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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.
Polygenic contributions to alcohol use and alcohol use disorders across population-based and clinically ascertained samples
- Emma C. Johnson, Sandra Sanchez-Roige, Laura Acion, Mark J. Adams, Kathleen K. Bucholz, Grace Chan, Michael J. Chao, David B. Chorlian, Danielle M. Dick, Howard J. Edenberg, Tatiana Foroud, Caroline Hayward, Jon Heron, Victor Hesselbrock, Matthew Hickman, Kenneth S. Kendler, Sivan Kinreich, John Kramer, Sally I-Chun Kuo, Samuel Kuperman, Dongbing Lai, Andrew M. McIntosh, Jacquelyn L. Meyers, Martin H. Plawecki, Bernice Porjesz, David Porteous, Marc A. Schuckit, Jinni Su, Yong Zang, Abraham A. Palmer, Arpana Agrawal, Toni-Kim Clarke, Alexis C. Edwards
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- Journal:
- Psychological Medicine / Volume 51 / Issue 7 / May 2021
- Published online by Cambridge University Press:
- 20 January 2020, pp. 1147-1156
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Background
Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds.
MethodsWe examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes.
ResultsIn COGA, AUDIT-P PRS was associated with alcohol dependence, AUD symptom count, maximum drinks (R2 = 0.47–0.68%, p = 2.0 × 10−8–1.0 × 10−10), and increased likelihood of onset of alcohol dependence (hazard ratio = 1.15, p = 4.7 × 10−8); AUDIT-C PRS was not an independent predictor of any phenotype. In ALSPAC, the AUDIT-C PRS was associated with alcohol dependence (R2 = 0.96%, p = 4.8 × 10−6). In GS, AUDIT-C PRS was a better predictor of weekly alcohol use (R2 = 0.27%, p = 5.5 × 10−11), while AUDIT-P PRS was more associated with problem drinking (R2 = 0.40%, p = 9.0 × 10−7). Lastly, AUDIT-P PRS was associated with ICD-based alcohol-related disorders in the UKB subset (R2 = 0.18%, p < 2.0 × 10−16).
ConclusionsAUDIT-P PRS was associated with a range of alcohol-related phenotypes across population-based and ascertained cohorts, while AUDIT-C PRS showed less utility in the ascertained cohort. We show that AUDIT-P is genetically correlated with both use and misuse and demonstrate the influence of ascertainment schemes on PRS analyses.
Development and clinimetric assessment of a nurse-administered screening tool for movement disorders in psychosis
- Bettina Balint, Helen Killaspy, Louise Marston, Thomas Barnes, Anna Latorre, Eileen Joyce, Caroline S. Clarke, Rosa De Micco, Mark J. Edwards, Roberto Erro, Thomas Foltynie, Rachael M. Hunter, Fiona Nolan, Anette Schrag, Nick Freemantle, Yvonne Foreshaw, Nicholas Green, Kailash P. Bhatia, Davide Martino
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- Journal:
- BJPsych Open / Volume 4 / Issue 5 / September 2018
- Published online by Cambridge University Press:
- 27 September 2018, pp. 404-410
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Background
Movement disorders associated with exposure to antipsychotic drugs are common and stigmatising but underdiagnosed.
AimsTo develop and evaluate a new clinical procedure, the ScanMove instrument, for the screening of antipsychotic-associated movement disorders for use by mental health nurses.
MethodItem selection and content validity assessment for the ScanMove instrument were conducted by a panel of neurologists, psychiatrists and a mental health nurse, who operationalised a 31-item screening procedure. Interrater reliability was measured on ratings for 30 patients with psychosis from ten mental health nurses evaluating video recordings of the procedure. Criterion and concurrent validity were tested comparing the ScanMove instrument-based rating of 13 mental health nurses for 635 community patients from mental health services with diagnostic judgement of a movement disorder neurologist based on the ScanMove instrument and a reference procedure comprising a selection of commonly used rating scales.
ResultsInterreliability analysis showed no systematic difference between raters in their prediction of any antipsychotic-associated movement disorders category. On criterion validity testing, the ScanMove instrument showed good sensitivity for parkinsonism (90%) and hyperkinesia (89%), but not for akathisia (38%), whereas specificity was low for parkinsonism and hyperkinesia, and moderate for akathisia.
ConclusionsThe ScanMove instrument demonstrated good feasibility and interrater reliability, and acceptable sensitivity as a mental health nurse-administered screening tool for parkinsonism and hyperkinesia.
Declaration of interestNone.
Contributors
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- By Rony A. Adam, Gloria Bachmann, Nichole M. Barker, Randall B. Barnes, John Bennett, Inbar Ben-Shachar, Jonathan S. Berek, Sarah L. Berga, Monica W. Best, Eric J. Bieber, Frank M. Biro, Shan Biscette, Anita K. Blanchard, Candace Brown, Ronald T. Burkman, Joseph Buscema, John E. Buster, Michael Byas-Smith, Sandra Ann Carson, Judy C. Chang, Annie N. Y. Cheung, Mindy S. Christianson, Karishma Circelli, Daniel L. Clarke-Pearson, Larry J. Copeland, Bryan D. Cowan, Navneet Dhillon, Michael P. Diamond, Conception Diaz-Arrastia, Nicole M. Donnellan, Michael L. Eisenberg, Eric Eisenhauer, Sebastian Faro, J. Stuart Ferriss, Lisa C. Flowers, Susan J. Freeman, Leda Gattoc, Claudine Marie Gayle, Timothy M. Geiger, Jennifer S. Gell, Alan N. Gordon, Victoria L. Green, Jon K. Hathaway, Enrique Hernandez, S. Paige Hertweck, Randall S. Hines, Ira R. Horowitz, Fred M. Howard, William W. Hurd, Fidan Israfilbayli, Denise J. Jamieson, Carolyn R. Jaslow, Erika B. Johnston-MacAnanny, Rohna M. Kearney, Namita Khanna, Caroline C. King, Jeremy A. King, Ira J. Kodner, Tamara Kolev, Athena P. Kourtis, S. Robert Kovac, Ertug Kovanci, William H. Kutteh, Eduardo Lara-Torre, Pallavi Latthe, Herschel W. Lawson, Ronald L. Levine, Frank W. Ling, Larry I. Lipshultz, Steven D. McCarus, Robert McLellan, Shruti Malik, Suketu M. Mansuria, Mohamed K. Mehasseb, Pamela J. Murray, Saloney Nazeer, Farr R. Nezhat, Hextan Y. S. Ngan, Gina M. Northington, Peggy A. Norton, Ruth M. O'Regan, Kristiina Parviainen, Resad P. Pasic, Tanja Pejovic, K. Ulrich Petry, Nancy A. Phillips, Ashish Pradhan, Elizabeth E. Puscheck, Suneetha Rachaneni, Devon M. Ramaeker, David B. Redwine, Robert L. Reid, Carla P. Roberts, Walter Romano, Peter G. Rose, Robert L. Rosenfield, Shon P. Rowan, Mack T. Ruffin, Janice M. Rymer, Evis Sala, Ritu Salani, Joseph S. Sanfilippo, Mahmood I. Shafi, Roger P. Smith, Meredith L. Snook, Thomas E. Snyder, Mary D. Stephenson, Thomas G. Stovall, Richard L. Sweet, Philip M. Toozs-Hobson, Togas Tulandi, Elizabeth R. Unger, Denise S. Uyar, Marion S. Verp, Rahi Victory, Tamara J. Vokes, Michelle J. Washington, Katharine O'Connell White, Paul E. Wise, Frank M. Wittmaack, Miya P. Yamamoto, Christine Yu, Howard A. Zacur
- Edited by Eric J. Bieber, Joseph S. Sanfilippo, University of Pittsburgh, Ira R. Horowitz, Emory University, Atlanta, Mahmood I. Shafi
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- Book:
- Clinical Gynecology
- Published online:
- 05 April 2015
- Print publication:
- 23 April 2015, pp viii-xiv
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Contributors
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- By Mohamed Aboulghar, Ahmed Abou-Setta, Mary E. Abusief, G. David Adamson, R. J. Aitken, Hesham Al-Inany, Baris Ata, Hamdy Azab, Adam Balen, David H. Barad, Pedro N. Barri, C. Blockeel, Giuseppe Botta, Mark Bowman, Chris Brewer, Dominique M. Butawan, Sandra A. Carson, Hai Ying Chen, Anne Clark, Buenaventura Coroleu, S. Das, C. Dechanet, H. Déchaud, Cora de Klerk, Sheryl de Lacey, S. Deutsch-Bringer, P. Devroey, Didier Dewailly, Hakan E. Duran, Walid El Sherbiny, Tarek El-Toukhy, Johannes L. H. Evers, Cynthia Farquhar, Rodney D. Franklin, Juan A. Garcia-Velasco, David K. Gardner, Norbert Gleicher, Gedis Grudzinskas, Roger Hart, B Hédon, Colin M. Howles, Jack Yu Jen Huang, N. P. Johnson, Hey-Joo Kang, Gab Kovacs, Ben Kroon, Anver Kuliev, William H. Kutteh, Nick Macklon, Ragaa Mansour, Lamiya Mohiyiddeen, Lisa J. Moran, David Mortimer, Sharon T. Mortimer, Luciano G. Nardo, Robert J. Norman, Willem Ombelet, Luk Rombauts, Zev Rosenwaks, Francisco J. Ruiz Flores, Anthony J. Rutherford, Gavin Sacks, Denny Sakkas, M. W. Seif, Ayse Seyhan, Caroline Smith, Kate Stern, Elizabeth A. Sullivan, Sesh Kamal Sunkara, Seang Lin Tan, Mohamed Taranissi, Kelton P. Tremellen, Wendy S. Vitek, V. Vloeberghs, Bradley J. Van Voorhis, S. F. van Voorst, Amr Wahba, Yueping A. Wang, Klaus E. Wiemer
- Edited by Gab Kovacs, Monash University, Victoria
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- Book:
- How to Improve your ART Success Rates
- Published online:
- 05 July 2011
- Print publication:
- 30 June 2011, pp viii-xii
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Neuropsychological Profile of Parkin Mutation Carriers with and without Parkinson Disease: The CORE-PD Study
- Elise Caccappolo, Roy N. Alcalay, Helen Mejia-Santana, Ming-X. Tang, Brian Rakitin, Llency Rosado, Elan D. Louis, Cynthia L. Comella, Amy Colcher, Danna Jennings, Martha A. Nance, Susan Bressman, William K. Scott, Caroline M. Tanner, Susan F. Mickel, Howard F. Andrews, Cheryl Waters, Stanley Fahn, Lucien J. Cote, Steven Frucht, Blair Ford, Michael Rezak, Kevin Novak, Joseph H. Friedman, Ronald F. Pfeiffer, Laura Marsh, Brad Hiner, Andrew D. Siderowf, Barbara M. Ross, Miguel Verbitsky, Sergey Kisselev, Ruth Ottman, Lorraine N. Clark, Karen S. Marder
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- Journal:
- Journal of the International Neuropsychological Society / Volume 17 / Issue 1 / January 2011
- Published online by Cambridge University Press:
- 24 November 2010, pp. 91-100
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The cognitive profile of early onset Parkinson’s disease (EOPD) has not been clearly defined. Mutations in the parkin gene are the most common genetic risk factor for EOPD and may offer information about the neuropsychological pattern of performance in both symptomatic and asymptomatic mutation carriers. EOPD probands and their first-degree relatives who did not have Parkinson’s disease (PD) were genotyped for mutations in the parkin gene and administered a comprehensive neuropsychological battery. Performance was compared between EOPD probands with (N = 43) and without (N = 52) parkin mutations. The same neuropsychological battery was administered to 217 first-degree relatives to assess neuropsychological function in individuals who carry parkin mutations but do not have PD. No significant differences in neuropsychological test performance were found between parkin carrier and noncarrier probands. Performance also did not differ between EOPD noncarriers and carrier subgroups (i.e., heterozygotes, compound heterozygotes/homozygotes). Similarly, no differences were found among unaffected family members across genotypes. Mean neuropsychological test performance was within normal range in all probands and relatives. Carriers of parkin mutations, whether or not they have PD, do not perform differently on neuropsychological measures as compared to noncarriers. The cognitive functioning of parkin carriers over time warrants further study. (JINS, 2011, 17, 1–10)