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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.
Simultaneous Three-Dimensional Vascular and Tubular Imaging of Whole Mouse Kidneys With X-ray μCT
- Willy Kuo, Ngoc An Le, Bernhard Spingler, Roland H. Wenger, Anja Kipar, Udo Hetzel, Georg Schulz, Bert Müller, Vartan Kurtcuoglu
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- Journal:
- Microscopy and Microanalysis / Volume 26 / Issue 4 / August 2020
- Published online by Cambridge University Press:
- 06 July 2020, pp. 731-740
- Print publication:
- August 2020
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Concurrent three-dimensional imaging of the renal vascular and tubular systems on the whole-kidney scale with capillary level resolution is labor-intensive and technically difficult. Approaches based on vascular corrosion casting and X-ray micro computed tomography (μCT), for example, suffer from vascular filling artifacts and necessitate imaging with an additional modality to acquire tubules. In this work, we report on a new sample preparation, image acquisition, and quantification protocol for simultaneous vascular and tubular μCT imaging of whole, uncorroded mouse kidneys. The protocol consists of vascular perfusion with the water-soluble, aldehyde-fixable, polymeric X-ray contrast agent XlinCA, followed by laboratory-source μCT imaging and structural analysis using the freely available Fiji/ImageJ software. We achieved consistent filling of the entire capillary bed and staining of the tubules in the cortex and outer medulla. After imaging at isotropic voxel sizes of 3.3 and 4.4 μm, we segmented vascular and tubular systems and quantified luminal volumes, surface areas, diffusion distances, and vessel path lengths. This protocol permits the analysis of vascular and tubular parameters with higher reliability than vascular corrosion casting, less labor than serial sectioning and leaves tissue intact for subsequent histological examination with light and electron microscopy.
The Formation of Nano-voids in electroless Cu Layers
- T. Bernhard, S. Branagan, R. Schulz, F. Brüning, L. Stamp, K. Wurdinger, S. Kempa
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- Journal:
- MRS Advances / Volume 4 / Issue 41-42 / 2019
- Published online by Cambridge University Press:
- 30 August 2019, pp. 2231-2240
- Print publication:
- 2019
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The electrical reliability of multilayer high density interconnection printed circuit boards (HDI-PCBs) is mainly affected by the thermo-mechanical stability of stacked micro via interconnections. Here, a critical failure mode is the stress related crack between the electrolytically filled via and the target pad, commonly known as target pad separation. The junction includes two Cu-Cu-interfaces, one between the target Cu pad and the thin electroless Cu layer and the second between electroless Cu and electrolytic Cu. In this paper we will show that state-of-the-art electroless Cu plating processes are able to provide solid, completely recrystallized and highly reliable stacked via junctions. Defect free interfaces were achieved by using ionic Pd-activators and electroless Cu baths with a cyanide based stabilizer system. Cyanide free electroless Cu baths tend more to the formation of nanometer sized defects, discovered via Transmission Electron Microscopy (TEM). In this case a precise adjustment of single stabilizer components is mandatory to achieve defect free layers. The defects are hollow and were identified as “nano voids”. A critical density of these nano voids weakens the interface, predefines the crack path and reduces the overall reliability of the junction. A precise localization of the nano voids within the junction was enabled by detecting the Ni-containing electroless Cu layer via TEM-Ni mapping. Slower volume exchange of the electroless Cu solution within the blind micro via (BMV) substantially increases the nano void density. The ability of nano voids to migrate and coalesce at elevated temperatures was investigated as well.
Mineral chemistry, geothermobarometry and pre-Alpine high-pressure metamorphism of eclogitic amphibolites and mica schists from the Schobergruppe, Austroalpine basement, Eastern Alps
- Bernhard Schulz
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- Journal:
- Mineralogical Magazine / Volume 57 / Issue 387 / June 1993
- Published online by Cambridge University Press:
- 05 July 2018, pp. 189-202
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Alternating eclogitic amphibolites, mica schists and orthogneisses in the Schobergruppe to the south of the Tauern Window suffered a post-Upper-Ordovician progressive deformation D1-D2 which produced parallel planar-linear structures in all the rocks. Zoned garnets, preferentially oriented zoned clinopyroxenes (Jd 35-42%) and albite (An 7-9%) give evidence of high-pressure metamorphism (550-650°C 14-16 kbar) of the metabasites. Ca-amphiboles crystallized during subsequent decompression. In a kyanite-staurolite-garnet mica schist 300 metres below the metabasites, garnetbearing assemblages grew synchronous with the development of foliations S1 and S2. Garnets are zoned with increasing XMg and decreasing-increasing-rcdecreasing Xca from cores to rims. Albitic plagioclase (An 1-3%) and micas are enclosed in garnet cores and rims, are in contact with garnet, and occur with garnet in microlithons. When these minerals are used for geothermobarometry, a prograde P-T evolution (460 to 680°C with coeval pressure variations which reach high-pressure conditions can be estimated. This suggests that garnet-plagioclase geobarometry with albitic plagioclase works in the relevant P-T field. Similar garnet zonation trends and a similarly shaped P-T path from mica schists of an adjacent region with late-Variscan cooling ages, points to an eady-Variscan age of the syn-D1-D2 high-pressure and subsequent amphibolite-facies metamorphism.
Microstructures and mineral chemistry in amphibolites from the western Tauern Window (Eastern Alps), and P-T deformation paths of the Alpine greenschist-amphibolite facies metamorphism
- Bernhard Schulz, Claude Triboulet, Claude Audren
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- Journal:
- Mineralogical Magazine / Volume 59 / Issue 397 / December 1995
- Published online by Cambridge University Press:
- 05 July 2018, pp. 641-659
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Amphibolites in the Mesozoic part of the parautochthonous Lower Schieferhülle (LSH), the allochthonous Upper Schieferhülle (USH) and the overlying Austroalpine basement (AA) in and around the western Tauern Window (Eastern Alps) suffered a progressive Alpine deformation. Lineations and foliations L1-S1, L2-S2 defined by preferentially oriented (Na-Ca) amphiboles as well as F3 folds and further foliations Smyl and S4 in the metabasites are structures of successive deformational stages with a constant W-E main extension axis of strain. The (Na-Ca) amphiboles in assemblages with epidote, chlorite, albite/oligoclase and quartz are zoned with similar continuous zonation trends from early actinolite in the cores to magnesio-hornblende and tschermakitic hornblende, and from magnesio-hornblende to late actinolite in the rims in the three lithostratigraphic units. Geothermobarometry involving tremolite-edenite and (pargasite-hastingsite)-tremolite end-member equilibria in amphiboles allowed us to reconstruct prograde-retrograde P-T paths for the Alpine greenschist-amphibolite facies event. The paths passed P/Tmax at 6–7 kbar/600°C. Similar shapes of the paths in AA, USH and Mesozoic LSH indicate a common metamorphic history and a stacking of these units prior to or during the pre-Pmax evolution. Moderate P-T ratios are characteristic for the temperature-dominated compression paths and indicate continental collisional rather than subduction zone metamorphism. The middle to late Alpine greenschist-amphibolite facies event appears as an independent metamorphism along a complete P-T loop which may have followed an earlier and poorly documented high-pressure/low-temperature event.