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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.
Invisible inequality leads to punishing the poor and rewarding the rich
- OLIVER P. HAUSER, GORDON T. KRAFT-TODD, DAVID G. RAND, MARTIN A. NOWAK, MICHAEL I. NORTON
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- Journal:
- Behavioural Public Policy / Volume 5 / Issue 3 / July 2021
- Published online by Cambridge University Press:
- 06 March 2019, pp. 333-353
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Four experiments examine how lack of awareness of inequality affect behaviour towards the rich and poor. In Experiment 1, participants who became aware that wealthy individuals donated a smaller percentage of their income switched from rewarding the wealthy to rewarding the poor. In Experiments 2 and 3, participants who played a public goods game – and were assigned incomes reflective of the US income distribution either at random or on merit – punished the poor (for small absolute contributions) and rewarded the rich (for large absolute contributions) when incomes were unknown; when incomes were revealed, participants punished the rich (for their low percentage of income contributed) and rewarded the poor (for their high percentage of income contributed). In Experiment 4, participants provided with public education contributions for five New York school districts levied additional taxes on mostly poorer school districts when incomes were unknown, but targeted wealthier districts when incomes were revealed. These results shed light on how income transparency shapes preferences for equity and redistribution. We discuss implications for policy-makers.
Effects of Insecticide, Weed-Free Period, and Row Spacing on Soybean (Glycine max) and Sicklepod (Cassia obtusifolia) Growth
- Robert H. Walker, Michael G. Patterson, Ellis Hauser, David J. Isenhour, James W. Todd, Gale A. Buchanan
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- Journal:
- Weed Science / Volume 32 / Issue 5 / September 1984
- Published online by Cambridge University Press:
- 12 June 2017, pp. 702-706
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Results from identical experiments conducted at Headland, AL, and Plains, GA, from 1980 through 1982 show insecticide treatment had little effect on soybean [Glycine max (L.) Merr. ‘Coker 237′] growth and morphology. Maximum insecticide applications increased soybean seed weight in two of five trials. Soybeans maintained free of sicklepod (Cassia obtusifolia L. ♯3 CASOB) for 4 weeks after emergence produced yields equal to those receiving season-long control in all trials, and 2-week control was equal to season-long maintenance in three trials. Length of weed interference-free maintenance did not affect soybean height. The number of pods per plant and seed weight were decreased when there was no control. Sicklepod shoot fresh weight and numbers decreased as the weed-free period increased from 0 weeks through the season. Row spacing had no effect on soybean height or seed size; however, the number of pods per plant was higher in 80- than in 40-cm rows. Row spacing influenced yield in only one trial where 20-cm rows outyielded 40-cm rows. A significant interaction occurred between the weed-free period and row spacing in two trials. Soybeans in 20-cm rows outyielded those in 40- and 80-cm rows when sicklepod was not controlled (i.e., 0 weeks interference-free maintenance).
Contributors
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- By Douglas L. Arnold, Laura J. Balcer, Amit Bar-Or, Sergio E. Baranzini, Frederik Barkhof, Robert A. Bermel, Francois A. Bethoux, Dennis N. Bourdette, Richard K. Burt, Peter A. Calabresi, Zografos Caramanos, Tanuja Chitnis, Stacey S. Cofield, Jeffrey A. Cohen, Nadine Cohen, Alasdair J. Coles, Devon Conway, Stuart D. Cook, Gary R. Cutter, Peter J. Darlington, Ann Dodds-Frerichs, Ranjan Dutta, Gilles Edan, Michelle Fabian, Franz Fazekas, Massimo Filippi, Elizabeth Fisher, Paulo Fontoura, Corey C. Ford, Robert J. Fox, Natasha Frost, Alex Z. Fu, Siegrid Fuchs, Kazuo Fujihara, Kristin M. Galetta, Jeroen J.G. Geurts, Gavin Giovannoni, Nada Gligorov, Ralf Gold, Andrew D. Goodman, Myla D. Goldman, Jenny Guerre, Stephen L. Hauser, Peter B. Imrey, Douglas R. Jeffery, Stephen E. Jones, Adam I. Kaplin, Michael W. Kattan, B. Mark Keegan, Kyle C. Kern, Zhaleh Khaleeli, Samia J. Khoury, Joep Killestein, Soo Hyun Kim, R. Philip Kinkel, Stephen C. Krieger, Lauren B. Krupp, Emmanuelle Le Page, David Leppert, Scott Litwiller, Fred D. Lublin, Henry F. McFarland, Joseph C. McGowan, Don Mahad, Jahangir Maleki, Ruth Ann Marrie, Paul M. Matthews, Francesca Milanetti, Aaron E. Miller, Deborah M. Miller, Xavier Montalban, Charity J. Morgan, Ichiro Nakashima, Sridar Narayanan, Avindra Nath, Paul W. O’Connor, Jorge R. Oksenberg, A. John Petkau, Michael D. Phillips, J. Theodore Phillips, Tammy Phinney, Sean J. Pittock, Sarah M. Planchon, Chris H. Polman, Alexander Rae-Grant, Stephen M. Rao, Stephen C. Reingold, Maria A. Rocca, Richard A. Rudick, Amber R. Salter, Paula Sandler, Jaume Sastre-Garriga, John R. Scagnelli, Dana J. Serafin, Lynne Shinto, Nancy L. Sicotte, Jack H. Simon, Per Soelberg Sørensen, Ryan E. Stagg, James M. Stankiewicz, Lael A. Stone, Amy Sullivan, Matthew Sutliff, Jessica Szpak, Alan J. Thompson, Bruce D. Trapp, Helen Tremlett, Maria Trojano, Orla Tuohy, Rhonda R. Voskuhl, Marc K. Walton, Mike P. Wattjes, Emmanuelle Waubant, Martin S. Weber, Howard L Weiner, Brian G. Weinshenker, Bianca Weinstock-Guttman, Jeffrey L. Winters, Jerry S. Wolinsky, Vijayshree Yadav, E. Ann Yeh, Scott S. Zamvil
- Edited by Jeffrey A. Cohen, Richard A. Rudick
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- Book:
- Multiple Sclerosis Therapeutics
- Published online:
- 05 December 2011
- Print publication:
- 20 October 2011, pp viii-xii
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COBE Observations of Zodiacal Emission
- Michael G. Hauser
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- Journal:
- International Astronomical Union Colloquium / Volume 150 / 1996
- Published online by Cambridge University Press:
- 27 February 2018, pp. 309-314
- Print publication:
- 1996
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The COBE Diffuse Infrared Background Experiment has obtained some of the most extensive observations of the interplanetary dust (IPD) cloud ever assembled. For the 10 months of cryogenic operation, the brightness of the entire celestial, sphere was mapped with an 0.7° x 0.7° field of view at wavelengths of 1.25, 2.2, 3.5, 4.9, 12, 25, 60, 100, 140, and 240 μm, and the linear polarization was mapped at 1.25, 2.2, and 3.5 μm. Observations with reduced sensitivity continued at all wavelengths short of 12 μm for over 3 years after cryogen expiration. Throughout these observations, nearly 1/2 of the sky was mapped every day at elongation angles ranging from 64° to 124°. I describe the DIRBE and the general character of the infrared sky, outline the DIRBE team's approach to isolating the IPD signal, and review results of our initial studies of the zodiacal dust bands, the circumsolar dust ring, and the character of IPD cloud particles.
The COBE DIRBE Search for the Cosmic Infrared Background
- Michael G. Hauser
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- Journal:
- Symposium - International Astronomical Union / Volume 168 / 1996
- Published online by Cambridge University Press:
- 25 May 2016, pp. 99-108
- Print publication:
- 1996
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The Diffuse Infrared Background Experiment (DIRBE) on the Cosmic Background Explorer (COBE) satellite is designed to conduct a sensitive search for isotropic cosmic infrared background radiation over the spectral range from 1.25 to 240 μm. The cumulative emissions of pregalactic, protogalactic, and evolving galactic systems are expected to be recorded in this background. The DIRBE instrument has mapped the full sky with high redundancy at solar elongation angles ranging from 64°to 124°to facilitate separation of interplanetary, Galactic, and extragalactic sources of emission. Conservative limits on the isotropic infrared background are given by the minimum observed sky brightnesses in each DIRBE spectral band during the 10 months of cryogenic operation. Extensive modeling of the foregrounds is under way to isolate or strongly limit the extragalactic infrared component. The current approach to these modeling efforts is described and representative present residuals are reported.