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1. Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target

Author

Mark Adams, Katrin Charlet, Daniel B. Rosoff, Jeesun Jung, Zachary Kaminsky, Miruna C. Barbu, Rosie M. Walker, Andrew M. McIntosh, Jisoo Lee, David J. Porteous, Emma M. O’Connell, Falk W. Lohoff, Stewart W. Morris, Archie Campbell, Mairead L. Bermingham, Toni-Kim Clarke, David M. Howard, Heather C. Whalley, and Kathryn L. Evans

Subjects
Oncology, medicine.medical_specialty, biology, business.industry, Disease, Alcohol use disorder, SLC7A11, medicine.disease, Cellular and Molecular Neuroscience, Psychiatry and Mental health, CpG site, Endophenotype, Internal medicine, Cohort, Mendelian randomization, DNA methylation, medicine, biology.protein, business, Molecular Biology
Abstract

Alcohol misuse is common in many societies worldwide and is associated with extensive morbidity and mortality, often leading to alcohol use disorders (AUD) and alcohol-related end-organ damage. The underlying mechanisms contributing to the development of AUD are largely unknown; however, growing evidence suggests that alcohol consumption is strongly associated with alterations in DNA methylation. Identification of alcohol-associated methylomic variation might provide novel insights into pathophysiology and novel treatment targets for AUD. Here we performed the largest single-cohort epigenome-wide association study (EWAS) of alcohol consumption to date (N = 8161) and cross-validated findings in AUD populations with relevant endophenotypes, as well as alcohol-related animal models. Results showed 2504 CpGs significantly associated with alcohol consumption (Bonferroni p value < 6.8 × 10−8) with the five leading probes located in SLC7A11 (p = 7.75 × 10−108), JDP2 (p = 1.44 × 10−56), GAS5 (p = 2.71 × 10−47), TRA2B (p = 3.54 × 10−42), and SLC43A1 (p = 1.18 × 10−40). Genes annotated to associated CpG sites are implicated in liver and brain function, the cellular response to alcohol and alcohol-associated diseases, including hypertension and Alzheimer’s disease. Two-sample Mendelian randomization confirmed the causal relationship of consumption on AUD risk (inverse variance weighted (IVW) p = 5.37 × 10−09). A methylation-based predictor of alcohol consumption was able to discriminate AUD cases in two independent cohorts (p = 6.32 × 10−38 and p = 5.41 × 10−14). The top EWAS probe cg06690548, located in the cystine/glutamate transporter SLC7A11, was replicated in an independent cohort of AUD and control participants (N = 615) and showed strong hypomethylation in AUD (p −17). Decreased CpG methylation at this probe was consistently associated with clinical measures including increased heavy drinking days (p −4), increased liver function enzymes (GGT (p = 1.03 × 10−21), ALT (p = 1.29 × 10−6), and AST (p = 1.97 × 10−8)) in individuals with AUD. Postmortem brain analyses documented increased SLC7A11 expression in the frontal cortex of individuals with AUD and animal models showed marked increased expression in liver, suggesting a mechanism by which alcohol leads to hypomethylation-induced overexpression of SLC7A11. Taken together, our EWAS discovery sample and subsequent validation of the top probe in AUD suggest a strong role of abnormal glutamate signaling mediated by methylomic variation in SLC7A11. Our data are intriguing given the prominent role of glutamate signaling in brain and liver and might provide an important target for therapeutic intervention.

Published
2021

2. Creativity First, Science Follows: Lessons in Digital Signal Processing Education

Author

Clive Cheong Took, Anush Yardim, David M. Howard, and S.R. Alty

Subjects
business.industry, Applied Mathematics, media_common.quotation_subject, 020206 networking & telecommunications, 02 engineering and technology, Creativity, Variety (cybernetics), Multidisciplinary approach, Coursework, Signal Processing, ComputingMilieux_COMPUTERSANDEDUCATION, 0202 electrical engineering, electronic engineering, information engineering, Mathematics education, Electrical and Electronic Engineering, business, Working environment, Digital signal processing, media_common
Abstract

Digital signal processing (DSP) education has traditionally employed more demanding mathematics than most topics found among courses in electrical/electronic/computer engineering. In some cases, the technical challenges posed by some courses have made it difficult for students to complete those courses successfully. Here, we advocate for creativity to be nurtured in the first place, after which the science will flow naturally. To foster creativity, our pedagogical approach includes a variety of solutions incorporating exploratory exercises, open-ended multidisciplinary coursework, blended lecture-laboratory sessions, and a colorful working environment. We firmly believe that creativity is the way forward. Student feedback supports our approach.

Published
2021

3. The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank

Author

Patricia B. Munroe, Caroline H. Brennan, Julia Ramirez, David M. Howard, Robert Keers, and Judit García-González

Subjects
Multifactorial Inheritance, medicine.medical_specialty, Hallucinations, Population, Predictive markers, Article, Delusions, lcsh:RC321-571, Cellular and Molecular Neuroscience, 03 medical and health sciences, 0302 clinical medicine, Genetic predisposition, Humans, Medicine, Attention deficit hyperactivity disorder, Bipolar disorder, Psychiatry, education, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, Depression (differential diagnoses), Biological Specimen Banks, 030304 developmental biology, 0303 health sciences, education.field_of_study, business.industry, Smoking, medicine.disease, Personalized medicine, Mental health, Biobank, United Kingdom, 030227 psychiatry, 3. Good health, Psychiatry and Mental health, Psychotic Disorders, Schizophrenia, business, 030217 neurology & neurosurgery
Abstract

While psychotic experiences are core symptoms of mental health disorders like schizophrenia, they are also reported by 5–10% of the population. Both smoking behaviour and genetic risk for psychiatric disorders have been associated with psychotic experiences, but the interplay between these factors remains poorly understood. We tested whether smoking status, maternal smoking around birth, and number of packs smoked/year were associated with lifetime occurrence of three psychotic experiences phenotypes: delusions (n = 2067), hallucinations (n = 6689), and any psychotic experience (delusions or hallucinations; n = 7803) in 157,366 UK Biobank participants. We next calculated polygenic risk scores for schizophrenia (PRSSCZ), bipolar disorder (PRSBP), major depression (PRSDEP) and attention deficit hyperactivity disorder (PRSADHD) in 144,818 UK Biobank participants of European ancestry to assess whether association between smoking and psychotic experiences was attenuated after adjustment of diagnosis of psychiatric disorders and the PRSs. Finally, we investigated whether smoking exacerbates the effects of genetic predisposition on the psychotic phenotypes in gene-environment interaction models. Smoking status, maternal smoking, and number of packs smoked/year were associated with psychotic experiences (p −5). Except for packs smoked/year, effects were attenuated but remained significant after adjustment for diagnosis of psychiatric disorders and PRSs (p −3). Gene-environment interaction models showed the effects of PRSDEP and PRSADHD (but not PRSSCZ or PRSBP) on delusions (but not hallucinations) were significantly greater in current smokers compared to never smokers (p

Published
2020

4. Epigenetic prediction of major depressive disorder

Author

Toni-Kim Clarke, Heather C. Whalley, Miruna C. Barbu, Xueyi Shen, David M. Howard, Kathryn L. Evans, Rosie M. Walker, David J. Porteous, Riccardo E. Marioni, Andrew M. McIntosh, Yanni Zeng, Stewart W. Morris, and Ian J. Deary

Subjects
0301 basic medicine, Epigenomics, medicine.medical_specialty, Multifactorial Inheritance, Sociodemographic Factors, Predictive markers, Article, Epigenesis, Genetic, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Internal medicine, Genetics, Medicine, Additive genetic effects, Humans, genetics, Genetic Predisposition to Disease, Epigenetics, Molecular Biology, Depressive Disorder, Major, business.industry, predictive markers, dNaM, Heritability, medicine.disease, Mental health, Psychiatry and Mental health, 030104 developmental biology, Major depressive disorder, business, Psychosocial, Body mass index, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10−7) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10−9) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10−7). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R2 = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10−16). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.

Published
2020

5. Genetic stratification of depression in UK Biobank

Author

Toni-Kim Clarke, Archie Campbell, Gerome Breen, Thomas Werge, Saskia P. Hagenaars, David M. Howard, Naomi R. Wray, Cathryn M. Lewis, David J. Porteous, Buhm Han, Andrew M. McIntosh, Lasse Folkersen, Kylie P. Glanville, Mark Adams, Jonathan R. I. Coleman, and Patrick F. Sullivan

Subjects
0301 basic medicine, Bipolar Disorder, Autism Spectrum Disorder, LOCI, Anorexia nervosa, Article, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Genetics, Medicine, Humans, Bipolar disorder, GENOME-WIDE ASSOCIATION, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, Depression (differential diagnoses), Biological Specimen Banks, business.industry, Depression, MAJOR DEPRESSION, medicine.disease, Biobank, Mental health, United Kingdom, 3. Good health, PREVALENCE, Psychiatry and Mental health, INSIGHTS, 030104 developmental biology, Autism spectrum disorder, Schizophrenia, Attention Deficit Disorder with Hyperactivity, Etiology, INFERENCE, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract

Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations were used to test for evidence of subgroups using genetic data from seven other complex traits and disorders that were genetically correlated with depression and had sufficient power (>0.6) for detection. We found no evidence for subgroups within depression for schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, anorexia nervosa, inflammatory bowel disease or obesity. This suggests that for these traits, genetic correlations with depression were driven by pleiotropic genetic variants carried by everyone rather than by a specific subgroup.

Published
2020

6. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Author

Niamh Mullins, JooEun Kang, Adrian I. Campos, Jonathan R.I. Coleman, Alexis C. Edwards, Hanga Galfalvy, Daniel F. Levey, Adriana Lori, Andrey Shabalin, Anna Starnawska, Mei-Hsin Su, Hunna J. Watson, Mark Adams, Swapnil Awasthi, Michael Gandal, Jonathan D. Hafferty, Akitoyo Hishimoto, Minsoo Kim, Satoshi Okazaki, Ikuo Otsuka, Stephan Ripke, Erin B. Ware, Andrew W. Bergen, Wade H. Berrettini, Martin Bohus, Harry Brandt, Xiao Chang, Wei J. Chen, Hsi-Chung Chen, Steven Crawford, Scott Crow, Emily DiBlasi, Philibert Duriez, Fernando Fernández-Aranda, Manfred M. Fichter, Steven Gallinger, Stephen J. Glatt, Philip Gorwood, Yiran Guo, Hakon Hakonarson, Katherine A. Halmi, Hai-Gwo Hwu, Sonia Jain, Stéphane Jamain, Susana Jiménez-Murcia, Craig Johnson, Allan S. Kaplan, Walter H. Kaye, Pamela K. Keel, James L. Kennedy, Kelly L. Klump, Dong Li, Shih-Cheng Liao, Klaus Lieb, Lisa Lilenfeld, Chih-Min Liu, Pierre J. Magistretti, Christian R. Marshall, James E. Mitchell, Eric T. Monson, Richard M. Myers, Dalila Pinto, Abigail Powers, Nicolas Ramoz, Stefan Roepke, Vsevolod Rozanov, Stephen W. Scherer, Christian Schmahl, Marcus Sokolowski, Michael Strober, Laura M. Thornton, Janet Treasure, Ming T. Tsuang, Stephanie H. Witt, D. Blake Woodside, Zeynep Yilmaz, Lea Zillich, Rolf Adolfsson, Ingrid Agartz, Tracy M. Air, Martin Alda, Lars Alfredsson, Ole A. Andreassen, Adebayo Anjorin, Vivek Appadurai, María Soler Artigas, Sandra Van der Auwera, M. Helena Azevedo, Nicholas Bass, Claiton H.D. Bau, Bernhard T. Baune, Frank Bellivier, Klaus Berger, Joanna M. Biernacka, Tim B. Bigdeli, Elisabeth B. Binder, Michael Boehnke, Marco P. Boks, Rosa Bosch, David L. Braff, Richard Bryant, Monika Budde, Enda M. Byrne, Wiepke Cahn, Miguel Casas, Enrique Castelao, Jorge A. Cervilla, Boris Chaumette, Sven Cichon, Aiden Corvin, Nicholas Craddock, David Craig, Franziska Degenhardt, Srdjan Djurovic, Howard J. Edenberg, Ayman H. Fanous, Jerome C. Foo, Andreas J. Forstner, Mark Frye, Janice M. Fullerton, Justine M. Gatt, Pablo V. Gejman, Ina Giegling, Hans J. Grabe, Melissa J. Green, Eugenio H. Grevet, Maria Grigoroiu-Serbanescu, Blanca Gutierrez, Jose Guzman-Parra, Steven P. Hamilton, Marian L. Hamshere, Annette Hartmann, Joanna Hauser, Stefanie Heilmann-Heimbach, Per Hoffmann, Marcus Ising, Ian Jones, Lisa A. Jones, Lina Jonsson, René S. Kahn, John R. Kelsoe, Kenneth S. Kendler, Stefan Kloiber, Karestan C. Koenen, Manolis Kogevinas, Bettina Konte, Marie-Odile Krebs, Mikael Landén, Jacob Lawrence, Marion Leboyer, Phil H. Lee, Douglas F. Levinson, Calwing Liao, Jolanta Lissowska, Susanne Lucae, Fermin Mayoral, Susan L. McElroy, Patrick McGrath, Peter McGuffin, Andrew McQuillin, Sarah E. Medland, Divya Mehta, Ingrid Melle, Yuri Milaneschi, Philip B. Mitchell, Esther Molina, Gunnar Morken, Preben Bo Mortensen, Bertram Müller-Myhsok, Caroline Nievergelt, Vishwajit Nimgaonkar, Markus M. Nöthen, Michael C. O’Donovan, Roel A. Ophoff, Michael J. Owen, Carlos Pato, Michele T. Pato, Brenda W.J.H. Penninx, Jonathan Pimm, Giorgio Pistis, James B. Potash, Robert A. Power, Martin Preisig, Digby Quested, Josep Antoni Ramos-Quiroga, Andreas Reif, Marta Ribasés, Vanesa Richarte, Marcella Rietschel, Margarita Rivera, Andrea Roberts, Gloria Roberts, Guy A. Rouleau, Diego L. Rovaris, Dan Rujescu, Cristina Sánchez-Mora, Alan R. Sanders, Peter R. Schofield, Thomas G. Schulze, Laura J. Scott, Alessandro Serretti, Jianxin Shi, Stanley I. Shyn, Lea Sirignano, Pamela Sklar, Olav B. Smeland, Jordan W. Smoller, Edmund J.S. Sonuga-Barke, Gianfranco Spalletta, John S. Strauss, Beata Świątkowska, Maciej Trzaskowski, Gustavo Turecki, Laura Vilar-Ribó, John B. Vincent, Henry Völzke, James T.R. Walters, Cynthia Shannon Weickert, Thomas W. Weickert, Myrna M. Weissman, Leanne M. Williams, Naomi R. Wray, Clement C. Zai, Allison E. Ashley-Koch, Jean C. Beckham, Elizabeth R. Hauser, Michael A. Hauser, Nathan A. Kimbrel, Jennifer H. Lindquist, Benjamin McMahon, David W. Oslin, Xuejun Qin, Esben Agerbo, Anders D. Børglum, Gerome Breen, Annette Erlangsen, Tõnu Esko, Joel Gelernter, David M. Hougaard, Ronald C. Kessler, Henry R. Kranzler, Qingqin S. Li, Nicholas G. Martin, Andrew M. McIntosh, Ole Mors, Merete Nordentoft, Catherine M. Olsen, David Porteous, Robert J. Ursano, Danuta Wasserman, Thomas Werge, David C. Whiteman, Cynthia M. Bulik, Hilary Coon, Ditte Demontis, Anna R. Docherty, Po-Hsiu Kuo, Cathryn M. Lewis, J. John Mann, Miguel E. Rentería, Daniel J. Smith, Eli A. Stahl, Murray B. Stein, Fabian Streit, Virginia Willour, Douglas M. Ruderfer, Manuel Mattheisen, Abdel Abdellaoui, Mark J. Adams, Till F.M. Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T.F. Beekman, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Jane Hvarregaard Christensen, Toni-Kim Clarke, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Gail Davies, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Josef Frank, Héléna A. Gaspar, Michael Gill, Fernando S. Goes, Scott D. Gordon, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, Volker Arolt, Dorret I. Boomsma, Udo Dannlowski, E.J.C. de Geus, J. Raymond Depaulo, Enrico Domenici, Katharina Domschke, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Howard, Rick Jansen, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zoltán Kutalik, Yihan Li, Penelope A. Lind, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Christel M. Middeldorp, Evelin Mihailov, Lili Milani, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O’Reilly, Hogni Oskarsson, Caroline Hayward, Andrew C. Heath, Glyn Lewis, Pamela A.F. Madden, Patrik K. Magnusson, Andres Metspalu, Sara A. Paciga, Nancy L. Pedersen, Jodie N. Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E. Peterson, Wouter J. Peyrot, Danielle Posthuma, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Saira Saeed Mirza, Robert Schoevers, Eva C. Schulte, Ling Shen, Engilbert Sigurdsson, Grant C.B. Sinnamon, Johannes H. Smit, Hreinn Stefansson, Stacy Steinberg, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Roy H. Perlis, David J. Porteous, Catherine Schaefer, Kari Stefansson, Henning Tiemeier, Rudolf Uher, Patrick F. Sullivan, Kevin S. O’Connell, Brandon Coombes, Zhen Qiao, Thomas D. Als, Sigrid Børte, Alexander W. Charney, Ole Kristian Drange, Michael J. Gandal, Saskia P. Hagenaars, Masashi Ikeda, Nolan Kamitaki, Kristi Krebs, Georgia Panagiotaropoulou, Brian M. Schilder, Laura G. Sloofman, Bendik S. Winsvold, Hong-Hee Won, Liliya Abramova, Kristina Adorjan, Mariam Al Eissa, Diego Albani, Ney Alliey-Rodriguez, Verneri Antilla, Anastasia Antoniou, Ji Hyun Baek, Michael Bauer, Eva C. Beins, Sarah E. Bergen, Armin Birner, Erlend Bøen, Murielle Brum, Ben M. Brumpton, Nathalie Brunkhorst-Kanaan, William Byerley, Murray Cairns, Miquel Casas, Pablo Cervantes, Cristiana Cruceanu, Alfredo Cuellar-Barboza, Julie Cunningham, David Curtis, Piotr M. Czerski, Anders M. Dale, Nina Dalkner, Friederike S. David, Amanda L. Dobbyn, Athanassios Douzenis, Torbjørn Elvsåshagen, I. Nicol Ferrier, Alessia Fiorentino, Tatiana M. Foroud, Liz Forty, Oleksandr Frei, Nelson B. Freimer, Louise Frisén, Katrin Gade, Julie Garnham, Ian R. Gizer, Katherine Gordon-Smith, Tiffany A. Greenwood, José Guzman-Parra, Kyooseob Ha, Magnus Haraldsson, Martin Hautzinger, Urs Heilbronner, Dennis Hellgren, Peter A. Holmans, Laura Huckins, Jessica S. Johnson, Janos L. Kalman, Yoichiro Kamatani, Sarah Kittel-Schneider, Maria Koromina, Thorsten M. Kranz, Michiaki Kubo, Ralph Kupka, Steven A. Kushner, Catharina Lavebratt, Markus Leber, Heon-Jeong Lee, Shawn E. Levy, Catrin Lewis, Martin Lundberg, Sigurdur H. Magnusson, Adam Maihofer, Dolores Malaspina, Eirini Maratou, Lina Martinsson, Nathaniel W. McGregor, James D. McKay, Helena Medeiros, Vincent Millischer, Jennifer L. Moran, Derek W. Morris, Thomas W. Mühleisen, Niamh O’Brien, Claire O’Donovan, Loes M. Olde Loohuis, Lilijana Oruc, Sergi Papiol, Antonio F. Pardiñas, Amy Perry, Andrea Pfennig, Evgenia Porichi, Towfique Raj, Mark H. Rapaport, J. Raymond DePaulo, Eline J. Regeer, Fabio Rivas, Julian Roth, Panos Roussos, Fanny Senner, Sally Sharp, Paul D. Shilling, Claire Slaney, Janet L. Sobell, Maria Soler Artigas, Anne T. Spijker, Dan J. Stein, Chikashi Terao, Claudio Toma, Paul Tooney, Evangelia-Eirini Tsermpini, Marquis P. Vawter, Helmut Vedder, Simon Xi, Wei Xu, Jessica Mei Kay Yang, Allan H. Young, Hannah Young, Peter P. Zandi, Hang Zhou, null HUNT All-In Psychiatry, Gulja Babadjanova, Lena Backlund, Susanne Bengesser, Douglas H.R. Blackwood, Vaughan J. Carr, Stanley Catts, Dimitris Dikeos, Bruno Etain, Panagiotis Ferentinos, Micha Gawlik, Elliot S. Gershon, Frans Henskens, Jan Hillert, Kyung Sue Hong, Christina M. Hultman, Kristian Hveem, Nakao Iwata, Assen V. Jablensky, George Kirov, Christine Lochner, Carmel Loughland, Carol A. Mathews, Francis J. McMahon, Patricia Michie, Bryan Mowry, Benjamin M. Neale, Caroline M. Nievergelt, Ketil J. Oedegaard, Tomas Olsson, Chris Pantelis, George P. Patrinos, Eva Z. Reininghaus, Takeo Saito, Ulrich Schall, Martin Schalling, Rodney J. Scott, Eystein Stordal, Arne E. Vaaler, Eduard Vieta, Irwin D. Waldman, John-Anker Zwart, John I. Nurnberger, Arianna Di Florio, Roger A.H. Adan, Tetsuya Ando, Harald Aschauer, Jessica H. Baker, Vladimir Bencko, Andreas Birgegård, Joseph M. Boden, Ilka Boehm, Claudette Boni, Vesna Boraska Perica, Katharina Buehren, Roland Burghardt, Laura Carlberg, Matteo Cassina, Maurizio Clementi, Roger D. Cone, Philippe Courtet, James J. Crowley, Unna N. Danner, Oliver S.P. Davis, Martina de Zwaan, George Dedoussis, Daniela Degortes, Janiece E. DeSocio, Danielle M. Dick, Christian Dina, Monika Dmitrzak-Weglarz, Elisa Docampo Martinez, Laramie E. Duncan, Karin Egberts, Morten Mattingsdal, Sara McDevitt, Ingrid Meulenbelt, Nadia Micali, James Mitchell, Karen Mitchell, Palmiero Monteleone, Alessio Maria Monteleone, Melissa A. Munn-Chernoff, Benedetta Nacmias, Marie Navratilova, Ioanna Ntalla, Julie K. O’Toole, Leonid Padyukov, Aarno Palotie, Jacques Pantel, Hana Papezova, Richard Parker, John F. Pearson, Stefan Ehrlich, Geòrgia Escaramís, Thomas Espeseth, Xavier Estivill, Anne Farmer, Angela Favaro, Krista Fischer, James A.B. Floyd, Manuel Föcker, Lenka Foretova, Monica Forzan, Christopher S. Franklin, Giovanni Gambaro, Johanna Giuranna, Paola Giusti-Rodríquez, Fragiskos Gonidakis, Scott Gordon, Monica Gratacos Mayora, Sébastien Guillaume, Ken B. Hanscombe, Konstantinos Hatzikotoulas, Johannes Hebebrand, Sietske G. Helder, Anjali K. Henders, Beate Herpertz-Dahlmann, Wolfgang Herzog, Anke Hinney, L. John Horwood, Christopher Hübel, Liselotte V. Petersen, Kirstin L. Purves, Anu Raevuori, Ted Reichborn-Kjennerud, Valdo Ricca, Samuli Ripatti, Franziska Ritschel, Marion Roberts, Filip Rybakowski, Paolo Santonastaso, André Scherag, Ulrike Schmidt, Nicholas J. Schork, Alexandra Schosser, Jochen Seitz, Lenka Slachtova, P. Eline Slagboom, Margarita C.T. Slof-Op ‘t Landt, Agnieszka Slopien, Nicole Soranzo, Sandro Sorbi, Lorraine Southam, Vidar W. Steen, Laura M. Huckins, James I. Hudson, Hartmut Imgart, Hidetoshi Inoko, Vladimir Janout, Jennifer Jordan, Antonio Julià, Gursharan Kalsi, Deborah Kaminská, Jaakko Kaprio, Leila Karhunen, Andreas Karwautz, Martien J.H. Kas, Martin A. Kennedy, Anna Keski-Rahkonen, Kirsty Kiezebrink, Youl-Ri Kim, Katherine M. Kirk, Lars Klareskog, Gun Peggy S. Knudsen, Janne T. Larsen, Stephanie Le Hellard, Virpi M. Leppä, Paul Lichtenstein, Bochao Danae Lin, Astri Lundervold, Jurjen Luykx, Mario Maj, Katrin Mannik, Sara Marsal, Garret D. Stuber, Jin P. Szatkiewicz, Ioanna Tachmazidou, Elena Tenconi, Alfonso Tortorella, Federica Tozzi, Artemis Tsitsika, Marta Tyszkiewicz-Nwafor, Konstantinos Tziouvas, Annemarie A. van Elburg, Eric F. van Furth, Tracey D. Wade, Gudrun Wagner, Esther Walton, H. Erich Wichmann, Elisabeth Widen, Shuyang Yao, Eleftheria Zeggini, Stephanie Zerwas, Stephan Zipfel, Martin Jungkunz, Lydie Dietl, Cornelia E. Schwarze, Norbert Dahmen, Björn H. Schott, Arian Mobascher, Silvia Crivelli, Michelle F. Dennis, Phillip D. Harvey, Bruce W. Carter, Jennifer E. Huffman, Daniel Jacobson, Ravi Madduri, Maren K. Olsen, John Pestian, J. Michael Gaziano, Sumitra Muralidhar, Rachel Ramoni, Jean Beckham, Kyong-Mi Chang, Christopher J. O’Donnell, Philip S. Tsao, James Breeling, Grant Huang, J.P. Casas Romero, Jennifer Moser, Stacey B. Whitbourne, Jessica V. Brewer, Mihaela Aslan, Todd Connor, Dean P. Argyres, Brady Stephens, Mary T. Brophy, Donald E. Humphries, Luis E. Selva, Nhan Do, Shahpoor Shayan, Kelly Cho, Saiju Pyarajan, Elizabeth Hauser, Yan Sun, Hongyu Zhao, Peter Wilson, Rachel McArdle, Louis Dellitalia, Kristin Mattocks, John Harley, Clement J. Zablocki, Jeffrey Whittle, Frank Jacono, Salvador Gutierrez, Gretchen Gibson, Kimberly Hammer, Laurence Kaminsky, Gerardo Villareal, Scott Kinlay, Junzhe Xu, Mark Hamner, Roy Mathew, Sujata Bhushan, Pran Iruvanti, Michael Godschalk, Zuhair Ballas, Douglas Ivins, Stephen Mastorides, Jonathan Moorman, Saib Gappy, Jon Klein, Nora Ratcliffe, Hermes Florez, Olaoluwa Okusaga, Maureen Murdoch, Peruvemba Sriram, Shing Shing Yeh, Neeraj Tandon, Darshana Jhala, Samuel Aguayo, David Cohen, Satish Sharma, Suthat Liangpunsakul, Kris Ann Oursler, Mary Whooley, Sunil Ahuja, Joseph Constans, Paul Meyer, Jennifer Greco, Michael Rauchman, Richard Servatius, Melinda Gaddy, Agnes Wallbom, Timothy Morgan, Todd Stapley, Scott Sherman, George Ross, Philip Tsao, Patrick Strollo, Edward Boyko, Laurence Meyer, Samir Gupta, Mostaqul Huq, Joseph Fayad, Adriana Hung, Jack Lichy, Robin Hurley, Brooks Robey, Robert Striker, Dietl, Lydie, Schwarze, Cornelia E., Dahmen, Norbert, Schott, Björn H., Nöthen, Markus M., Ripke, Stephan, Mobascher, Arian, Rujescu, Dan, Lieb, Klaus, Roepke, Stefan, Schmahl, Christian, Bohus, Martin, Rietschel, Marcella, Crivelli, Silvia, Dennis, Michelle F., Harvey, Phillip D., Carter, Bruce W., Huffman, Jennifer E., Jacobson, Daniel, Madduri, Ravi, Olsen, Maren K., Pestian, John, Gaziano, J. Michael, Muralidhar, Sumitra, Ramoni, Rachel, Beckham, Jean, Chang, Kyong-Mi, O'Donnell, Christopher J., Tsao, Philip S., Breeling, James, Huang, Grant, Romero, J. P. Casas, Moser, Jennifer, Whitbourne, Stacey B., Brewer, Jessica V., Aslan, Mihaela, Connor, Todd, Argyres, Dean P., Stephens, Brady, Brophy, Mary T., Humphries, Donald E., Selva, Luis E., Do, Nhan, Shayan, Shahpoor, Cho, Kelly, Pyarajan, Saiju, Hauser, Elizabeth, Sun, Yan, Zhao, Hongyu, Wilson, Peter, McArdle, Rachel, Dellitalia, Louis, Mattocks, Kristin, Harley, John, Zablocki, Clement J., Whittle, Jeffrey, Jacono, Frank, Gutierrez, Salvador, Gibson, Gretchen, Hammer, Kimberly, Kaminsky, Laurence, Villareal, Gerardo, Kinlay, Scott, Xu, Junzhe, Hamner, Mark, Mathew, Roy, Bhushan, Sujata, Iruvanti, Pran, Godschalk, Michael, Ballas, Zuhair, Ivins, Douglas, Mastorides, Stephen, Moorman, Jonathan, Gappy, Saib, Klein, Jon, Ratcliffe, Nora, Florez, Hermes, Okusaga, Olaoluwa, Murdoch, Maureen, Sriram, Peruvemba, Yeh, Shing Shing, Tandon, Neeraj, Jhala, Darshana, Aguayo, Samuel, Cohen, David, Sharma, Satish, Liangpunsakul, Suthat, Oursler, Kris Ann, Whooley, Mary, Ahuja, Sunil, Constans, Joseph, Meyer, Paul, Greco, Jennifer, Rauchman, Michael, Servatius, Richard, Gaddy, Melinda, Wallbom, Agnes, Morgan, Timothy, Stapley, Todd, Sherman, Scott, Ross, George, Tsao, Philip, Strollo, Patrick, Boyko, Edward, Meyer, Laurence, Gupta, Samir, Huq, Mostaqul, Fayad, Joseph, Hung, Adriana, Lichy, Jack, Hurley, Robin, Robey, Brooks, Striker, Robert, Wray, Naomi R., Mattheisen, Manuel, Trzaskowski, Maciej, Byrne, Enda M., Abdellaoui, Abdel, Adams, Mark J., Agerbo, Esben, Air, Tracy M., Andlauer, Till F. M., Bacanu, Silviu-Alin, Bækvad-Hansen, Marie, Beekman, Aartjan T. F., Bigdeli, Tim B., Binder, Elisabeth B., Bryois, Julien, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Cai, Na, Castelao, Enrique, Christensen, Jane Hvarregaard, Clarke, Toni-Kim, Coleman, Jonathan R. I., Colodro-Conde, Lucía, Couvy-Duchesne, Baptiste, Craddock, Nick, Crawford, Gregory E., Davies, Gail, Degenhardt, Franziska, Derks, Eske M., Direk, Nese, Dolan, Conor V., Dunn, Erin C., Eley, Thalia C., Escott-Price, Valentina, Hassan Kiadeh, Farnush Farhadi, Finucane, Hilary K., Foo, Jerome C., Forstner, Andreas J., Frank, Josef, Gaspar, Héléna A., Gill, Michael, Goes, Fernando S., Gordon, Scott D., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Yang, Jian, Zhang, Futao, Arolt, Volker, Baune, Bernhard T., Berger, Klaus, Boomsma, Dorret I., Cichon, Sven, Dannlowski, Udo, de Geus, E. J. C., DePaulo, J. Raymond, Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Grabe, Hans J., Hamilton, Steven P., Grove, Jakob, Hall, Lynsey S., Hansen, Christine Søholm, Hansen, Thomas F., Herms, Stefan, Hickie, Ian B., Hoffmann, Per, Homuth, Georg, Horn, Carsten, Hottenga, Jouke-Jan, Hougaard, David M., Howard, David M., Ising, Marcus, Jansen, Rick, Jones, Ian, Jones, Lisa A., Jorgenson, Eric, Knowles, James A., Kohane, Isaac S., Kraft, Julia, Kretzschmar, Warren W., Kutalik, Zoltán, Li, Yihan, Lind, Penelope A., MacIntyre, Donald J., MacKinnon, Dean F., Maier, Robert M., Maier, Wolfgang, Marchini, Jonathan, Mbarek, Hamdi, McGrath, Patrick, McGuffin, Peter, Medland, Sarah E., Mehta, Divya, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Mondimore, Francis M., Montgomery, Grant W., Mostafavi, Sara, Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nivard, Michel G., Nyholt, Dale R., O'Reilly, Paul F., Oskarsson, Hogni, Hayward, Caroline, Heath, Andrew C., Kendler, Kenneth S., Kloiber, Stefan, Lewis, Glyn, Li, Qingqin S., Lucae, Susanne, Madden, Pamela A. F., Magnusson, Patrik K., Martin, Nicholas G., McIntosh, Andrew M., Metspalu, Andres, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nordentoft, Merete, O'Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Owen, Michael J., Painter, Jodie N., Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Peterson, Roseann E., Peyrot, Wouter J., Pistis, Giorgio, Posthuma, Danielle, Quiroz, Jorge A., Qvist, Per, Rice, John P., Riley, Brien P., Rivera, Margarita, Mirza, Saira Saeed, Schoevers, Robert, Schulte, Eva C., Shen, Ling, Shi, Jianxin, Shyn, Stanley I., Sigurdsson, Engilbert, Sinnamon, Grant C. B., Smit, Johannes H., Smith, Daniel J., Stefansson, Hreinn, Steinberg, Stacy, Streit, Fabian, Strohmaier, Jana, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Trubetskoy, Vassily, Uitterlinden, André G., Umbricht, Daniel, Van der Auwera, Sandra, van Hemert, Albert M., Viktorin, Alexander, Visscher, Peter M., Wang, Yunpeng, Webb, Bradley T., Penninx, Brenda W. J. H., Perlis, Roy H., Porteous, David J., Potash, James B., Preisig, Martin, Schaefer, Catherine, Schulze, Thomas G., Smoller, Jordan W., Stefansson, Kari, Tiemeier, Henning, Uher, Rudolf, Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Lewis, Cathryn M., Levinson, Douglas F., Breen, Gerome, Børglum, Anders D., Sullivan, Patrick F., O'Connell, Kevin S., Coombes, Brandon, Qiao, Zhen, Als, Thomas D., Børte, Sigrid, Charney, Alexander W., Drange, Ole Kristian, Gandal, Michael J., Hagenaars, Saskia P., Ikeda, Masashi, Kamitaki, Nolan, Kim, Minsoo, Krebs, Kristi, Panagiotaropoulou, Georgia, Schilder, Brian M., Sloofman, Laura G., Winsvold, Bendik S., Won, Hong-Hee, Abramova, Liliya, Adorjan, Kristina, Al Eissa, Mariam, Albani, Diego, Alliey-Rodriguez, Ney, Anjorin, Adebayo, Antilla, Verneri, Antoniou, Anastasia, Awasthi, Swapnil, Baek, Ji Hyun, Bass, Nicholas, Bauer, Michael, Beins, Eva C., Bergen, Sarah E., Birner, Armin, Bøen, Erlend, Boks, Marco P., Bosch, Rosa, Brum, Murielle, Brumpton, Ben M., Brunkhorst-Kanaan, Nathalie, Budde, Monika, Byerley, William, Cairns, Murray, Casas, Miquel, Cervantes, Pablo, Cruceanu, Cristiana, Cuellar-Barboza, Alfredo, Cunningham, Julie, Curtis, David, Czerski, Piotr M., Dale, Anders M., Dalkner, Nina, David, Friederike S., Djurovic, Srdjan, Dobbyn, Amanda L., Douzenis, Athanassios, Elvsåshagen, Torbjørn, Ferrier, I. Nicol, Fiorentino, Alessia, Foroud, Tatiana M., Forty, Liz, Frei, Oleksandr, Freimer, Nelson B., Frisén, Louise, Gade, Katrin, Garnham, Julie, Gelernter, Joel, Gizer, Ian R., Gordon-Smith, Katherine, Greenwood, Tiffany A., Guzman-Parra, José, Ha, Kyooseob, Haraldsson, Magnus, Hautzinger, Martin, Heilbronner, Urs, Hellgren, Dennis, Holmans, Peter A., Huckins, Laura, Jamain, Stéphane, Johnson, Jessica S., Kalman, Janos L., Kamatani, Yoichiro, Kennedy, James L., Kittel-Schneider, Sarah, Kogevinas, Manolis, Koromina, Maria, Kranz, Thorsten M., Kranzler, Henry R., Kubo, Michiaki, Kupka, Ralph, Kushner, Steven A., Lavebratt, Catharina, Lawrence, Jacob, Leber, Markus, Lee, Heon-Jeong, Lee, Phil H., Levy, Shawn E., Lewis, Catrin, Liao, Calwing, Lundberg, Martin, Magnusson, Sigurdur H., Maihofer, Adam, Malaspina, Dolores, Maratou, Eirini, Martinsson, Lina, McGregor, Nathaniel W., McKay, James D., Medeiros, Helena, Millischer, Vincent, Moran, Jennifer L., Morris, Derek W., Mühleisen, Thomas W., O'Brien, Niamh, O'Donovan, Claire, Olde Loohuis, Loes M., Oruc, Lilijana, Papiol, Sergi, Pardiñas, Antonio F., Perry, Amy, Pfennig, Andrea, Porichi, Evgenia, Quested, Digby, Raj, Towfique, Rapaport, Mark H., Regeer, Eline J., Rivas, Fabio, Roth, Julian, Roussos, Panos, Ruderfer, Douglas M., Sánchez-Mora, Cristina, Senner, Fanny, Sharp, Sally, Shilling, Paul D., Sirignano, Lea, Slaney, Claire, Smeland, Olav B., Sobell, Janet L., Artigas, Maria Soler, Spijker, Anne T., Stein, Dan J., Strauss, John S., Świątkowska, Beata, Terao, Chikashi, Toma, Claudio, Tooney, Paul, Tsermpini, Evangelia-Eirini, Vawter, Marquis P., Vedder, Helmut, Walters, James T. R., Xi, Simon, Xu, Wei, Kay Yang, Jessica Mei, Young, Allan H., Young, Hannah, Zandi, Peter P., Zhou, Hang, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Alfredsson, Lars, Babadjanova, Gulja, Backlund, Lena, Bellivier, Frank, Bengesser, Susanne, Berrettini, Wade H., Blackwood, Douglas H. R., Boehnke, Michael, Carr, Vaughan J., Catts, Stanley, Corvin, Aiden, Craddock, Nicholas, Dikeos, Dimitris, Etain, Bruno, Ferentinos, Panagiotis, Frye, Mark, Fullerton, Janice M., Gawlik, Micha, Gershon, Elliot S., Green, Melissa J., Grigoroiu-Serbanescu, Maria, Hauser, Joanna, Henskens, Frans, Hillert, Jan, Hong, Kyung Sue, Hultman, Christina M., Hveem, Kristian, Iwata, Nakao, Jablensky, Assen V., Kahn, René S., Kelsoe, John R., Kirov, George, Landén, Mikael, Leboyer, Marion, Lissowska, Jolanta, Lochner, Christine, Loughland, Carmel, Mathews, Carol A., Mayoral, Fermin, McElroy, Susan L., McMahon, Francis J., Melle, Ingrid, Michie, Patricia, Mitchell, Philip B., Morken, Gunnar, Mowry, Bryan, Myers, Richard M., Neale, Benjamin M., Nievergelt, Caroline M., Oedegaard, Ketil J., Olsson, Tomas, Pantelis, Chris, Pato, Carlos, Pato, Michele T., Patrinos, George P., Ramos-Quiroga, Josep Antoni, Reif, Andreas, Reininghaus, Eva Z., Ribasés, Marta, Rouleau, Guy A., Saito, Takeo, Schall, Ulrich, Schalling, Martin, Schofield, Peter R., Scott, Laura J., Scott, Rodney J., Serretti, Alessandro, Weickert, Cynthia Shannon, Stordal, Eystein, Turecki, Gustavo, Vaaler, Arne E., Vieta, Eduard, Vincent, John B., Waldman, Irwin D., Weickert, Thomas W., Zwart, John-Anker, Biernacka, Joanna M., Nurnberger, John I., Edenberg, Howard J., Stahl, Eli A., McQuillin, Andrew, Di Florio, Arianna, Ophoff, Roel A., Andreassen, Ole A., Adan, Roger A. H., Ando, Tetsuya, Aschauer, Harald, Baker, Jessica H., Bencko, Vladimir, Bergen, Andrew W., Birgegård, Andreas, Boden, Joseph M., Boehm, Ilka, Boni, Claudette, Perica, Vesna Boraska, Brandt, Harry, Buehren, Katharina, Bulik, Cynthia M., Burghardt, Roland, Carlberg, Laura, Cassina, Matteo, Clementi, Maurizio, Cone, Roger D., Courtet, Philippe, Crawford, Steven, Crow, Scott, Crowley, James J., Danner, Unna N., Davis, Oliver S. P., de Zwaan, Martina, Dedoussis, George, Degortes, Daniela, DeSocio, Janiece E., Dick, Danielle M., Dina, Christian, Dmitrzak-Weglarz, Monika, Martinez, Elisa Docampo, Duncan, Laramie E., Egberts, Karin, Marshall, Christian R., Mattingsdal, Morten, McDevitt, Sara, Meulenbelt, Ingrid, Micali, Nadia, Mitchell, James, Mitchell, Karen, Monteleone, Palmiero, Monteleone, Alessio Maria, Munn-Chernoff, Melissa A., Nacmias, Benedetta, Navratilova, Marie, Ntalla, Ioanna, Olsen, Catherine M., O'Toole, Julie K., Padyukov, Leonid, Palotie, Aarno, Pantel, Jacques, Papezova, Hana, Parker, Richard, Pearson, John F., Ehrlich, Stefan, Escaramís, Geòrgia, Espeseth, Thomas, Estivill, Xavier, Farmer, Anne, Favaro, Angela, Fernández-Aranda, Fernando, Fichter, Manfred M., Fischer, Krista, Floyd, James A. B., Föcker, Manuel, Foretova, Lenka, Forzan, Monica, Franklin, Christopher S., Gallinger, Steven, Gambaro, Giovanni, Giegling, Ina, Giuranna, Johanna, Giusti-Rodríquez, Paola, Gonidakis, Fragiskos, Gordon, Scott, Gorwood, Philip, Mayora, Monica Gratacos, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine A., Hanscombe, Ken B., Hatzikotoulas, Konstantinos, Hebebrand, Johannes, Helder, Sietske G., Henders, Anjali K., Herpertz-Dahlmann, Beate, Herzog, Wolfgang, Hinney, Anke, Horwood, L. John, Hübel, Christopher, Petersen, Liselotte V., Pinto, Dalila, Purves, Kirstin L., Raevuori, Anu, Ramoz, Nicolas, Reichborn-Kjennerud, Ted, Ricca, Valdo, Ripatti, Samuli, Ritschel, Franziska, Roberts, Marion, Rybakowski, Filip, Santonastaso, Paolo, Scherag, André, Scherer, Stephen W., Schmidt, Ulrike, Schork, Nicholas J., Schosser, Alexandra, Seitz, Jochen, Slachtova, Lenka, Slagboom, P. Eline, Slof-Op 't Landt, Margarita C. T., Slopien, Agnieszka, Soranzo, Nicole, Sorbi, Sandro, Southam, Lorraine, Steen, Vidar W., Strober, Michael, Huckins, Laura M., Hudson, James I., Imgart, Hartmut, Inoko, Hidetoshi, Janout, Vladimir, Jiménez-Murcia, Susana, Johnson, Craig, Jordan, Jennifer, Julià, Antonio, Kalsi, Gursharan, Kaminská, Deborah, Kaplan, Allan S., Kaprio, Jaakko, Karhunen, Leila, Karwautz, Andreas, Kas, Martien J. H., Kaye, Walter H., Kennedy, Martin A., Keski-Rahkonen, Anna, Kiezebrink, Kirsty, Kim, Youl-Ri, Kirk, Katherine M., Klareskog, Lars, Klump, Kelly L., Knudsen, Gun Peggy S., Larsen, Janne T., Le Hellard, Stephanie, Leppä, Virpi M., Li, Dong, Lichtenstein, Paul, Lilenfeld, Lisa, Lin, Bochao Danae, Lundervold, Astri, Luykx, Jurjen, Magistretti, Pierre J., Maj, Mario, Mannik, Katrin, Marsal, Sara, Stuber, Garret D., Szatkiewicz, Jin P., Tachmazidou, Ioanna, Tenconi, Elena, Thornton, Laura M., Tortorella, Alfonso, Tozzi, Federica, Treasure, Janet, Tsitsika, Artemis, Tyszkiewicz-Nwafor, Marta, Tziouvas, Konstantinos, van Elburg, Annemarie A., van Furth, Eric F., Wade, Tracey D., Wagner, Gudrun, Walton, Esther, Watson, Hunna J., Whiteman, David C., Wichmann, H. Erich, Widen, Elisabeth, Woodside, D. Blake, Yao, Shuyang, Yilmaz, Zeynep, Zeggini, Eleftheria, Zerwas, Stephanie, Zipfel, Stephan, Jungkunz, Martin, Mullins, N., Kang, J., Campos, A. I., Coleman, J. R. I., Edwards, A. C., Galfalvy, H., Levey, D. F., Lori, A., Shabalin, A., Starnawska, A., Su, M. -H., Watson, H. J., Adams, M., Awasthi, S., Gandal, M., Hafferty, J. D., Hishimoto, A., Kim, M., Okazaki, S., Otsuka, I., Ripke, S., Ware, E. B., Bergen, A. W., Berrettini, W. H., Bohus, M., Brandt, H., Chang, X., Chen, W. J., Chen, H. -C., Crawford, S., Crow, S., Diblasi, E., Duriez, P., Fernandez-Aranda, F., Fichter, M. M., Gallinger, S., Glatt, S. J., Gorwood, P., Guo, Y., Hakonarson, H., Halmi, K. A., Hwu, H. -G., Jain, S., Jamain, S., Jimenez-Murcia, S., Johnson, C., Kaplan, A. S., Kaye, W. H., Keel, P. K., Kennedy, J. L., Klump, K. L., Li, D., Liao, S. -C., Lieb, K., Lilenfeld, L., Liu, C. -M., Magistretti, P. J., Marshall, C. R., Mitchell, J. E., Monson, E. T., Myers, R. M., Pinto, D., Powers, A., Ramoz, N., Roepke, S., Rozanov, V., Scherer, S. W., Schmahl, C., Sokolowski, M., Strober, M., Thornton, L. M., Treasure, J., Tsuang, M. T., Witt, S. H., Woodside, D. B., Yilmaz, Z., Zillich, L., Adolfsson, R., Agartz, I., Air, T. M., Alda, M., Alfredsson, L., Andreassen, O. A., Anjorin, A., Appadurai, V., Soler Artigas, M., Van der Auwera, S., Azevedo, M. H., Bass, N., Bau, C. H. D., Baune, B. T., Bellivier, F., Berger, K., Biernacka, J. M., Bigdeli, T. B., Binder, E. B., Boehnke, M., Boks, M. P., Bosch, R., Braff, D. L., Bryant, R., Budde, M., Byrne, E. M., Cahn, W., Casas, M., Castelao, E., Cervilla, J. A., Chaumette, B., Cichon, S., Corvin, A., Craddock, N., Craig, D., Degenhardt, F., Djurovic, S., Edenberg, H. J., Fanous, A. H., Foo, J. C., Forstner, A. J., Frye, M., Fullerton, J. M., Gatt, J. M., Gejman, P. V., Giegling, I., Grabe, H. J., Green, M. J., Grevet, E. H., Grigoroiu-Serbanescu, M., Gutierrez, B., Guzman-Parra, J., Hamilton, S. P., Hamshere, M. L., Hartmann, A., Hauser, J., Heilmann-Heimbach, S., Hoffmann, P., Ising, M., Jones, I., Jones, L. A., Jonsson, L., Kahn, R. S., Kelsoe, J. R., Kendler, K. S., Kloiber, S., Koenen, K. C., Kogevinas, M., Konte, B., Krebs, M. -O., Landen, M., Lawrence, J., Leboyer, M., Lee, P. H., Levinson, D. F., Liao, C., Lissowska, J., Lucae, S., Mayoral, F., Mcelroy, S. L., Mcgrath, P., Mcguffin, P., Mcquillin, A., Medland, S. E., Mehta, D., Melle, I., Milaneschi, Y., Mitchell, P. B., Molina, E., Morken, G., Mortensen, P. B., Muller-Myhsok, B., Nievergelt, C., Nimgaonkar, V., Nothen, M. M., O'Donovan, M. C., Ophoff, R. A., Owen, M. J., Pato, C., Pato, M. T., Penninx, B. W. J. H., Pimm, J., Pistis, G., Potash, J. B., Power, R. A., Preisig, M., Quested, D., Ramos-Quiroga, J. A., Reif, A., Ribases, M., Richarte, V., Rietschel, M., Rivera, M., Roberts, A., Roberts, G., Rouleau, G. A., Rovaris, D. L., Rujescu, D., Sanchez-Mora, C., Sanders, A. R., Schofield, P. R., Schulze, T. G., Scott, L. J., Serretti, A., Shi, J., Shyn, S. I., Sirignano, L., Sklar, P., Smeland, O. B., Smoller, J. W., Sonuga-Barke, E. J. S., Spalletta, G., Strauss, J. S., Swiatkowska, B., Trzaskowski, M., Turecki, G., Vilar-Ribo, L., Vincent, J. B., Volzke, H., Walters, J. T. R., Shannon Weickert, C., Weickert, T. W., Weissman, M. M., Williams, L. M., Wray, N. R., Zai, C. C., Ashley-Koch, A. E., Beckham, J. C., Hauser, E. R., Hauser, M. A., Kimbrel, N. A., Lindquist, J. H., Mcmahon, B., Oslin, D. W., Qin, X., Mattheisen, M., Abdellaoui, A., Adams, M. J., Agerbo, E., Andlauer, T. F. M., Bacanu, S. -A., Baekvad-Hansen, M., Beekman, A. T. F., Bryois, J., Buttenschon, H. N., Bybjerg-Grauholm, J., Cai, N., Christensen, J. H., Clarke, T. -K., Colodro-Conde, L., Couvy-Duchesne, B., Crawford, G. E., Davies, G., Derks, E. M., Direk, N., Dolan, C. V., Dunn, E. C., Eley, T. C., Escott-Price, V., Hassan Kiadeh, F. F., Finucane, H. K., Frank, J., Gaspar, H. A., Gill, M., Goes, F. S., Gordon, S. D., Weinsheimer, S. M., Wellmann, J., Willemsen, G., Wu, Y., Xi, H. S., Yang, J., Zhang, F., Arolt, V., Boomsma, D. I., Dannlowski, U., Depaulo, J. R., Domenici, E., Domschke, K., Esko, T., Grove, J., Hall, L. S., Hansen, C. S., Hansen, T. F., Herms, S., Hickie, I. B., Homuth, G., Horn, C., Hottenga, J. -J., Hougaard, D. M., Howard, D. M., Jansen, R., Jorgenson, E., Knowles, J. A., Kohane, I. S., Kraft, J., Kretzschmar, W. W., Kutalik, Z., Li, Y., Lind, P. A., Macintyre, D. J., Mackinnon, D. F., Maier, R. M., Maier, W., Marchini, J., Mbarek, H., Middeldorp, C. M., Mihailov, E., Milani, L., Mondimore, F. M., Montgomery, G. W., Mostafavi, S., Nauck, M., Ng, B., Nivard, M. G., Nyholt, D. R., O'Reilly, P. F., Oskarsson, H., Hayward, C., Heath, A. C., Lewis, G., Li, Q. S., Madden, P. A. F., Magnusson, P. K., Martin, N. G., Mcintosh, A. M., Metspalu, A., Mors, O., Nordentoft, M., Paciga, S. A., Pedersen, N. L., Painter, J. N., Pedersen, C. B., Pedersen, M. G., Peterson, R. E., Peyrot, W. J., Posthuma, D., Quiroz, J. A., Qvist, P., Rice, J. P., Riley, B. P., Mirza, S. S., Schoevers, R., Schulte, E. 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P., Stephens, B., Brophy, M. T., Humphries, D. E., Selva, L. E., Do, N., Shayan, S., Cho, K., Pyarajan, S., Hauser, E., Sun, Y., Zhao, H., Wilson, P., Mcardle, R., Dellitalia, L., Mattocks, K., Harley, J., Zablocki, C. J., Whittle, J., Jacono, F., Gutierrez, S., Gibson, G., Hammer, K., Kaminsky, L., Villareal, G., Kinlay, S., Xu, J., Hamner, M., Mathew, R., Bhushan, S., Iruvanti, P., Godschalk, M., Ballas, Z., Ivins, D., Mastorides, S., Moorman, J., Gappy, S., Klein, J., Ratcliffe, N., Florez, H., Okusaga, O., Murdoch, M., Sriram, P., Yeh, S. S., Tandon, N., Jhala, D., Aguayo, S., Cohen, D., Sharma, S., Liangpunsakul, S., Oursler, K. A., Whooley, M., Ahuja, S., Constans, J., Meyer, P., Greco, J., Rauchman, M., Servatius, R., Gaddy, M., Wallbom, A., Morgan, T., Stapley, T., Sherman, S., Ross, G., Tsao, P., Strollo, P., Boyko, E., Meyer, L., Gupta, S., Huq, M., Fayad, J., Hung, A., Lichy, J., Hurley, R., Robey, B., Striker, R., Erlangsen, A., Kessler, R. C., Porteous, D., Ursano, R. J., Wasserman, D., Coon, H., Demontis, D., Docherty, A. R., Kuo, P. -H., Mann, J. J., Renteria, M. E., Stein, M. B., Willour, V., Psychiatry, Biological Psychology, APH - Methodology, APH - Mental Health, APH - Health Behaviors & Chronic Diseases, AMS - Sports, AMS - Ageing & Vitality, APH - Personalized Medicine, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Institute for Molecular Medicine Finland, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, HUS Psychiatry, Department of Public Health, Clinicum, Nuorisopsykiatria, Faculty Common Matters (Faculty of Social Sciences), Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Biostatistics Helsinki, Anna Keski-Rahkonen / Principal Investigator, Elisabeth Ingrid Maria Widen / Principal Investigator, Genomic Discoveries and Clinical Translation, Internal medicine, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Digital Health, Mullins N., Kang J., Campos A.I., Coleman J.R.I., Edwards 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E.Z., Saito T., Schall U., Schalling M., Scott R.J., Weickert C.S., Stordal E., Vaaler A.E., Vieta E., Waldman I.D., Zwart J.-A., Nurnberger J.I., Stahl E.A., Di Florio A., Adan R.A.H., Ando T., Aschauer H., Baker J.H., Bencko V., Birgegard A., Boden J.M., Boehm I., Boni C., Perica V.B., Buehren K., Bulik C.M., Burghardt R., Carlberg L., Cassina M., Clementi M., Cone R.D., Courtet P., Crowley J.J., Danner U.N., Davis O.S.P., de Zwaan M., Dedoussis G., Degortes D., DeSocio J.E., Dick D.M., Dina C., Dmitrzak-Weglarz M., Martinez E.D., Duncan L.E., Egberts K., Mattingsdal M., McDevitt S., Meulenbelt I., Micali N., Mitchell J., Mitchell K., Monteleone P., Monteleone A.M., Munn-Chernoff M.A., Nacmias B., Navratilova M., Ntalla I., Olsen C.M., O'Toole J.K., Padyukov L., Palotie A., Pantel J., Papezova H., Parker R., Pearson J.F., Ehrlich S., Escaramis G., Espeseth T., Estivill X., Farmer A., Favaro A., Fischer K., Floyd J.A.B., Focker M., Foretova L., Forzan M., Franklin C.S., Gambaro G., Giuranna J., Giusti-Rodriquez P., Gonidakis F., Gordon S., Mayora M.G., Guillaume S., Hanscombe K.B., Hatzikotoulas K., Hebebrand J., Helder S.G., Henders A.K., Herpertz-Dahlmann B., Herzog W., Hinney A., Horwood L.J., Hubel C., Petersen L.V., Purves K.L., Raevuori A., Reichborn-Kjennerud T., Ricca V., Ripatti S., Ritschel F., Roberts M., Rybakowski F., Santonastaso P., Scherag A., Schmidt U., Schork N.J., Schosser A., Seitz J., Slachtova L., Slagboom P.E., Slof-Op 't Landt M.C.T., Slopien A., Soranzo N., Sorbi S., Southam L., Steen V.W., Huckins L.M., Hudson J.I., Imgart H., Inoko H., Janout V., Jordan J., Julia A., Kalsi G., Kaminska D., Kaprio J., Karhunen L., Karwautz A., Kas M.J.H., Kennedy M.A., Keski-Rahkonen A., Kiezebrink K., Kim Y.-R., Kirk K.M., Klareskog L., Knudsen G.P.S., Larsen J.T., Le Hellard S., Leppa V.M., Lichtenstein P., Lin B.D., Lundervold A., Luykx J., Maj M., Mannik K., Marsal S., Stuber G.D., Szatkiewicz J.P., Tachmazidou I., Tenconi E., Tortorella A., Tozzi F., Tsitsika A., Tyszkiewicz-Nwafor M., Tziouvas K., van Elburg A.A., van Furth E.F., Wade T.D., Wagner G., Walton E., Whiteman D.C., Wichmann H.E., Widen E., Yao S., Zeggini E., Zerwas S., Zipfel S., Jungkunz M., Dietl L., Schwarze C.E., Dahmen N., Schott B.H., Mobascher A., Crivelli S., Dennis M.F., Harvey P.D., Carter B.W., Huffman J.E., Jacobson D., Madduri R., Olsen M.K., Pestian J., Gaziano J.M., Muralidhar S., Ramoni R., Beckham J., Chang K.-M., O'Donnell C.J., Tsao P.S., Breeling J., Huang G., Romero J.P.C., Moser J., Whitbourne S.B., Brewer J.V., Aslan M., Connor T., Argyres D.P., Stephens B., Brophy M.T., Humphries D.E., Selva L.E., Do N., Shayan S., Cho K., Pyarajan S., Hauser E., Sun Y., Zhao H., Wilson P., McArdle R., Dellitalia L., Mattocks K., Harley J., Zablocki C.J., Whittle J., Jacono F., Gutierrez S., Gibson G., Hammer K., Kaminsky L., Villareal G., Kinlay S., Xu J., Hamner M., Mathew R., Bhushan S., Iruvanti P., Godschalk M., Ballas Z., Ivins D., Mastorides S., Moorman J., Gappy S., Klein J., Ratcliffe N., Florez H., Okusaga O., Murdoch M., Sriram P., Yeh S.S., Tandon N., Jhala D., Aguayo S., Cohen D., Sharma S., Liangpunsakul S., Oursler K.A., Whooley M., Ahuja S., Constans J., Meyer P., Greco J., Rauchman M., Servatius R., Gaddy M., Wallbom A., Morgan T., Stapley T., Sherman S., Ross G., Tsao P., Strollo P., Boyko E., Meyer L., Gupta S., Huq M., Fayad J., Hung A., Lichy J., Hurley R., Robey B., Striker R., Erlangsen A., Kessler R.C., Porteous D., Ursano R.J., Wasserman D., Coon H., Demontis D., Docherty A.R., Kuo P.-H., Mann J.J., Renteria M.E., Stein M.B., and Willour V.

Subjects
LD SCORE REGRESSION, Genome-wide association study, Suicide, Attempted, 3124 Neurology and psychiatry, 0302 clinical medicine, Risk Factors, Insomnia, Suicide attempt, GWAS, Suïcidi, Depression (differential diagnoses), Cause of death, Psychiatry, 0303 health sciences, Factors de risc en les malalties, Mental Disorders, Genetic Correlation, Genome-wide Association Study, Pleiotropy, Polygenicity, Suicide, Suicide Attempt, DEPRESSION, 3. Good health, Genetic correlation, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Depressive Disorder, Major, Mental illness, Cohort, SEX, medicine.symptom, Human, medicine.medical_specialty, Risk factors in diseases, BF, Locus (genetics), BEHAVIORS, Psykiatri, EVENTS, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, ddc:610, GENOME-WIDE ASSOCIATION, IDEATION, Socioeconomic status, METAANALYSIS, Biological Psychiatry, 030304 developmental biology, business.industry, Risk Factor, Genetic architecture, THOUGHTS, RC0321, business, Malalties mentals, 030217 neurology & neurosurgery
Abstract

Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu), which is supported by the Office of Research Infrastructure of the National Institutes of Health (Grant Nos. S10OD018522 and S10OD026880). This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. This work was funded by the National Institutes of Health (Grant Nos. R01MH116269 and R01MH121455 [to DMR]), NIGMS of the National Institutes of Health (Grant No. T32GM007347 [to JK]), and the Brain & Behavior Research Foundation (NARSAD Young Investigator Award No. 29551 [to NM])., BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders., Office of Research Infrastructure of the National Institutes of Health S10OD018522 S10OD026880, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA R01MH116269 R01MH121455, NIH National Institute of General Medical Sciences (NIGMS) T32GM007347 NARSAD 29551

Published
2022

7. Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Author

S. Hong Lee, Cathryn M. Lewis, Ken B. Hanscombe, Oliver Pain, Jonathan R. I. Coleman, Alexandra Gillett, Bradley Jermy, David M. Howard, and Saskia P. Hagenaars

Subjects
Multivariate statistics, business.industry, Norm (group), Covariate, Medicine, Quantitative trait locus, business, Biobank, Mental health, Neuroticism, Depression (differential diagnoses), Clinical psychology
Abstract

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Published
2021

8. Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression

Author

Catharina A. Hartman, Azmeraw T. Amare, Zoha Kamali, Yi-Hsiang Hsu, Henning Tiemeier, Ute Bültmann, Harold Snieder, David M. Howard, Andrew M. McIntosh, Nese Direk, Ahmad Vaez, Epidemiology, Psychiatry, Amare, Azmeraw T, Vaez, Ahmad, Hsu, Yi-Hsiang, Direk, Nese, Kamali, Zoha, Howard, David M, McIntosh, Andrew M, Tiemeier, Henning, Bültmann, Ute, Snieder, Harold, Hartman, Catharina A, Public Health Research (PHR), Life Course Epidemiology (LCE), and Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE)

Subjects
0301 basic medicine, Male, Bipolar Disorder, Genome-wide association study, behavioral disciplines and activities, Article, neuroscience, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, mental disorders, medicine, Genetics, GWAS, Humans, genetics, Genetic Predisposition to Disease, Bipolar disorder, Molecular Biology, Depression (differential diagnoses), Genetic association, RISK, Depressive Disorder, Major, IDENTIFICATION, business.industry, Depression, medicine.disease, Phenotype, Twin study, Psychiatry and Mental health, 030104 developmental biology, Schizophrenia, Genetic Loci, DISEASES, depression, Major depressive disorder, Female, Self Report, business, 030217 neurology & neurosurgery, Clinical psychology, Neuroscience, Genome-Wide Association Study
Abstract

Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (rg ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general. Refereed/Peer-reviewed

Published
2019

9. Multiple measures of depression to enhance validity of major depressive disorder in the UK Biobank

Author

Paul F. O'Reilly, Cathryn M. Lewis, Bradley Jermy, Ryan Arathimos, Christopher Hübel, Gerome Breen, Oliver Pain, Jonathan R. I. Coleman, David M. Howard, Ken B. Hanscombe, and Kylie P. Glanville

Subjects
Depressive disorders, UK Biobank, 03 medical and health sciences, 0302 clinical medicine, Medicine, Academic Psychiatry, genetics, Depression (differential diagnoses), 030304 developmental biology, 0303 health sciences, business.industry, prospective health study, Heritability, CIDI, medicine.disease, Explained variation, Mental health, Biobank, Psychiatry and Mental health, classification, Sample size determination, Papers, Major depressive disorder, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract

Background The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders. Aims To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD). Method In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study. Results The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons. Conclusions Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.

Published
2021

10. 13. EXPLORING POLYGENIC-ENVIRONMENT AND NONGENETIC-ENVIRONMENT INTERACTIONS FOR DEPRESSIVE SYMPTOMS WITHIN THE UK BIOBANK

Author

Oliver Pain, David M. Howard, Saskia P. Hagenaars, Cathryn M. Lewis, Ken B. Hanscombe, Alexandra Gillett, Bradley Jermy, Jonathan R. I. Coleman, and Sang Hong Lee

Subjects
Pharmacology, Psychiatry and Mental health, medicine.medical_specialty, Neurology, business.industry, Medicine, Pharmacology (medical), Neurology (clinical), business, Psychiatry, Biobank, Biological Psychiatry, Depressive symptoms
Published
2021

11. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Author

Guiyan Ni, Jian Zeng, Joana A. Revez, Ying Wang, Zhili Zheng, Tian Ge, Restuadi Restuadi, Jacqueline Kiewa, Dale R. Nyholt, Jonathan R.I. Coleman, Jordan W. Smoller, Jian Yang, Peter M. Visscher, Naomi R. Wray, Stephan Ripke, Benjamin M. Neale, Aiden Corvin, James T.R. Walters, Kai-How Farh, Peter A. Holmans, Phil Lee, Brendan Bulik-Sullivan, David A. Collier, Hailiang Huang, Tune H. Pers, Ingrid Agartz, Esben Agerbo, Margot Albus, Madeline Alexander, Farooq Amin, Silviu A. Bacanu, Martin Begemann, Richard A. Belliveau, Judit Bene, Sarah E. Bergen, Elizabeth Bevilacqua, Tim B. Bigdeli, Donald W. Black, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, William Byerley, Wiepke Cahn, Guiqing Cai, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Stanley V. Catts, Kimberley D. Chambert, Raymond C.K. Chan, Ronald Y.L. Chen, Eric Y.H. Chen, Wei Cheng, Eric F.C. Cheung, Siow Ann Chong, C. Robert Cloninger, David Cohen, Nadine Cohen, Paul Cormican, Nick Craddock, James J. Crowley, Michael Davidson, Kenneth L. Davis, Franziska Degenhardt, Jurgen Del Favero, Ditte Demontis, Dimitris Dikeos, Timothy Dinan, Srdjan Djurovic, Gary Donohoe, Elodie Drapeau, Jubao Duan, Frank Dudbridge, Naser Durmishi, Peter Eichhammer, Johan Eriksson, Valentina Escott-Price, Laurent Essioux, Ayman H. Fanous, Martilias S. Farrell, Josef Frank, Lude Franke, Robert Freedman, Nelson B. Freimer, Marion Friedl, Joseph I. Friedman, Menachem Fromer, Giulio Genovese, Lyudmila Georgieva, Ina Giegling, Paola Giusti-Rodríguez, Stephanie Godard, Jacqueline I. Goldstein, Vera Golimbet, Srihari Gopal, Jacob Gratten, Lieuwe de Haan, Christian Hammer, Marian L. Hamshere, Mark Hansen, Thomas Hansen, Vahram Haroutunian, Annette M. Hartmann, Frans A. Henskens, Stefan Herms, Joel N. Hirschhorn, Per Hoffmann, Andrea Hofman, Mads V. Hollegaard, David M. Hougaard, Masashi Ikeda, Inge Joa, Antonio Julià, René S. Kahn, Luba Kalaydjieva, Sena Karachanak-Yankova, Juha Karjalainen, David Kavanagh, Matthew C. Keller, James L. Kennedy, Andrey Khrunin, Yunjung Kim, Janis Klovins, James A. Knowles, Bettina Konte, Vaidutis Kucinskas, Zita Ausrele Kucinskiene, Hana Kuzelova-Ptackova, Anna K. Kähler, Claudine Laurent, Jimmy Lee, S. Hong Lee, Sophie E. Legge, Bernard Lerer, Miaoxin Li, Tao Li, Kung-Yee Liang, Jeffrey Lieberman, Svetlana Limborska, Carmel M. Loughland, Jan Lubinski, Jouko Lönnqvist, Milan Macek, Patrik K.E. Magnusson, Brion S. Maher, Wolfgang Maier, Jacques Mallet, Sara Marsal, Manuel Mattheisen, Morten Mattingsdal, Robert W. McCarley, Colm McDonald, Andrew M. McIntosh, Sandra Meier, Carin J. Meijer, Bela Melegh, Ingrid Melle, Raquelle I. Mesholam-Gately, Andres Metspalu, Patricia T. Michie, Lili Milani, Vihra Milanova, Younes Mokrab, Derek W. Morris, Ole Mors, Kieran C. Murphy, Robin M. Murray, Inez Myin-Germeys, Bertram Müller-Myhsok, Mari Nelis, Igor Nenadic, Deborah A. Nertney, Gerald Nestadt, Kristin K. Nicodemus, Liene Nikitina-Zake, Laura Nisenbaum, Annelie Nordin, Eadbhard O’Callaghan, Colm O’Dushlaine, F. Anthony O’Neill, Sang-Yun Oh, Ann Olincy, Line Olsen, Jim Van Os, Psychosis Endophenotypes International Consortium, Christos Pantelis, George N. Papadimitriou, Sergi Papiol, Elena Parkhomenko, Michele T. Pato, Tiina Paunio, Milica Pejovic-Milovancevic, Diana O. Perkins, Olli Pietiläinen, Jonathan Pimm, Andrew J. Pocklington, John Powell, Alkes Price, Ann E. Pulver, Shaun M. Purcell, Digby Quested, Henrik B. Rasmussen, Abraham Reichenberg, Mark A. Reimers, Alexander L. Richards, Joshua L. Roffman, Panos Roussos, Douglas M. Ruderfer, Veikko Salomaa, Alan R. Sanders, Ulrich Schall, Christian R. Schubert, Thomas G. Schulze, Sibylle G. Schwab, Edward M. Scolnick, Rodney J. Scott, Larry J. Seidman, Jianxin Shi, Engilbert Sigurdsson, Teimuraz Silagadze, Jeremy M. Silverman, Kang Sim, Petr Slominsky, Hon-Cheong So, Chris C.A. Spencer, Eli A. Stahl, Hreinn Stefansson, Stacy Steinberg, Elisabeth Stogmann, Richard E. Straub, Eric Strengman, Jana Strohmaier, T. Scott Stroup, Mythily Subramaniam, Jaana Suvisaari, Dragan M. Svrakic, Jin P. Szatkiewicz, Erik Söderman, Srinivas Thirumalai, Draga Toncheva, Sarah Tosato, Juha Veijola, John Waddington, Dermot Walsh, Dai Wang, Qiang Wang, Bradley T. Webb, Mark Weiser, Dieter B. Wildenauer, Nigel M. Williams, Stephanie Williams, Stephanie H. Witt, Aaron R. Wolen, Emily H.M. Wong, Brandon K. Wormley, Hualin Simon Xi, Clement C. Zai, Xuebin Zheng, Fritz Zimprich, Kari Stefansson, Wellcome Trust Case-Control Consortium, Rolf Adolfsson, Ole A. Andreassen, Douglas H.R. Blackwood, Elvira Bramon, Joseph D. Buxbaum, Anders D. Børglum, Sven Cichon, Ariel Darvasi, Enrico Domenici, Hannelore Ehrenreich, Tõnu Esko, Pablo V. Gejman, Michael Gill, Hugh Gurling, Christina M. Hultman, Nakao Iwata, Assen V. Jablensky, Erik G. Jönsson, Kenneth S. Kendler, George Kirov, Jo Knight, Todd Lencz, Douglas F. Levinson, Qingqin S. Li, Jianjun Liu, Anil K. Malhotra, Steven A. McCarroll, Andrew McQuillin, Jennifer L. Moran, Preben B. Mortensen, Bryan J. Mowry, Markus M. Nöthen, Roel A. Ophoff, Michael J. Owen, Aarno Palotie, Carlos N. Pato, Tracey L. Petryshen, Danielle Posthuma, Marcella Rietschel, Brien P. Riley, Dan Rujescu, Pak C. Sham, Pamela Sklar, David St Clair, Daniel R. Weinberger, Jens R. Wendland, Thomas Werge, Mark J. Daly, Patrick F. Sullivan, Michael C. O’Donovan, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Tracy M. Air, Till F.M. Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T.F. Beekman, Elisabeth B. Binder, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Gregory E. Crawford, Gail Davies, Ian J. Deary, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Jerome C. Foo, Andreas J. Forstner, Héléna A. Gaspar, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Ian B. Hickie, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Howard, Marcus Ising, Rick Jansen, Ian Jones, Lisa A. Jones, Eric Jorgenson, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zoltán Kutalik, Yihan Li, Penelope A. Lind, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Paul F. O’Reilly, Hogni Oskarsson, Jodie N. Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E. Peterson, Wouter J. Peyrot, Giorgio Pistis, Jorge A. Quiroz, Per Qvist, John P. Rice, Margarita Rivera, Saira Saeed Mirza, Robert Schoevers, Eva C. Schulte, Ling Shen, Stanley I. Shyn, Grant C.B. Sinnamon, Johannes H. Smit, Daniel J. Smith, Fabian Streit, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Yunpeng Wang, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Yang Wu, Hualin S. Xi, Futao Zhang, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Udo Dannlowski, E.J.C. de Geus, J. Raymond DePaulo, Katharina Domschke, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, Stefan Kloiber, Glyn Lewis, Susanne Lucae, Pamela A.F. Madden, Patrik K. Magnusson, Nicholas G. Martin, Preben Bo Mortensen, Merete Nordentoft, Sara A. Paciga, Nancy L. Pedersen, Adult Psychiatry, APH - Mental Health, ANS - Complex Trait Genetics, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, Ni, Guiyan, Zeng, Jian, Revez, Joana A., Wang, Ying, Zhili, Zheng, Ge, Tian, Restuadi, Restuadi, Kiewa, Jacqueline, Nyholt, Dale R, Coleman, Jonathan RI, Smoller, Jordan W, Lee, S Hong, APH - Methodology, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Econometrics, Psychiatry, Department of Technology and Operations Management, Child and Adolescent Psychiatry / Psychology, Epidemiology, Erasmus MC other, Urology, Internal Medicine, Medical Informatics, Immunology, Human genetics, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Reproduction & Development (AR&D), and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep

Subjects
0301 basic medicine, Multifactorial Inheritance, medicine.medical_specialty, LDpred2, BF, Genomics, Disease, Major depressive disorder, risk prediction, 03 medical and health sciences, 0302 clinical medicine, MegaPRS, SDG 3 - Good Health and Well-being, medicine, Humans, Genetic Predisposition to Disease, ddc:610, Genetic risk, Psychiatry, Biological Psychiatry, Genetic association, Depressive Disorder, Major, business.industry, Mental Disorders, medicine.disease, Genetic architecture, Polygenic scores, Risk prediction, polygenic scores, PRS-CS, psychiatric disorders, 030104 developmental biology, SBayesR, Schizophrenia, Cohort, Lassosum, RC0321, business, Psychiatric disorders, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

BACKGROUND: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.METHODS: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.RESULTS: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.CONCLUSIONS: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

Published
2021

12. Multiple measures of depression to enhance validity of Major Depressive Disorder in the UK Biobank

Author

David M. Howard, Ryan Arathimos, Oliver Pain, Paul F. O'Reilly, Gerome Breen, Ken B. Hanscombe, Kylie P. Glanville, Bradley Jermy, Christopher Hübel, Jonathan R. I. Coleman, and Cathryn M. Lewis

Subjects
business.industry, Sample size determination, medicine, Major depressive disorder, Genome-wide association study, Heritability, medicine.disease, business, CIDI, Explained variation, Mental health, Biobank, Clinical psychology
Abstract

BackgroundThe UK Biobank (UKB) contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders.AimsTo investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of Major Depressive Disorder.MethodsIn participants who did not complete the MHQ (n = 325k), we calculated the number of other depression measures endorsed, e.g. from hospital episode statistics and interview data. We compared the strength of the genetic contribution in cases defined this way, to CIDI-defined cases. We compared the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, SNP-based heritability, and genetic correlations with summary statistics from the Psychiatric Genomics Consortium Major Depressive Disorder (PGC MDD) GWAS.ResultsThe strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% with one measure of depression to 21% with four or five. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UKB and PGC MDD exceeded 0.7, but there was variability between pairwise comparisons.ConclusionsMultiple measures of depression can serve as a reliable approximation for case-status where the CIDI measure is not available, with the implication that sample size can be optimised using the full suite of UKB data.

Published
2020

13. Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden

Author

Riccardo E. Marioni, Ian J. Deary, Steve Horvath, Rosie M. Walker, Anna J. Stevenson, David M. Howard, David J. Porteous, Robert F. Hillary, Caroline Hayward, Andrew M. McIntosh, Kathryn L. Evans, Archie Campbell, Daniel L. McCartney, and Craig W. Ritchie

Subjects
Epigenomics, Male, epigenetic age acceleration, Aging, Epidemiology, Epigenetic age acceleration, Myocardial Ischemia, biological ageing, Disease, Epigenesis, Genetic, Pulmonary Disease, Chronic Obstructive, 03 medical and health sciences, 0302 clinical medicine, Cost of Illness, Predictive Value of Tests, Cause of Death, Prevalence, Genetics, Humans, Medicine, Molecular Biology, Genetics (clinical), Disease burden, 030304 developmental biology, 2. Zero hunger, 0303 health sciences, COPD, DNA methylation, business.industry, Incidence, Research, Incidence (epidemiology), Hazard ratio, dNaM, Middle Aged, Biological ageing, medicine.disease, 3. Good health, Diabetes Mellitus, Type 2, Scotland, Ageing, Female, epidemiology, business, Body mass index, 030217 neurology & neurosurgery, Developmental Biology, Demography
Abstract

Background Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These measures include five ‘epigenetic clocks’ which provide an index of how much an individual’s biological age differs from their chronological age at the time of measurement. The five clocks encompass methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). A sixth epigenetic measure of ageing differs from these clocks in that it acts as a speedometer providing a single time-point measurement of the pace of an individual’s biological ageing. This measure of ageing is termed DunedinPoAm. In this study, we test the association between these six epigenetic measures of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries (n ≤ 9537, Generation Scotland: Scottish Family Health Study). Results DNAm GrimAge predicted incidence of clinically diagnosed chronic obstructive pulmonary disease (COPD), type 2 diabetes and ischemic heart disease after 13 years of follow-up (hazard ratios = 2.22, 1.52 and 1.41, respectively). DunedinPoAm predicted the incidence of COPD and lung cancer (hazard ratios = 2.02 and 1.45, respectively). DNAm PhenoAge predicted incidence of type 2 diabetes (hazard ratio = 1.54). DNAm Telomere Length associated with the incidence of ischemic heart disease (hazard ratio = 0.80). DNAm GrimAge associated with all-cause mortality, the prevalence of COPD and spirometry measures at the study baseline. These associations were present after adjusting for possible confounding risk factors including alcohol consumption, body mass index, deprivation, education and tobacco smoking and surpassed stringent Bonferroni-corrected significance thresholds. Conclusions Our data suggest that epigenetic measures of ageing may have utility in clinical settings to complement gold-standard methods for disease assessment and management.

Published
2020

14. Using Major Depression Polygenic Risk Scores to Explore the Depressive Symptom Continuum

Author

Cathryn M. Lewis, Bradley Jermy, Jonathan R. I. Coleman, Evangelos Vassos, Saskia P. Hagenaars, Kylie P. Glanville, Gerome Breen, and David M. Howard

Subjects
Multifactorial Inheritance, Patient Health Questionnaire, Structural equation modeling, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Humans, Medicine, p-value, Risk factor, Association (psychology), Categorical variable, Applied Psychology, Depression (differential diagnoses), 030304 developmental biology, Depressive Disorder, Major, 0303 health sciences, Continuum (measurement), Depression, business.industry, Confirmatory factor analysis, 030227 psychiatry, Psychiatry and Mental health, Observational study, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract

BackgroundMajor depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression.MethodsFactor analysis on symptom-level data from the UK Biobank (N= 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (N= 119 692).ResultsConfirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardisedβrange: 0.057–0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (pvalue range: 2.23 × 10−3–3.94 × 10−7).ConclusionsAn association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

Published
2020

15. Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden

Author

Archie Campbell, Steve Horvath, Kathryn L. Evans, Caroline Hayward, Anna J. Stevenson, David M. Howard, Ian J. Deary, Andrew M. McIntosh, Craig W. Ritchie, David J. Porteous, Daniel L. McCartney, Robert F. Hillary, Riccardo E. Marioni, and Rosie M. Walker

Subjects
2. Zero hunger, Oncology, 0303 health sciences, medicine.medical_specialty, business.industry, Incidence (epidemiology), Confounding, Hazard ratio, dNaM, Disease, Odds ratio, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, business, Body mass index, 030217 neurology & neurosurgery, Disease burden, 030304 developmental biology
Abstract

Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These include methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). In this study, we test the association between these epigenetic markers of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries. Furthermore, we test the clocks’ relationships with phenotypic measures associated with these conditions, including spirometric and biochemical traits. We carry out these analyses in 9,537 individuals from the Generation Scotland: Scottish Family Health Study. We find that DNAm GrimAge outperforms other epigenetic clocks in its associations with self-report disease prevalence and related clinical traits. DNAm GrimAge associates with chronic obstructive pulmonary disease (COPD) prevalence (Odds Ratio = 3.29, P = 3.0 × 10-4) and pulmonary spirometry tests (β = [-0.10 to −0.15], P = [1.4 × 10-4 to 1.4 × 10-6]) at study baseline after adjusting for possibly confounding risk factors including alcohol, body mass index, deprivation, education and smoking. After adjusting for these confounding risk factors, DNAm GrimAge, DNAm PhenoAge and DNAm Telomere Length, measured at study baseline, predict incidence of ICD-10-coded disease states including COPD, type 2 diabetes and cardiovascular disease after thirteen years of follow-up (Hazard Ratios = [0.80 (telomere length) to 2.19 (GrimAge)], P = [9.9 × 10-4, 1.9 × 10-14]). Our data show that despite accounting for several possible confounding variables, epigenetic markers of ageing predict incidence of common disease. This may have significant implications for their potential utility in clinical settings to complement gold-standard methods of clinical assessment and management.

Published
2020

16. Genetic comorbidity between major depression and cardio‐metabolic traits, stratified by age at onset of major depression

Author

Gerome Breen, Patrick F. Sullivan, Stephan Ripke, John Whitfield, Andrew M. McIntosh, Ian Jones, Tracy Air, Elisabeth B. Binder, Jonathan R. I. Coleman, Volker Arolt, Mark Adams, Douglas Blackwood, Saskia P. Hagenaars, Shing Wan Choi, Bernhard T. Baune, Marcus Ising, Stanley I. Shyn, Yuri Milaneschi, Lisa Jones, Archie Campbell, Giorgio Pistis, Nicholas G. Martin, Karen Hodgson, Katharina Domschke, David M. Howard, Steven P. Hamilton, Susanne Lucae, Zoltán Kutalik, Eco J. C. de Geus, Jouke-Jan Hottenga, Brenda W.J.H. Penninx, Udo Dannlowski, Martin Preisig, Naomi R. Wray, Matthew Traylor, David J. Porteous, Ian J. Deary, Cathryn M. Lewis, Caroline Hayward, Sandosh Padmanabhan, Bertram Mueller-Myhsok, Till F. M. Andlauer, Ian B. Hickie, Helena Gaspar, Dorret I. Boomsma, Micah Cearns, Michael John Owen, Darina Czamara, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Digital Health, Biological Psychology, APH - Methodology, APH - Health Behaviors & Chronic Diseases, and APH - Personalized Medicine

Subjects
Male, Netherlands Twin Register (NTR), Multifactorial Inheritance, Genome-wide association study, Comorbidity, Coronary Artery Disease, Disease, Type 2 diabetes, Linkage Disequilibrium, Body Mass Index, Coronary artery disease, Databases, Genetic, genetics, Age of Onset, Genetics (clinical), Depression (differential diagnoses), Metabolic Syndrome, Age Factors, ddc, Stroke, Psychiatry and Mental health, Phenotype, polygenic risk scores, depression, Female, cardio-metabolic disease, age at onset, medicine.medical_specialty, Genotype, Late onset, Polymorphism, Single Nucleotide, Article, Cellular and Molecular Neuroscience, SDG 3 - Good Health and Well-being, Internal medicine, medicine, Humans, Genetic Predisposition to Disease, Genetic Association Studies, Depressive Disorder, Major, business.industry, Cardiometabolic Risk Factors, medicine.disease, Diabetes Mellitus, Type 2, Case-Control Studies, RC0321, business, Body mass index, Genome-Wide Association Study
Abstract

INTRODUCTION: It’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. METHODS: Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. RESULTS: All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. CONCLUSIONS: The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

Published
2019

17. Epigenetic prediction of major depressive disorder

Author

Andrew M. McIntosh, Stewart W. Morris, David M. Howard, Rosie M. Walker, David J. Porteous, Riccardo E. Marioni, Ian J. Deary, Miruna C. Barbu, Toni-Kim Clarke, Heather C. Whalley, and Kathryn L. Evans

Subjects
Oncology, medicine.medical_specialty, Framingham Risk Score, business.industry, dNaM, medicine.disease, behavioral disciplines and activities, Environmental risk, CpG site, Internal medicine, mental disorders, DNA methylation, medicine, Major depressive disorder, Smoking status, Epigenetics, business
Abstract

ObjectiveDNA methylation (DNAm) is associated with environmental risk factors for major depressive disorder (MDD) but has not yet been tested for its ability to discriminate individuals with MDD from unaffected individuals.MethodsUsing penalized regression based on genome-wide CpG methylation, we trained a DNAm risk score of MDD (DNAm-RS) in 1,223 cases and 1,824 controls and tested in a second independent sample of 363 prevalent cases and 1,417 controls. Using DNA from 1,607 unaffected individuals, we tested whether DNAm-RS could discriminate the 190 incident cases of lifetime MDD from the 1,417 individuals who remained unaffected at follow-up.ResultsA weighted linear combination of 196 CpG sites were derived from the training sample to form a DNAm-RS. The DNAm-RS explained 1.75% of the variance in MDD risk in an independent case-control sample and significantly predicted future incident episodes of MDD at follow up (R2=0.52%). DNAm-RS and MDD polygenic risk scores together additively explained 3.99% of the variance in prevalent MDD. The DNAm-RS was also significantly associated with lifestyle factors associated with MDD, including smoking status (β=0.440, p=−16) and alcohol use (β=0.092, p=9.85×10−5). The DNAm-RS remained significantly associated with MDD after adjustment for these environmental factors (independent association: β=0.338, p=1.17×10−7 association post-adjustment: β=0.081, p=0.0006).ConclusionsA novel risk score of MDD based on DNAm data significantly discriminated MDD cases from controls in an independent dataset, and controls who would subsequently develop MDD from those who remained unaffected. DNAm-RS captured the effects of exposure to key lifestyle risk factors for MDD, revealing a potential role in risk stratification.

Published
2019

18. Insulin resistance:Genetic associations with depression and cognition in population based cohorts

Author

Mark Adams, Lynsey S. Hall, Ian J. Deary, Masoud Shirali, Blair H. Smith, Toni-Kim Clarke, Andrew M. McIntosh, David J. Porteous, Chris Haley, Alison D. Murray, Sandosh Padmanabhan, Sophia Frangou, Jude Gibson, and David M. Howard

Subjects
0301 basic medicine, Male, cognition, Aging, Multifactorial Inheritance, gentic association, medicine.medical_treatment, Intelligence, Cohort Studies, 0302 clinical medicine, Cognition, insulin resistance, Aged, 80 and over, education.field_of_study, Depression, Middle Aged, Verbal reasoning, polygenic risk scores, Neurology, depression, Female, Clinical psychology, Adult, Adolescent, Population, Risk Assessment, Article, 03 medical and health sciences, Young Adult, Insulin resistance, Developmental Neuroscience, Mendelian randomization, medicine, Humans, Genetic Predisposition to Disease, education, Genetic association, Aged, business.industry, Insulin, medicine.disease, Genetic architecture, United Kingdom, 030104 developmental biology, Scotland, Insulin Resistance, business, 030217 neurology & neurosurgery, Biomarkers
Abstract

Insulin resistance, broadly defined as the reduced ability of insulin to exert its biological action, has been associated with depression and cognitive dysfunction in observational studies. However, it is unclear whether these associations are causal and whether they might be underpinned by other shared factors. To address this knowledge gap, we capitalized on the stability of genetic biomarkers through the lifetime, and on their unidirectional relationship with depression and cognition. Specifically, we determined the association between quantitative measures of cognitive function and depression and genetic instruments of insulin resistance traits in two large-scale population samples, the Generation Scotland: Scottish Family Health Study (GS: SFHS; N = 19,994) and in the UK Biobank (N = 331,374). In the GS:SFHS, the polygenic risk score (PRS) for fasting insulin was associated with verbal intelligence and depression while the PRS for the homeostasis model assessment of insulin resistance was associated with verbal intelligence. Despite this overlap in genetic architecture, Mendelian randomization analyses in the GS:SFHS and in the UK Biobank samples did not yield evidence for causal associations from insulin resistance traits to either depression or cognition. These findings may be due to weak genetic instruments, limited cognitive measures and insufficient power but they may also indicate the need to identify other biological mechanisms that may mediate the relationship from insulin resistance to depression and cognition.

Published
2019

19. Genetic comorbidity between major depression and cardio-metabolic disease, stratified by age at onset of major depression

Author

Ian J. Deary, Cathryn M. Lewis, Mark Adams, Steven P. Hamilton, Yuri Milaneschi, David M. Howard, Darina Czamara, Stanley I. Shyn, Jri Coleman, Giorgio Pistis, Tracy Air, Lisa Jones, Saskia P. Hagenaars, Elisabeth B. Binder, D. H. R. Blackwood, P.F. Sullivan, Naomi R. Wray, Tfm Andlauer, Bernhardt T. Baune, David J. Porteous, Udo Dannlowski, D.I. Boomsma, Karen Hodgson, V. Arolt, Susanne Lucae, Stephan Ripke, Martin Preisig, Micah Cearns, M. J. Owen, Campbell A, Bwjh Penninx, N. G. Martin, Zoltán Kutalik, Ian Jones, Ian B. Hickie, Helena Gaspar, Caroline Hayward, John Whitfield, Andrew M. McIntosh, Sandosh Padmanabhan, Matthew Traylor, Ejc de Geus, Shing Wan Choi, Bertram Mueller-Myhsok, Marcus Ising, Katharina Domschke, J-J Hottenga, and Gerome Breen

Subjects
2. Zero hunger, 0303 health sciences, medicine.medical_specialty, business.industry, Late onset, Disease, Type 2 diabetes, medicine.disease, Comorbidity, 3. Good health, Coronary artery disease, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, business, Body mass index, Stroke, 030217 neurology & neurosurgery, Depression (differential diagnoses), 030304 developmental biology
Abstract

IntroductionIt’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression.MethodsPolygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank.ResultsAll cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression.ConclusionsThe phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

Published
2019
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20. Pharmaco-epidemiology of antidepressant exposure in a UK cohort record-linkage study

Author

Jonathan D. Hafferty, David M. Howard, Stephen M. Lawrie, David J. Porteous, Eleanor M. Wigmore, Archie Campbell, Mark Adams, Donald J. MacIntyre, Toni-Kim Clarke, Kristin K. Nicodemus, and Andrew M. McIntosh

Subjects
Adult, Male, medicine.medical_specialty, Medication Adherence, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Psychiatric medication, Epidemiology, Prevalence, medicine, Humans, Pharmacology (medical), 030212 general & internal medicine, Psychiatry, Pharmacology, business.industry, Incidence, Pharmacoepidemiology, Antidepressive Agents, Drug Utilization, 3. Good health, 030227 psychiatry, Psychiatry and Mental health, Scotland, Cohort, Antidepressant, Female, Record Linkage Study, business, High income countries, Record linkage
Abstract

Objectives: Antidepressants are the most commonly prescribed psychiatric medication but concern has been raised about significant increases in their usage in high income countries. We aimed to quantify antidepressant prevalence, incidence, adherence and predictors of use in the adult population. Methods: The study record-linked administrative prescribing and morbidity data to the Generation Scotland cohort ( N = 11,052), between 2009 and 2016. Prevalence and incidence of any antidepressant use was determined. Antidepressant adherence was measured using Proportion of Days Covered and Medication Possession Ratio. Time-to-event analysis for incident antidepressant use within 5 years of Generation Scotland: Scottish Family Health Study (GS:SFHS) recruitment was performed to reveal patient-level predictors of use. Results: Almost one-third (28.0%, 95%CI 26.9–29.1) of the adults in our sample were prescribed at least one antidepressant in the 5-year period 2012–2016. There was a 36.2% increase in annual prevalence between 2010 and 2016. Incidence was 2.4(2.1–2.7)% per year. The majority of antidepressant episodes (57.6%) were greater than 9 months duration and adherence was generally high (69.0% with Proportion of Days Covered >80%). Predictors of new antidepressant use included history of affective disorder, being female, physical comorbidities, higher neuroticism scores, and lower cognitive function scores. Conclusions: Antidepressant prevalence is greater than previously reported but incidence remains relatively stable. We found the majority of antidepressant episodes to be of relatively long duration with good estimated adherence. Our study supports the hypothesis that increased long-term use among existing (and returning) users, along with wider ranges of indications for antidepressants, has significantly increased the prevalence of these medications.

Published
2019

21. Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts

Author

Andrew M. McIntosh, Gail Davies, David M. Howard, Howard J. Edenberg, Mark Adams, Richard C. Crist, Sandra Sanchez-Roige, Abraham A. Palmer, Sarah L. Elson, Toni-Kim Clarke, Pierre Fontanillas, and Ian J. Deary

Subjects
Male, Epidemiology, Alcohol abuse, Genome-wide association study, Cardiovascular, Medical and Health Sciences, Cohort Studies, Substance Misuse, Alcohol Use and Health, 0302 clinical medicine, Medicine, 2.1 Biological and endogenous factors, Aetiology, Cation Transport Proteins, Psychiatry, Alcohol Use Disorders Identification Test, Alcohol Consumption, Depression, Substance Abuse, Adaptor Proteins, Single Nucleotide, the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Middle Aged, Substance abuse, Psychiatry and Mental health, Alcoholism, Mental Health, Meta-analysis, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Female, social and economic factors, Cohort study, Alcohol Dependence, medicine.medical_specialty, Alcohol Drinking, European Continental Ancestry Group, 23andMe Research Team, Article, White People, 03 medical and health sciences, Clinical Research, 2.3 Psychological, Environmental health, Genetics, Genome-Wide Association Studies, Humans, Genetic Predisposition to Disease, Polymorphism, Klotho Proteins, Depressive Disorder, business.industry, Whites, Alcohol dependence, Human Genome, Psychology and Cognitive Sciences, Alcohol Dehydrogenase, Signal Transducing, Alcohol Abuse, Membrane Proteins, Major, medicine.disease, United Kingdom, 030227 psychiatry, Brain Disorders, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Schizophrenia, business, Cell Adhesion Molecules, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

ObjectiveAlcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits.MethodThis study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P).ResultsThe GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects.ConclusionsAUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Published
2019

22. Correction: Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Author

Roseann E. Peterson, EM Byrne, M J Owen, Sven Cichon, Gcb Sinnamon, Jian Yang, Stephan Ripke, Andreas J. Forstner, Stephanie H. Witt, TM Air, Isaac S. Kohane, M. Rietschel, Tõnu Esko, Jakob Grove, Eske M. Derks, Hans-Jörgen Grabe, Christine Søholm Hansen, Hualin S. Xi, Kenneth I. Berger, A. C. Heath, Henry Völzke, Manuel Mattheisen, Bernard Ng, Hamdi Mbarek, Stefan Kloiber, Jodie N. Painter, Marianne Giørtz Pedersen, Jerome C. Foo, Carsten Horn, Yang Wu, Alexander Viktorin, Hilary K. Finucane, Paf Madden, Lili Milani, Katharina Domschke, Yun Li, Bernard T. Baune, I. Jones, M. M. Nöthen, Atf Beekman, Eric Jorgenson, Matthew Hotopf, Christopher Rayner, Giorgio Pistis, Stanley I. Shyn, J.H. Smit, A Abdellaoui, N. G. Martin, Paul F. O'Reilly, Enrico Domenici, Daphna Levinson, John P. Rice, Thomas Werge, Ling Shen, Catherine Schaefer, Andrea Danese, Jonathan Marchini, Na Cai, Michel G. Nivard, Scott D. Gordon, Shantel Weinsheimer, Steven P. Hamilton, G. Homuth, Yunpeng Wang, David M. Hougaard, Andres Metspalu, Nese Direk, Gonneke Willemsen, Francis M. Mondimore, J-J Hottenga, M. Gill, S. E. Medland, Donald M. Lyall, Peter Hoffmann, Merete Nordentoft, Udo Dannlowski, Stacy Steinberg, Tfm Andlauer, Ian J. Deary, Caroline Hayward, Cathryn M. Lewis, Penelope A. Lind, Nancy L. Pedersen, David J. Porteous, Hogni Oskarsson, D.I. Boomsma, Evelin Mihailov, Thorgeir E. Thorgeirsson, Evangelos Vassos, Rudolf Uher, Gary Davies, Gerome Breen, KW Choi, Christopher Hübel, Carol Kan, Sara A. Paciga, Kirstin L. Purves, Torben Hansen, Jri Coleman, Naomi R. Wray, Erin C. Dunn, Engilbert Sigurdsson, Bradley T. Webb, Jorge A. Quiroz, van, Hemert, Am, Christel M. Middeldorp, Jonas Bybjerg-Grauholm, Robert A. Schoevers, Maciej Trzaskowski, Jing Shi, Ole Mors, Alexander Teumer, Fernando S. Goes, S-A Bacanu, James B. Potash, David J. Smith, Niamh Mullins, EB Binder, J Bryois, Dean F. MacKinnon, Arolt, Daniel Umbricht, Andrew M. McIntosh, P. B. Mortensen, Anders D. Børglum, Futao Zhang, Susanne Lucae, W. Maier, Eva C. Schulte, Jens Treutlein, Carsten Bøcker Pedersen, Henning Tiemeier, Grant W. Montgomery, Trubetskoy, Thomas G. Schulze, Martin Preisig, Bwjh Penninx, TB Bigdeli, Thalia C. Eley, Shing Wan Choi, Robert Maier, E Agerbo, Katherine E. Tansey, P. McGuffin, James A. Knowles, de, Geus, Ejc, Franziska Degenhardt, Jane H. Christensen, Julia Kraft, Enrique Castelao, Ian B. Hickie, Helena Gaspar, Danielle Posthuma, K. Stefansson, Gregory E. Crawford, Wesley K. Thompson, Kas Davis, Jana Strohmaier, Henriette N. Buttenschøn, Margarita Rivera, Josef Frank, Van der Auwera S, Fabian Streit, Erik Pettersson, Peter M. Visscher, Donald J. MacIntyre, Qingqin S. Li, Rick Jansen, Conor V. Dolan, Matthias Nauck, Barbara Maughan, Escott-Price, Glyn Lewis, Patrick K.E. Magnusson, Henning Teismann, DePaulo, Saira Saeed Mirza, Sara Mostafavi, Kenneth S. Kendler, Matthew Traylor, Brien P. Riley, Roy H. Perlis, Patrick J. McGrath, Bertram Müller-Myhsok, Mark Adams, David M. Howard, Lucía Colodro-Conde, Lisa Hall, Divya Mehta, Nyholt, Y. Milaneschi, Jordan W. Smoller, M Baekvad-Hansen, Marcus Ising, M O'Donovan, Warren W. Kretzschmar, Baptiste Couvy-Duchesne, Wouter J. Peyrot, P. A. Thomson, P.F. Sullivan, Stefan Herms, Clarke T, Ffh Kiadeh, Jürgen Wellmann, Lisa Jones, A.G. Uitterlinden, Per Qvist, Z. Kutalik, Hreinn Stefansson, Myrna M. Weissman, and N. Craddock

Subjects
medicine.medical_specialty, SDG 16 - Peace, business.industry, SDG 16 - Peace, Justice and Strong Institutions, MEDLINE, Nearly Every Day, medicine.disease, Biobank, Genome, Justice and Strong Institutions, Cellular and Molecular Neuroscience, Psychiatry and Mental health, medicine, Major depressive disorder, Psychiatry, business, Molecular Biology, Depression (differential diagnoses)
Abstract

Following publication of this article, the authors noticed an error in Supplementary Table 1. In the original Supplementary Table 1, one of the criteria for control participants was incorrectly given as ‘Report extensive recent symptoms of depression: less than 14 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’. This has now been corrected to: ‘Report extensive recent symptoms of depression: less than 5 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’.

Published
2020

23. Impact of Polygenic Risk for Schizophrenia on Cortical Structure in UK Biobank

Author

Gail Davies, Andrew M. McIntosh, Ian J. Deary, Simon R. Cox, Mark Adams, Emma Neilson, Xueyi Shen, David M. Howard, Toni-Kim Clarke, Jude Gibson, Heather C. Whalley, Stephen M. Lawrie, Eleanor M. Wigmore, and Harris Ma

Subjects
0301 basic medicine, Oncology, Male, Risk, medicine.medical_specialty, Databases, Factual, Birth weight, Population, Negative association, Cortical surface area, Cortical volume, Cortical thickness, 03 medical and health sciences, 0302 clinical medicine, Neurodevelopmental disorder, Internal medicine, Medicine, Birth Weight, Humans, education, Biological Psychiatry, Aged, Cerebral Cortex, education.field_of_study, business.industry, Polygenic risk, Middle Aged, medicine.disease, Biobank, Magnetic Resonance Imaging, 030104 developmental biology, Schizophrenia, Polygenic risk score, Female, business, 030217 neurology & neurosurgery
Abstract

BACKGROUND: Schizophrenia is a neurodevelopmental disorder with many genetic variants of individually small effect contributing to phenotypic variation. Lower cortical thickness (CT), surface area, and cortical volume have been demonstrated in people with schizophrenia. Furthermore, a range of obstetric complications (e.g., lower birth weight) are consistently associated with an increased risk for schizophrenia. We investigated whether a high polygenic risk score for schizophrenia (PGRS-SCZ) is associated with CT, surface area, and cortical volume in UK Biobank, a population-based sample, and tested for interactions with birth weight.METHODS: Data were available for 2864 participants ( n male/ n female = 1382/1482; mean age = 62.35 years, SD = 7.40). Linear mixed models were used to test for associations among PGRS-SCZ and cortical volume, surface area, and CT and between PGRS-SCZ and birth weight. Interaction effects of these variables on cortical structure were also tested.RESULTS: We found a significant negative association between PGRS-SCZ and global CT; a higher PGRS-SCZ was associated with lower CT across the whole brain. We also report a significant negative association between PGRS-SCZ and insular lobe CT. PGRS-SCZ was not associated with birth weight and no PGRS-SCZ × birth weight interactions were found.CONCLUSIONS: These results suggest that individual differences in CT are partly influenced by genetic variants and are most likely not due to factors downstream of disease onset. This approach may help to elucidate the genetic pathophysiology of schizophrenia. Further investigation in case-control and high-risk samples could help identify any localized effects of PGRS-SCZ, and other potential schizophrenia risk factors, on CT as symptoms develop.

Published
2018

24. Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,932)

Author

Sandra Sanchez-Roige, Pierre Fontanillas, Toni-Kim Clarke, Ian J. Deary, Abraham A. Palmer, Mark Adams, Gail Davies, David M. Howard, Andrew M. McIntosh, Howard J. Edenberg, Richard C. Crist, and Sarah L. Elson

Subjects
0303 health sciences, education.field_of_study, Alcohol Use Disorders Identification Test, business.industry, Alcohol dependence, Population, ADH1B, Genome-wide association study, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Schizophrenia, Meta-analysis, medicine, Major depressive disorder, business, education, 030217 neurology & neurosurgery, 030304 developmental biology, Clinical psychology
Abstract

Alcohol use disorders (AUD) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test (AUDIT) from two population-based cohorts of European ancestry: UK Biobank (UKB; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption (AUDIT-C), and for items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (rg=0.22, p=3.0×10−10), major depressive disorder (rg=0.26, p=5.6×10−3), and attention-deficit/hyperactivity disorder (ADHD; rg=0.23, p=1.1×10−5), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (rg=−0.24, p=3.7×10−3) and ADHD (rg=−0.10, p=1.8×10−2). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of ≤4 as controls and ≥12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (rg=0.82, p=3.2×10−6) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.

Published
2018
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25. RESTING STATE FUNCTIONAL DYNAMICS IN MAJOR DEPRESSIVE DISORDER AND RELATIONSHIP WITH POLYGENIC RISK LOADING: EVIDENCE FROM UK BIOBANK

Author

Simon R. Cox, Xueyi Shen, David M. Howard, Heather C. Whalley, Andrew M. McIntosh, Mark Adams, and Jude Gibson

Subjects
Pharmacology, Resting state fMRI, medicine.diagnostic_test, business.industry, Precuneus, medicine.disease, behavioral disciplines and activities, Psychiatry and Mental health, medicine.anatomical_structure, Neurology, Neuroimaging, Posterior cingulate, mental disorders, Rumination, medicine, Major depressive disorder, Pharmacology (medical), Neurology (clinical), medicine.symptom, business, Functional magnetic resonance imaging, Neuroscience, Biological Psychiatry, Default mode network
Abstract

Background Major Depressive Disorder (MDD) is a common psychiatric illness. Neuroimaging studies have indicated the involvement functional networks in the disorder. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive, task-free way of studying such distributed functional dynamics of the brain. Previous studies have found significant alteration in rs-fMRI functional connectivity in MDD patients. Hyperactivity of default mode network (DMN) has been reported to be associated with rumination of negative emotional stimuli, and abnormal connectivity between DMN and task-positive networks was associated with cognitive impairment within MDD, however other studies do not report such effects. Previous rs-fMRI studies were however often conducted on small sample sizes which may account for inconsistency in the literature, and few studies were conducted on the association between genetic risk of MDD and rs-fMRI functional connectivity. Therefore, the present study used imaging data from a large-population based sample from UK Biobank to test the association of brain functional connectivity with MDD status and its genetic risk. Methods We used IDPs (Imaging-Derived Phenotypes) from UK Biobank imaging project. The imaging data was collected on a single scanner and preprocessed using FSL packages by UK Biobank. Data was parcelled into 21 non-noise functional networks, and the L2-regularised partial correlation was estimated between each pair of networks. These estimated connections were then used for further analysis in the current study. MDD status was derived based on self-reported depressive symptoms and history of hospital admission data (Case: N=256, Control: N=488,). Polygenic risk of MDD (MDD-pgrs) was calculated based on the summary statistics from the PGC (N=914). Regression models were applied to test the MDD case-control difference and the associations of MDD-pgrs with rs-fMRI functional connectivity. Age, age2, sex and assessment centre were controlled for in the model of case-control differences, and genotype batch and the first 15 principal component of genetic variance were additionally controlled for MDD-pgrs. FDR correction was applied. All effect sizes reported were standardised. Results MDD cases showed higher positive connectivity between the precuneus, the bilateral superior posterior cingulate cortex and temporal-parietal junction (β=0.292, pcorr=0.047); all components of DMN. Higher MDD-pgrs was associated with less connectivity between posterior motor area and posterior parietal gyrus (PPG) (β=-0.123, pcor=0.045), less negative connectivity between precuneus and PPG (β=0.122, pcor=0.045) and higher negative connection of right fronto-temporal network (FTN) and left fronto-parietal network (FPN) (β=-0.124, pcor=0.045). Discussion In the present study, MDD cases exhibited significantly higher connection between two DMN components, and a higher negative connection between FTN and FPN. Conversely, higher genetic risk for MDD was associated with decreased connectivity between DMN regions and task-positive networks. These results were highly consistent with previous studies which found that altered functioning of DMN was related with MDD status. The present study also found functional connections that involve with DMN, FTN and FPN associated with MDD-pgrs.

Published
2019

26. F43POLYGENIC RISK OF MAJOR DEPRESSIVE DISORDER IS ASSOCIATED WITH WHITE MATTER MICROSTRUCTURE: EVIDENCE FROM UK BIOBANK IMAGING STUDY OF 9,748 PARTICIPANTS

Author

Mark Adams, Jude Gibson, Andrew M. McIntosh, Heather C. Whalley, Xueyi Shen, and David M. Howard

Subjects
Pharmacology, medicine.medical_specialty, business.industry, Imaging study, medicine.disease, Biobank, White matter microstructure, Psychiatry and Mental health, Neurology, medicine, Major depressive disorder, Pharmacology (medical), Neurology (clinical), Psychiatry, business, Biological Psychiatry
Published
2019

27. Accelerated Epigenetic Ageing in Major Depressive Disorder

Author

Mark Adams, David J. Porteous, Jude Gibson, David M. Howard, Kathryn L. Evans, Lynsey S. Hall, Ian J. Deary, Toni-Kim Clarke, Andrew M. McIntosh, Heather C. Whalley, Rosie M. Walker, andMe Rsearch Team, Riccardo E. Marioni, and Stewart W. Morris

Subjects
2. Zero hunger, Oncology, 0303 health sciences, Genetic correlation analysis, Mediation (statistics), medicine.medical_specialty, business.industry, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, Ageing, Internal medicine, Etiology, medicine, Major depressive disorder, Epigenetics, business, Body mass index, 030217 neurology & neurosurgery, Depression (differential diagnoses), 030304 developmental biology
Abstract

BackgroundMajor depressive disorder (MDD) is a severe, heritable psychiatric disorder associated with shortened lifespan and comorbidities of advancing age. It is unknown however whether MDD is associated with accelerated biological ageing relative to chronological age. This hypothesis was tested using the epigenetic clock as a measure of biological age.MethodsTo address the main hypothesis, using peripheral blood, we derived measures of Epigenetic Age Acceleration (EAA) in 3,833 controls and 1,219 MDD cases based on Hannum and Horvath epigenetic clocks in Generation Scotland (GS:SFHS, mean age 48 years, std dev 14.5). Models controlled for relatedness, sex, cell counts, and processing batch (basic model), as well as additional covariates of smoking and drinking status, and body mass index (BMI) (full models).ResultsAccelerated epigenetic ageing was found in MDD cases versus controls using the Horvath clock (β=0.0804, p=0.012 equivalent to 0.20 years) in both the basic and full models. Significant MDD*age interactions indicated greatest effects at younger age ranges. No significant differences were observed for the Hannum clock. BMI was the only additional covariate found to attenuate the relationship between EAAHorvathand MDD. Further, genetic correlation analysis indicated significant overlap in the genetic aetiology of EAAHorvathwith BMI (rG=0.20, p=0.03), between MDD with BMI (rG=0.10, p=9.86×10−6), but not between EAAHorvathand MDD (rG=0.14, p=0.125). Mediation analysis indicated partial mediation of the relationship between EAAHorvathand depression status through BMI (β =0.0028; p=0.0248, ~13%).ConclusionThese data imply that accelerated biological ageing is associated with MDD and partially mediated through BMI.

Published
2017

28. Haplotype-based association analysis of general cognitive ability in Generation Scotland, the English Longitudinal Study of Ageing, and UK Biobank

Author

Alison D. Murray, Toni-Kim Clarke, Caroline Hayward, Saskia P. Hagenaars, G. David Batty, David J. Porteous, Reka Nagy, Donald M. Lyall, Andrew M. McIntosh, Chris Haley, Mark Adams, Eleanor M. Wigmore, Blair H. Smith, Yanni Zeng, Kathryn L. Evans, Ian J. Deary, David M. Howard, and Pippa A. Thomson

Subjects
0301 basic medicine, Longitudinal study, Candidate gene, Cognitive Neuroscience, Medicine (miscellaneous), Library science, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, cognitive ability, Genotype, Coding region, Medicine, Bipolar disorder, Genetic association, Genetics, haplotype association analysis, business.industry, Haplotype, Cognition, Articles, intelligence, medicine.disease, 030104 developmental biology, IQ, business, 030217 neurology & neurosurgery, Research Article
Abstract

Background:Cognitive ability is a heritable trait with a polygenic architecture, for which several associated variants have been identified using genotype-based and candidate gene approaches. Haplotype-based analyses are a complementary technique that take phased genotype data into account, and potentially provide greater statistical power to detect lower frequency variants.Methods:In the present analysis, three cohort studies (ntotal= 48,002) were utilised: Generation Scotland: Scottish Family Health Study (GS:SFHS), the English Longitudinal Study of Ageing (ELSA), and the UK Biobank. A genome-wide haplotype-based meta-analysis of cognitive ability was performed, as well as a targeted meta-analysis of several gene coding regions.Results:None of the assessed haplotypes provided evidence of a statistically significant association with cognitive ability in either the individual cohorts or the meta-analysis. Within the meta-analysis, the haplotype with the lowest observedP-value overlapped with the D-amino acid oxidase activator (DAOA) gene coding region. This coding region has previously been associated with bipolar disorder, schizophrenia and Alzheimer’s disease, which have all been shown to impact upon cognitive ability. Another potentially interesting region highlighted within the current genome-wide association analysis (GS:SFHS:P= 4.09 x 10-7), was the butyrylcholinesterase (BCHE) gene coding region. The protein encoded byBCHEhas been shown to influence the progression of Alzheimer’s disease and its role in cognitive ability merits further investigation.Conclusions:Although no evidence was found for any haplotypes with a statistically significant association with cognitive ability, our results did provide further evidence that the genetic variants contributing to the variance of cognitive ability are likely to be of small effect.

Published
2017

29. Genetic stratification of depression in UK Biobank suggests a subgroup linked to age of natural menopause

Author

Saskia P. Hagenaars, David M. Howard, Naomi R. Wray, Jonathan R. I. Coleman, Cathryn M. Lewis, Lasse Folkersen, Patrick F. Sullivan, David J. Porteous, Mark Adams, Buhm Han, Gerome Breen, Thomas Werge, Archie Campbell, Andrew M. McIntosh, Toni-Kim Clarke, and Kylie P. Glanville

Subjects
Menopause, Natural menopause, business.industry, Cohort, Etiology, Medicine, Effective treatment, business, medicine.disease, Biobank, Mental health, Depression (differential diagnoses), Demography
Abstract

Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations was used to test for evidence of subgroups by examining whether there was clustering of independent genetic variants associated with eleven other complex traits and disorders in people with depression. We found evidence of a subgroup within depression using age of natural menopause variants (P= 1.69 × 10−3) and this effect remained significant in females (P= 1.18 × 10−3), but not males (P= 0.186). However, no evidence for this subgroup (P> 0.05) was found in Generation Scotland, iPSYCH, a UK Biobank replication cohort or the GERA cohort. In the UK Biobank, having depression was also associated with a later age of menopause (beta = 0.34, standard error = 0.06,P= 9.92 × 10−8). A potential age of natural menopause subgroup within depression and the association between depression and a later age of menopause suggests that they partially share a developmental pathway.

Published
2017

30. 52 A PHENOME-WIDE ASSOCIATION AND MENDELIAN RANDOMISATION STUDY OF POLYGENIC RISK FOR DEPRESSION IN UK BIOBANK

Author

Ian J. Deary, Mark Adams, Heather C. Whalley, Xueyi Shen, David M. Howard, and Andrew M. McIntosh

Subjects
Pharmacology, medicine.medical_specialty, business.industry, Phenome, Biobank, Psychiatry and Mental health, symbols.namesake, Neurology, Mendelian inheritance, symbols, medicine, Pharmacology (medical), Polygenic risk score, Neurology (clinical), Psychiatry, business, Biological Psychiatry, Depression (differential diagnoses)
Published
2019

31. Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Author

Lynsey S. Hall, Archie Campbell, David J. Porteous, Aleix Arnau Soler, Caroline Hayward, Lauren Navrady, Charley Xia, Ian J. Deary, Blair H. Smith, Carmen Amador, Yanni Zeng, Andrew M. McIntosh, Lynne J. Hocking, Toni-Kim Clarke, David M. Howard, Pippa A. Thomson, Pau Navarro, Chris Haley, Sandosh Padmanabhan, and Mark Adams

Subjects
0301 basic medicine, Medicine (miscellaneous), Single-nucleotide polymorphism, Stress, Genetic correlation, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, mental disorders, Genotype, Genetics, medicine, Sibling, Depression (differential diagnoses), 030304 developmental biology, Neuroticism, 0303 health sciences, Depression, business.industry, Life events, Articles, Heritability, medicine.disease, Regression, 3. Good health, 030104 developmental biology, Major depressive disorder, Psychology, business, 030217 neurology & neurosurgery, Research Article, Clinical psychology
Abstract

Background: Stressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable and genetic risk for SLEs is correlated with risk for MDD. We sought to investigate the genetic and environmental contributions to SLEs in a family-based sample, and quantify genetic overlap with MDD and neuroticism. Methods: A subset of Generation Scotland: the Scottish Family Health Study (GS), consisting of 9618 individuals with information on MDD, past 6 month SLEs, neuroticism and genome-wide genotype data was used in the present study. We estimated the heritability of SLEs using GCTA software. The environmental contribution to SLEs was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression (LDSC) we analysed the genetic overlap between MDD, neuroticism and SLEs. Results: Past 6-month life events were positively correlated with lifetime MDD status (β=0.21, r2=1.1%, p=2.5 x 10-25) and neuroticism (β =0.13, r2=1.9%, p=1.04 x 10-37) at the phenotypic level. Common SNPs explained 8% of the phenotypic variance in personal life events (those directly affecting the individual) (S.E.=0.03, p= 9 x 10-4). A significant effect of couple environment was detected accounting for 13% (S.E.=0.03, p=0.016) of the phenotypic variation in SLEs. PRS analyses found that reporting more SLEs was associated with a higher polygenic risk for MDD (β =0.05, r2=0.3%, p=3 x 10-5), but not a higher polygenic risk for neuroticism. LDSC showed a significant genetic correlation between SLEs and both MDD (rG=0.33, S.E.=0.08 ) and neuroticism (rG=0.15, S.E.=0.07). Conclusions: These findings suggest that SLEs should not be regarded solely as environmental risk factors for MDD as they are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work is needed to determine the causal direction and source of these associations.

Published
2019

32. SA66EPIGENOME-WIDE ASSOCIATION STUDY OF ANTIDEPRESSANT USE

Author

Toni Clarke, David M. Howard, Kristin K. Nicodemus, Riccardo E. Marioni, Mark Adams, Jonathan D. Hafferty, David J. Porteous, Andrew M. McIntosh, Stewart W. Morris, Mairead L. Bermingham, Archie Campbell, Kathryn L. Evans, and Rosie M. Walker

Subjects
Pharmacology, Psychiatry and Mental health, medicine.medical_specialty, Neurology, business.industry, Association (object-oriented programming), Medicine, Antidepressant, Pharmacology (medical), Neurology (clinical), business, Psychiatry, Biological Psychiatry
Published
2019

33. THE IDENTIFICATION OF HETEROGENEOUS GENETIC SUBGROUPS FOR MAJOR DEPRESSIVE DISORDER

Author

Jude Gibson, Ian J. Deary, Thibaud Boutin, Andrew M. McIntosh, Mark Adams, David J. Porteous, Yanni Zeng, Jonathan D. Hafferty, Eleanor M. Wigmore, David M. Howard, Pippa A. Thomson, Heather C. Whalley, Toni Clarke, and Chris Haley

Subjects
Pharmacology, business.industry, Heritability, medicine.disease, behavioral disciplines and activities, Biobank, Genetic architecture, 030227 psychiatry, 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, Neurology, mental disorders, Risk allele, Trait, Medicine, Major depressive disorder, Pharmacology (medical), Identification (biology), Neurology (clinical), business, 030217 neurology & neurosurgery, Biological Psychiatry, Depression (differential diagnoses), Clinical psychology
Abstract

Background Major Depressive Disorder (MDD) is a leading cause of disability worldwide with an estimated heritability of 37%. MDD is clinically heterogeneous and has frequently been shown to be comorbid with a variety of other conditions. Both of these factors have potentially confounded previous attempts to elucidate MDD's genetic architecture. To investigate causal heterogeneity within MDD we sought to identify genetic subgroups associated with other traits within MDD cases. Methods We examined two cohorts comprising of unrelated individuals, Generation Scotland: Scottish Family Health Study (GS:SFHS; n=6,946) and UK Biobank (n=25,035), for evidence of heterogeneous genetic sub-groups within MDD cases. Within GS:SFHS, a diagnosis of MDD was made by trained researchers using the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders. UK Biobank participants completed a touchscreen assessment of depressive symptoms and previous treatments from which a probable diagnosis of MDD was determined. We obtained risk alleles for a total of 34 traits and used the software package Buhmbox to determine whether there was a sharing of risk alleles associated with each trait and a subgroup within our MDD cases. Results There was evidence (P Discussion As far as we are aware, ours is the first study to identify the existence of genetic subgroup heterogeneity within MDD. In our study, the strongest evidence of genetic subgroups within MDD cases were for blood pressure, cholesterol and triglyceride levels. Our study has provided replicable evidence of subgroup heterogeneity within MDD for a number of traits underlining the potential of using genomic data to develop stratified approaches for the diagnosis and treatment of depression.

Published
2019

34. 3GENOME-WIDE META-ANALYSIS OF DEPRESSION

Author

Patrick F. Sullivan, Ian J. Deary, Cathryn M. Lewis, Toni-Kim Clarke, Jonathan D. Hafferty, David M. Howard, Naomi R. Wray, Andrew M. McIntosh, Gerome Breen, Mark Adams, Jude Gibson, Jonathan R. I. Coleman, and Daniel J. Smith

Subjects
Pharmacology, Psychiatry and Mental health, Neurology, business.industry, Meta-analysis, Medicine, Pharmacology (medical), Neurology (clinical), business, Biological Psychiatry, Depression (differential diagnoses), Clinical psychology
Published
2019

35. Spectrographic analysis of a cockpit voice recorder tape

Author

David M. Howard and Allen Hirson

Subjects
Linguistics and Language, Engineering, Accident investigation, business.industry, business, Telecommunications, Law, Cockpit
Abstract

Hirson and Howard present a case study and analysis of the cockpit voice recorder (CVR) from a South African Airways passenger aircraft off Mauritius on 28 November 1987.

Published
2013

36. Developing the Vocal Tract Organ

Author

David M. Howard

Subjects
Communication, Excitation signal, Music psychology, business.industry, Speech recognition, Music making, Generative music, Natural (music), Psychology, business, Music, Vocal tract, Human voice
Abstract

The human voice has essentially always played a part in music making; indeed, larger pipe organs have incorporated a Vox Humana stop for many years. Work on improving the naturalness of vocal synthesis using articulatory knowledge requires magnetic resonance imaging of natural vocal tract shapes and this has enabled 3-D printed models to be made. These can be stimulated with a quasi-larynx excitation signal and the possibility of a Vocal Tract Organ has emerged. This research report describes the current state of technical development of the Vocal Tract Organ and how it has been and could be used musically.

Published
2016

37. Do Regional Brain Volumes and Major Depressive Disorder Share Genetic Architecture?: a study of Generation Scotland (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766)

Author

Eleanor M. Wigmore, Toni-Kim Clarke, Mark J. Adams, Ana M. Fernandez-Pujals, Jude Gibson, David M. Howard, Gail Davies, Lynsey S. Hall, Yanni Zeng, Pippa A. Thomson, Caroline Hayward, Blair H. Smith, Lynne J. Hocking, Sandosh Padmanabhan, Ian J. Deary, David J. Porteous, Kristin K. Nicodemus, and Andrew M. McIntosh

Subjects
0303 health sciences, medicine.medical_specialty, Longitudinal study, business.industry, Genome-wide association study, Logistic regression, medicine.disease, Biobank, Genetic architecture, 03 medical and health sciences, 0302 clinical medicine, mental disorders, Brain size, Medicine, Major depressive disorder, business, Psychiatry, 030217 neurology & neurosurgery, Depression (differential diagnoses), 030304 developmental biology
Abstract

Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from ENIGMA consortium’s genome-wide association study (GWAS) of regional brain volume, we sought to test whether there is shared genetic architecture between 8 subcortical brain volumes and MDD. Using LD score regression utilising summary statistics from ENIGMA and the Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. We also generated polygenic risk scores (PRS) to assess potential pleiotropy between regional brain volumes and MDD in three cohorts (Generation Scotland; Scottish Family Health Study (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766). We used logistic regression to examine volumetric PRS and MDD and performed a meta-analysis across the three cohorts. No regional volumetric PRS demonstrated significant association with MDD or recurrent MDD. In this study we provide some evidence that hippocampal volume and MDD may share genetic architecture, albeit this did not survive multiple testing correction and was in the opposite direction to most reported phenotypic correlations. We therefore found no evidence to support a shared genetic architecture for MDD and regional subcortical volumes.

Published
2016

38. Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement Using the Principles of Evolution

Author

Crispin Cooper, David M. Howard, and Andy M. Tyrrell

Subjects
Engineering, genetic structures, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Speech recognition, Figure copying, Magnetic resonance imaging, Context (language use), behavioral disciplines and activities, Test (assessment), Task (project management), medicine, Representation (mathematics), business, psychological phenomena and processes, Vocal tract
Abstract

In this work, a revised form of Implicit Context Representation Cartesian Genetic Programming is used in the development of a diagnostic tool for the assessment of patients with neurological dysfunction such as Alzheimer's disease. Specifically, visuo-spatial ability is assessed by analysing subjects' digitised responses to a simple figure copying task using a conventional test environment. The algorithm was trained to distinguish between classes of visuo-spatial ability based on responses to the figure copying test by 7-11 year old children in which visuo-spatial ability is at varying stages of maturity. Results from receiver operating characteristic (ROC) analysis are presented for the training and subsequent testing of the algorithm and demonstrate this technique has the potential to form the basis of an objective assessment of visuo-spatial ability.

Published
2010

39. Content engineering: building system from the data up

Author

David M. Howard

Subjects
Information Systems and Management, business.industry, Computer science, Library and Information Sciences, Data structure, business, Data science, Structuring, Information Systems, Valuation (finance), Content management
Abstract

Now that content is king, we need to institutionalise its monarchy with a cross-discipline approach to developing the data structures of the future. Content engineering is about the application of a rigorous structured approach to the design, management and processing of content. Content engineering is the "process of creating information experiences that have specific value to specific audiences in specific contexts" and splits it into five key disciplines: discovery, structuring contextualisation, valuation and presentation. Its thesis is based around the notion that content engineering is about data transformation, combined with knowledge and previous experiences in order to provide future audiences with what they require.

Published
2007

40. Technology For Real-Time Visual Feedback In Singing Lessons

Author

David M. Howard

Subjects
Multimedia PC, Communication, Point (typography), business.industry, Computer science, 05 social sciences, Context (language use), 06 humanities and the arts, Speech science, 050105 experimental psychology, 060404 music, Education, Human–computer interaction, Audio analyzer, 0501 psychology and cognitive sciences, Singing, business, Engineering research, 0604 arts, Music, Computer technology
Abstract

Computer technology has advanced to the point where audio analysis techniques, previously the hallowed preserve of speech science and engineering research laboratories, are now available to anyone who makes use of a modern PC machine in the home or office. Much of this analysis can be carried out in real-time. Thus it is currently perfectly feasible to make available real-time visual feedback in singing lessons, and a number of systems are now available. There is a considerable body of (well-established) literature describing quantifiable changes that occur when the singing voice is trained in a context within which visual feedback can be offered. This paper discusses voice production and those elements that can be quantified using today's multimedia PC machines, the use of real-time visual feedback in singing lessons and the importance of some understanding of the principles behind typical analysis algorithms employed in such systems. It is shown that real-time displays are useful in singing lessons. Displays should be clear and unambiguous and users must be aware of what is expected of them. User-controlled parameters should be altered with caution with an informed expectation of what the output might look like and the impact of any errors that are likely to occur.

Published
2005

41. Voice source changes of child and adolescent subjects undergoing singing training--a preliminary study

Author

C. A. Barlow and David M. Howard

Subjects
Male, Larynx, medicine.medical_specialty, Adolescent, Voice Quality, Pilot Projects, Closed quotient, Audiology, Child and adolescent, Speech and Hearing, Arts and Humanities (miscellaneous), medicine, Humans, Choir, Child, Voice source, Communication, Verbal Behavior, business.industry, LPN and LVN, Electric Stimulation, Voice Training, medicine.anatomical_structure, Voice, Female, Voice change, Singing, Psychology, business
Abstract

Many vocal practitioners have strong beliefs regarding the age at which singing training of a child should begin, and the different ways in which male and female children should be treated. These beliefs are not substantiated by any scientific research, leading to considerable dispute between vocal coaches and choral directors. The singing voices of over 127 child singers and non-singers aged 8-18 were analysed using electrolaryngographic measures. Analysis particularly concentrated on the laryngographically derived vocal fold closed quotient (CQ). Results indicated that the voice source characteristics of subjects could be divided into groups according to age, gender and the level of vocal training received. Female subjects in particular exhibited a marked development of voice source production according to the length of training received, while male subjects exhibited patterning according to both age (and related pubertal development), and training received. It was concluded that the process of training a young voice has a quantifiable effect upon the singing voice production of the child, and in particular on the female voice, while pubertal development also creates measurable effects on the voice source production of the male child.

Published
2002

42. Interactive multimedia systems for engineering education in acoustics, synthesis and signal processing

Author

Ross Kirk, Andy M. Tyrrell, David M. Howard, Kingsley Ash, and Andrew J. Hunt

Subjects
Engineering, Signal processing, Multimedia, Higher education, business.industry, General Engineering, Music technology, Electrical engineering, computer.software_genre, Physical modelling, Education, Variety (cybernetics), Software, Engineering education, business, computer, Interactive media
Abstract

This paper describes programmable multimedia systems, developed at the University of York, which are used extensively for teaching on a variety of music technology and mainstream engineering courses. Software and hardware systems are described for the physical modelling of acoustic spaces, and for constructing interactive synthesis and signal processing networks. Details are given on how these have been successfully integrated into higher education programmes at York.

Published
2001

43. The harmonious chipsmith

Author

David M. Howard and Andy M. Tyrrell

Subjects
Harmony (color), Digital signal processor, Computer science, business.industry, Electronic music, Speech recognition, Electrical and Electronic Engineering, Singing, business, Digital signal processing
Abstract

The authors describe how modern digital signal processing (DSP) chips and parallel processing have helped realise a real-time, four-part singing synthesiser. They have developed a singing system based on the SHARC digital signal processor from Analog Devices. Using DSP chips operating in parallel, this system readily supports the synthesis of multipart singing, allowing close harmony and choral textures to be achieved with or without supporting musical accompaniment.

Published
1999

44. Real-time display of voice source characteristics

Author

Paul E. Garner and David M. Howard

Subjects
Computer science, business.industry, Speech recognition, Fundamental frequency, Integrated circuit, LPN and LVN, law.invention, Speech and Hearing, Arts and Humanities (miscellaneous), law, IBM PC compatible, Voice Training, Waveform, Singing, business, Voice source, Digital signal processing
Abstract

Previous research investigating electrolaryngographically derived voice source characteristics suggest that the vocal fold closed quotient (CQ) and fundamental frequency (F0) are useful parameters for assessing and developing voice production skills. This paper describes the design of a real-time visual feedback display system of CQ and F0 based on the electrolaryngograph output waveform, comprising a PC compatible computer with an Ariel PC-56D DSP co-processor board, which incorporates a Motorola 56001 DSP integrated circuit. Details of a pilot study are given, in which the system was used during a number of singing training sessions. Early indications show that this system provides useful visual feedback of progress to both the pupil and the instructor during voice training.

Published
1999

45. Transputer-based human hearing simulation

Author

Andy M. Tyrrell, David M. Howard, and Tim Brookes

Subjects
Engineering, business.industry, Speech recognition, media_common.quotation_subject, Fast Fourier transform, Speech synthesis, computer.software_genre, Speech processing, Naturalness, Real-time simulation, Modeling and Simulation, Perception, Spectrogram, Psychoacoustics, business, computer, media_common
Abstract

Simulations of the human hearing system can help in a number of research fields; including work with the speech and hearing impaired as well as improving the accuracy of speech recognition systems and the naturalness of speech synthesis technology. The results from psychoacoustic experiments carried out over the last few decades have enabled models of the human peripheral hearing system to be developed. Conventionally, analyses such as the Fast Fourier Transform are used to analyze speech and other sounds to establish the acoustic cues which are important for human perception. Such analyses can be shown to be inappropriate in a number of ways. Additional insights could be gained into the importance of various acoustic cues if analyses based on hearing models were used. This paper describes an implementation of a real-time spectrograph based on a contemporary model of the peripheral human hearing system, executing on a network of T9000 transputers. The differences between it and conventional spectrographs are illustrated by means of test signals and speech sounds.

Published
1998

46. Stereo output transputer interface board

Author

Andy M. Tyrrell, David M. Howard, and Tony P. W. Price

Subjects
Computer Networks and Communications, business.industry, Computer science, Transputer, Carry (arithmetic), Interface (computing), Field (computer science), Parallel processing (DSP implementation), Artificial Intelligence, Hardware and Architecture, Psychoacoustics, business, Software, Computer hardware
Abstract

There are many applications, particularly within the field of music synthesis and studio post-production, where a stereo output is required. Stereo is the industry standard format for the great majority of commercial recordings which are often subject to digital processing as well as being the format in which most composers and researchers in music technology carry out their work. It is also the audio format used for sound localisation processing and much psychoacoustic testing. This paper presents a design of a high performance stereo output system that can be interfaced to a parallel processing environment. The parallel processing environment and interfaces are based around transputers, the stereo processing is based around a Philips SAA7322/SAA7323 CMOS chip.

Published
1998

47. T9000 and T800 transputers: A real-time application

Author

Andy M. Tyrrell, Tim Brookes, and David M. Howard

Subjects
Packet routing, Hardware and Architecture, Computer science, business.industry, Transputer, Embedded system, Systems architecture, Image processing, business, Software
Abstract

The T9000 transputer and C104 packet routing switch are the latest variants from SGS Thompson. In this paper a real-time application is developed using these new devices and a comparison with a, T800 system is made. It is shown that the increased performance of the T9000 system allows improvement and enhancement of the application without the loss of real-time operation.

Published
1997

48. 'Ring' in the solo child singing voice

Author

Christian T. Herbst, Jenevora Williams, and David M. Howard

Subjects
Male, medicine.medical_specialty, business.product_category, Sound Spectrography, Adolescent, Voice Quality, Speech recognition, Singing, Context (language use), Audiology, Key (music), Speech and Hearing, Phenomenon, medicine, Pressure, Humans, Child, Headphones, Ring (mathematics), Piano, Age Factors, Acoustics, LPN and LVN, Formant, Otorhinolaryngology, Auditory Perception, Female, business, Psychology
Abstract

Summary Objectives/Hypothesis Listeners often describe the voices of solo child singers as being "pure" or "clear"; these terms would suggest that the voice is not only pleasant but also clearly audible. The audibility or clarity could be attributed to the presence of high-frequency partials in the sound: a "brightness" or "ring." This article aims to investigate spectrally the acoustic nature of this ring phenomenon in children's solo voices, and in particular, relating it to their "nonring" production. Additionally, this is set in the context of establishing to what extent, if any, the spectral characteristics of ring are shared with those of the singer's formant cluster associated with professional adult opera singers in the 2.5–3.5kHz region. Methods A group of child solo singers, acknowledged as outstanding by a singing teacher who specializes in teaching professional child singers, were recorded in a major UK concert hall performing Come unto him, all ye that labour , from the aria He shall feed his flock from The Messiah by GF Handel. Their singing was accompanied by a recording of a piano played through in-ear headphones. Sound pressure recordings were made from well within the critical distance in the hall. The singers were observed to produce notes with and without ring, and these recordings were analyzed in the frequency domain to investigate their spectra. Results The results indicate that there is evidence to suggest that ring in child solo singers is carried in two areas of the output spectrum: first in the singer's formant cluster region, centered around 4kHz, which is more than 1000Hz higher than what is observed in adults; and second in the region around 7.5–11kHz where a significant strengthening of harmonic presence is observed. A perceptual test has been carried out demonstrating that 94% of 62 listeners label a synthesized version of the calculated overall average ring spectrum for all subjects as having ring when compared with a synthesized version of the calculated overall average nonring spectrum. Conclusions The notion of ring in the child solo voice manifests itself not only with spectral features in common with the projection peak found in adult singers but also in a higher frequency region. It is suggested that the formant cluster at around 4kHz is the children's equivalent of the singers' formant cluster; the frequency is higher than in the adult, most likely due to the smaller dimensions of the epilaryngeal tube. The frequency cluster observed as a strong peak at about 7.5–11kHz, when added to the children's singers' formant cluster, may be the key to cueing the notion of ring in the child solo voice.

Published
2013

49. Evaluation of voice and quality of life after transoral endoscopic laser resection of early glottic carcinoma

Author

A R H Grace, Y. Bajaj, R. Sharma, David M. Howard, Andrew P. Coatesworth, A R Nicolaides, and S. Uppal

Subjects
Male, medicine.medical_specialty, Glottis, Voice Quality, Anterior commissure, Severity of Illness Index, Laser resection, Quality of life, Surveys and Questionnaires, Outcome Assessment, Health Care, Carcinoma, medicine, Humans, Phonation, Stroboscopy, Laryngeal Neoplasms, Aged, Neoplasm Staging, Aged, 80 and over, Voice Disorders, Laryngoscopy, business.industry, General Medicine, Middle Aged, medicine.disease, Surgery, Glottis Carcinoma, medicine.anatomical_structure, Cross-Sectional Studies, Otorhinolaryngology, Glottic cancer, Quality of Life, Laser Therapy, business
Abstract

Objective:This study aimed to evaluate voice and quality of life after transoral laser resection of early glottic carcinoma.Methods:We studied 19 patients undergoing transoral laser resection of tumour stage (T) one or T2 glottic carcinoma. Laryngeal function was evaluated by video-stroboscopy, vocal function by the Voice Symptom Scale, the grade-roughness-breathiness-asthenia-strain scale and objective phoniatric assessment, and quality of life by the University of Washington Quality of Life questionnaire.Results:Patients’ glottic carcinoma tumour-node-metastasis (TNM) staging was T1 N0 M0 in 14 patients and T2 N0 M0 in five. Overall voice grade, roughness and breathiness were mild to moderate in 84 per cent; asthenia and voice strain were more uniformly distributed, with 15 per cent of patients having normal voice quality. Eight patients developed a glottic web post-operatively; anterior commissure web was significantly associated with worse voice grade (p = 0.05). Seven patients (47 per cent) had a ‘mucosal wave’ on the operated vocal fold; this was significantly associated with less strain on phonation (p = 0.05). Voice Symptom Scale score was low overall (15 patients (78.9 per cent) scored less than 30). The fundamental frequency and frequency irregularity were normal in nine patients (47.3 per cent); the closed quotient was normal in six (31.5 per cent). The averaged quality of life score was ≥90 in 14 patients (73.7 per cent); 18 (94.7 per cent) felt their health-related quality of life was either the same or better post-operatively; and overall quality of life was positive in all.Conclusion:Transoral laser resection of T1 and T2 glottic carcinoma enables adequate tumour tissue excision with preservation of acceptable vocal function. Most patients’ post-operative quality of life is very good. Anterior commissure web formation is associated with poorer vocal function.

Published
2011

50. Visual displays for the assessment of vocal pitch matching development

Author

David M. Howard and Graham Welch

Subjects
Engineering, Matching (statistics), Acoustics and Ultrasonics, MIDI, business.industry, Speech recognition, Interface (computing), computer.file_format, Graphical display, Fundamental frequency, Vocal pitch, Task (project management), Singing, business, computer
Abstract

The instrumental assessment of singing development requires (1) musically relevant stimuli with known and readily controlled variations, (2) an interface device which gives a reliable estimate of the fundamental frequency of sung responses and (3) a clear and quantifiable display of vocal task stimuli and responses from which useful measurements can be readily taken. The first can be achieved with a music synthesizer controlled via the Musical Instrument Digital Interface (MIDI), the second with so-called pitch extraction systems available primarily for speech applications and the third by means of a mouse-driven graphical display. This paper describes the design and implementation of a vocal pitch matching development assessment system, and illustrates its operation with results gained from an experienced singer. The system described is a development from an earlier system, which, although much more limited in its scope, has nevertheless been shown to be useful in primary school music teaching. It is suggested that this new implementation will find greater application in many areas of singing teaching.

Published
1993

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