antidepressant response by the serotonin transporter gene
- Patricia Huezo-Diaz, Rudolf Uher, Rebecca Smith, Marcella Rietschel, Neven Henigsberg, Andrej Marušiˇ, Ole Mors, Wolfgang Maier, Joanna Hauser, Daniel Souery, Anna Placentino, Astrid Zobel, Erik Roj Larsen, Piotr M. Czerski, Bhanu Gupta, Farzana Hoda, Nader Perroud, Anne Farmer, Ian Craig, Katherine J. Aitchison, Peter McGuffin
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- Journal:
- The British Journal of Psychiatry / Volume 195 / Issue 1 / July 2009
- Published online by Cambridge University Press:
- 02 January 2018, pp. 30-38
- Print publication:
- July 2009
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Background
There have been conflicting reports on whether the length polymorphism in the promoter of the serotonin transporter gene (5-HTTLPR) moderates the antidepressant effects of selective serotonin reuptake inhibitors (SSRIs). We hypothesised that the pharmacogenetic effect of 5-HTTLPR is modulated by gender, age and other variants in the serotonin transporter gene.
AimsTo test the hypothesis that the 5-HTTLPR differently influences response to escitalopram (an SSRI) compared with nortriptyline (a noradrenaline reuptake inhibitor).
MethodThe 5-HTTLPR and 13 additional markers across the serotonin transporter gene were genotyped in 795 adults with moderate-to-severe depression treated with escitalopram or nortriptyline in the Genome Based Therapeutic Drugs for Depression (GENDEP) project.
ResultsThe 5-HTTLPR moderated the response to escitalopram, with long-allele carriers improving more than short-allele homozygotes. A significant three-way interaction between 5-HTTLPR, drug and gender indicated that the effect was concentrated in males treated with escitalopram. The single-nucleotide polymorphism rs2020933 also influenced outcome.
ConclusionsThe effect of 5-HTTLPR on antidepressant response is SSRI specific conditional on gender and modulated by another polymorphism at the 5' end of the serotonin transporter gene.