4 results
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
-
- 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
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
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
-
- 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
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
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.
Contributors
-
- By Linda S. Aglio, Cyrus Ahmadi Yazdi, Syed Irfan Qasim Ali, Caryn Barnet, Jessica Bauerle, Felicity Billings, Evan Blaney, Beverly Chang, Christopher Chen, Zinaida Chepurny, Hyung Sun Choi, Allison Clark, Lauren J. Cornella, Lisa Crossley, Michael D’Ambra, Galina Davidyuk, Whitney de Luna, Manisha S. Desai, Sukumar P. Desai, Kelly G. Elterman, Michaela K. Farber, Iuliu Fat, Jaida Fitzgerald, Devon Flaherty, John A. Fox, Gyorgy Frendl, Rejean Gareau, Joseph M. Garfield, Andrea Girnius, Laverne D. Gugino, J. Tasker Gundy, Carly C. Guthrie, Lisa M. Hammond, M. Tariq Hanifi, James Hardy, Philip M. Hartigan, Thomas Hickey, Richard Hsu, Mohab Ibrahim, David Janfaza, Yuka Kiyota, Suzanne Klainer, Benjamin Kloesel, Hanjo Ko, Bhavani Kodali, Vesela Kovacheva, J. Matthew Kynes, Robert W. Lekowski, Joyce Lo, Jeffrey Lu, Alvaro A. Macias, Zahra M. Malik, Erich N. Marks, Brendan McGinn, Jonathan R. Meserve, Annette Mizuguchi, Srdjan S. Nedeljkovic, Ju-Mei Ng, Michael Nguyen, Olutoyin Okanlawon, Jennifer Oliver, Krishna Parekh, Jessica Patterson, Christian Peccora, Pete Pelletier, Sujatha Pentakota, James H. Philip, Marc Philip T. Pimentel, Timothy D. Quinn, Elizabeth M. Rickerson, Susan L. Sager, Julia Serber, Shaheen Shaikh, Stanton Shernan, David Silver, Alissa Sodickson, Pingping Song, George P. Topulos, Agnieszka Trzcinka, Richard D. Urman, Rosemary Uzomba, Joshua Vacanti, Assia Valovska, Michael Vaninetti, Scott W. Vaughan, Kamen Vlassakov, Christopher Voscopoulos, Emily L. Wang, Laura Westfall, Zhiling Xiong, Stephanie Yacoubian, Dongdong Yao, Martin Zammert, Maksim Zayaruzny, Jose Luis Zeballos, Natthasorn Zinboonyahgoon, Jie Zhou
- Edited by Linda S. Aglio, Robert W. Lekowski, Richard D. Urman
-
- Book:
- Essential Clinical Anesthesia Review
- Published online:
- 05 February 2015
- Print publication:
- 08 January 2015, pp xi-xvi
-
- Chapter
- Export citation
Radiofrequency plasma stabilization of a low-Reynolds-number channel flow
- Timothy J. Fuller, Andrea G. Hsu, Rodrigo Sanchez-Gonzalez, Jacob C. Dean, Simon W. North, Rodney D. W. Bowersox
-
- Journal:
- Journal of Fluid Mechanics / Volume 748 / 10 June 2014
- Published online by Cambridge University Press:
- 08 May 2014, pp. 663-691
-
- Article
- Export citation
-
The effects of plasma heating and thermal non-equilibrium on the statistical properties of a low-Reynolds-number ($Re_{\tau } = 49$) turbulent channel flow were experimentally quantified using particle image velocimetry, two-line planar laser-induced fluorescence, coherent anti-Stokes Raman spectroscopy and emission spectroscopy. Tests were conducted at two radiofrequency plasma settings. The nitrogen, in air, was vibrationally excited to $T_{vib} \sim 1240\ \mathrm{K}$ and 1550 K for 150 W and 300 W plasma settings, respectively, while the vibrational temperature of the oxygen and the rotational/translational temperatures of all species remained near room temperature. The peak axial turbulence intensities in the shear layers were reduced by 15 and 30 % in moving across the plasma for the 150 and 300 W cases, respectively. The plasma did not alter the transverse intensities. The Reynolds shear stresses were reduced by 30 and 50 % for the 150 and 300 W cases. The corresponding Reynolds shear stress correlation coefficient was also reduced, which indicates that the large-scale structures were diminished. Finally, the plasma enhanced the turbulence decay in the zero-shear regions, where the power law decay $t^{-1/n}$ exponential factor $n$ decreased from 1.0 to 0.8.