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2. Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study

Author

Wu, Honghan, Hodgson, Karen, Dyson, Sue, Morley, Katherine I., Ibrahim, Zina M., Iqbal, Ehtesham, Stewart, Robert, Dobson, Richard JB, and Sudlow, Cathie

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste", which collectively impede efficient model reuse. We propose a phenotype embedding based approach to minimize these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% (duplicate waste), i.e. phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% (imbalance waste), i.e. the effort required in "blind" model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

Published
2019
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3. Identifying the Common Genetic Basis of Antidepressant Response

Author

Kasper, Siegfried, Zohar, Joseph, Souery, Daniel, Montgomery, Stuart, Albani, Diego, Forloni, Gianluigi, Ferentinos, Panagiotis, Rujescu, Dan, Mendlewicz, Julien, Wray, Naomi R., Ripke, Stephan, 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, Deary, Ian J., 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., 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, 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., 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., 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, Nöthen, Markus M., O’Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Penninx, Brenda W.J.H., Perlis, Roy H., Porteous, David J., Potash, James B., Preisig, Martin, Rietschel, Marcella, 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., Pain, Oliver, Hodgson, Karen, Marshe, Victoria S., Campos, Adrian I., Carrillo-Roa, Tania, Cattaneo, Annamaria, Als, Thomas D., Dernovsek, Mojca Z., Fabbri, Chiara, Henigsberg, Neven, Hauser, Joanna, Kennedy, James L., Lenze, Eric J., Müller, Daniel J., Mulsant, Benoit H., Perroud, Nader, Rentería, Miguel E., Reynolds, Charles F., III, Wigmore, Eleanor M., Aitchison, Katherine J., Biernacka, Joanna M., Bondolfi, Guido, Kato, Masaki, Liu, Yu-Li, Serretti, Alessandro, Tsai, Shih-Jen, Turecki, Gustavo, and Weinshilboum, Richard

Published
2022
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4. Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity

Author

Yao, Nailin, Winkler, Anderson M, Barrett, Jennifer, Book, Gregory A, Beetham, Tamara, Horseman, Rachel, Leach, Olivia, Hodgson, Karen, Knowles, Emma E, Mathias, Samuel, Stevens, Michael C, Assaf, Michal, van Erp, Theo GM, Pearlson, Godfrey D, and Glahn, David C

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Schizophrenia, Mental Health, Neurosciences, Serious Mental Illness, Biomedical Imaging, 2.3 Psychological, social and economic factors, 4.2 Evaluation of markers and technologies, Aetiology, Detection, screening and diagnosis, Mental health, Adult, Bipolar Disorder, Brain, Cohort Studies, Diagnosis, Differential, Female, Head, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Biological, Motion, Organ Size, Psychiatric Status Rating Scales, Neuroanatomy, Neuroimaging, Structural MRI, bipolar disorder, head motion, neuroanatomic specificity, neuroanatomy, neuroimaging, schizophrenia, structural MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc.

Published
2017

5. Biomarkers of antidepressant treatment outcomes

Author

Hodgson, Karen, McGuffin, Peter, and Dobson, Richard James Butler

Subjects
616.85, Pharmacogenetics, Antidepressants, Transcriptomics, Depression
Abstract

Whilst antidepressants are widely prescribed, there is a large degree of variation between patients in terms of treatment outcomes. Furthermore, the mechanisms by which these drugs exert their effects remain unclear. In this thesis, genetic biomarkers of antidepressant outcomes have been explored, in order to better understand the molecular mechanisms underpinning effective antidepressant treatment. The research presented here use data from the GENDEP project, which is a large pharmacogenetic study of depressed patients receiving antidepressant treatment. Firstly, the pharmacological underpinnings of antidepressant-associated side effects were used to categorise these side effects and conduct a candidate gene analysis. Whilst a significant association between variation within the HTR2C gene and serotonergic side effects was found, the observation was not replicated in a second sample. Secondly, the role of variability in drug metabolism rates in treatment outcomes was investigated. Examining genotypic information on the cytochrome P450 enzymes, no associations with treatment response, side effects or study discontinuation were observed. Furthermore, serum concentrations of antidepressant were unrelated to treatment response or overall burden of side effects, predicting only a minority of specific side effects. Thirdly, transcriptomic changes with drug administration were explored in relation to treatment efficacy. Two genes were identified where changes in expression levels were significantly associated with treatment response amongst patients taking nortriptyline. Furthermore, using a network-based approach, changes in gene expression across one module of coexpressed genes showed significant correlation with symptom improvement; this biological network generalised across different antidepressant medications. Finally, genomic and transcriptomic data were combined, in an examination of the genetic control of gene expression. This analysis then was used to gain an insight into the molecular processes that link genotype to phenotype. The evidence presented within this thesis, when considered in combination with existing literature, highlights that antidepressant efficacy is a complex trait, influenced by many genes of small effect. Nevertheless, by layering together different levels of information, we can begin to dissect the molecular mechanisms involved in antidepressant action.

Published
2015

6. Describing unpaid carers’ health service use in local areas across Wales: A retrospective cohort study using linked routinely collected data

Author

Bentley, Laura, primary, Peh, Jerlyn, additional, Beckett-Hill, Georgia, additional, Hodgson, Karen, additional, Akbari, Ashley, additional, Newman, Claire, additional, Davies, Owen, additional, Chehtane, Walid, additional, Trigg, Lisa, additional, Dundon, Joanna, additional, John, Gareth, additional, and Davies, Alisha, additional

Published
2023
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8. Transcriptomics and the mechanisms of antidepressant efficacy

Author

Hodgson, Karen, Tansey, Katherine E., Powell, Timothy R., Coppola, Giovanni, Uher, Rudolf, Zvezdana Dernovšek, Mojca, Mors, Ole, Hauser, Joanna, Souery, Daniel, Maier, Wolfgang, Henigsberg, Neven, Rietschel, Marcella, Placentino, Anna, Aitchison, Katherine J., Craig, Ian W., Farmer, Anne E., Breen, Gerome, McGuffin, Peter, and Dobson, Richard

Published
2016
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14. Inequalities in colorectal cancer screening uptake in Wales: an examination of the impact of the temporary suspension of the screening programme during the COVID-19 pandemic

Author

Bright, Diana, primary, Hillier, Sharon, additional, Song, Jiao, additional, Huws, Dyfed W., additional, Greene, Giles, additional, Hodgson, Karen, additional, Akbari, Ashley, additional, Griffiths, Rowena, additional, Davies, Alisha R., additional, and Gjini, Ardiana, additional

Published
2022
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15. Identifying the Common Genetic Basis of Antidepressant Response

Author

Pain, Oliver, Hodgson, Karen, Trubetskoy, Vassily, Ripke, Stephan, Marshe, Victoria S., Adams, Mark J., Byrne, Enda M., Campos, Adrian I., Carrillo-Roa, Tania, Cattaneo, Annamaria, Als, Thomas D., Souery, Daniel, Dernovsek, Mojca Z., Fabbri, Chiara, Hayward, Caroline, Henigsberg, Neven, Hauser, Joanna, Kennedy, James L., Lenze, Eric J., Lewis, Glyn, Müller, Daniel J., Martin, Nicholas G., Mulsant, Benoit H., Mors, Ole, Perroud, Nader, Porteous, David J., Rentería, Miguel E., Reynolds, Charles F., Rietschel, Marcella, Uher, Rudolf, Wigmore, Eleanor M., Maier, Wolfgang, Wray, Naomi R., Aitchison, Katherine J., Arolt, Volker, Baune, Bernhard T., Biernacka, Joanna M., Bondolfi, Guido, Liu, Yu Li, McIntosh, Andrew, Lewis, Cathryn M., Pain, Oliver, Hodgson, Karen, Trubetskoy, Vassily, Ripke, Stephan, Marshe, Victoria S., Adams, Mark J., Byrne, Enda M., Campos, Adrian I., Carrillo-Roa, Tania, Cattaneo, Annamaria, Als, Thomas D., Souery, Daniel, Dernovsek, Mojca Z., Fabbri, Chiara, Hayward, Caroline, Henigsberg, Neven, Hauser, Joanna, Kennedy, James L., Lenze, Eric J., Lewis, Glyn, Müller, Daniel J., Martin, Nicholas G., Mulsant, Benoit H., Mors, Ole, Perroud, Nader, Porteous, David J., Rentería, Miguel E., Reynolds, Charles F., Rietschel, Marcella, Uher, Rudolf, Wigmore, Eleanor M., Maier, Wolfgang, Wray, Naomi R., Aitchison, Katherine J., Arolt, Volker, Baune, Bernhard T., Biernacka, Joanna M., Bondolfi, Guido, Liu, Yu Li, McIntosh, Andrew, and Lewis, Cathryn M.

Abstract

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has

Published
2022

19. Identifying the Common Genetic Basis of Antidepressant Response

Author

Pain, Oliver, primary, Hodgson, Karen, additional, Trubetskoy, Vassily, additional, Ripke, Stephan, additional, Marshe, Victoria S., additional, Adams, Mark J., additional, Byrne, Enda M., additional, Campos, Adrian I., additional, Carrillo-Roa, Tania, additional, Cattaneo, Annamaria, additional, Als, Thomas D., additional, Souery, Daniel, additional, Dernovsek, Mojca Z., additional, Fabbri, Chiara, additional, Hayward, Caroline, additional, Henigsberg, Neven, additional, Hauser, Joanna, additional, Kennedy, James L., additional, Lenze, Eric J., additional, Lewis, Glyn, additional, Müller, Daniel J., additional, Martin, Nicholas G., additional, Mulsant, Benoit H., additional, Mors, Ole, additional, Perroud, Nader, additional, Porteous, David J., additional, Rentería, Miguel E., additional, Reynolds, Charles F., additional, Rietschel, Marcella, additional, Uher, Rudolf, additional, Wigmore, Eleanor M., additional, Maier, Wolfgang, additional, Wray, Naomi R., additional, Aitchison, Katherine J., additional, Arolt, Volker, additional, Baune, Bernhard T., additional, Biernacka, Joanna M., additional, Bondolfi, Guido, additional, Domschke, Katharina, additional, Kato, Masaki, additional, Li, Qingqin S., additional, Liu, Yu-Li, additional, Serretti, Alessandro, additional, Tsai, Shih-Jen, additional, Turecki, Gustavo, additional, Weinshilboum, Richard, additional, McIntosh, Andrew M., additional, Lewis, Cathryn M., additional, Kasper, Siegfried, additional, Zohar, Joseph, additional, Montgomery, Stuart, additional, Albani, Diego, additional, Forloni, Gianluigi, additional, Ferentinos, Panagiotis, additional, Rujescu, Dan, additional, Mendlewicz, Julien, additional, Mattheisen, Manuel, additional, Trzaskowski, Maciej, additional, Abdellaoui, Abdel, additional, Agerbo, Esben, additional, Air, Tracy M., additional, Andlauer, Till F.M., additional, Bacanu, Silviu-Alin, additional, Bækvad-Hansen, Marie, additional, Beekman, Aartjan T.F., additional, Bigdeli, Tim B., additional, Binder, Elisabeth B., additional, Bryois, Julien, additional, Buttenschøn, Henriette N., additional, Bybjerg-Grauholm, Jonas, additional, Cai, Na, additional, Castelao, Enrique, additional, Christensen, Jane Hvarregaard, additional, Clarke, Toni-Kim, additional, Coleman, Jonathan R.I., additional, Colodro-Conde, Lucía, additional, Couvy-Duchesne, Baptiste, additional, Craddock, Nick, additional, Crawford, Gregory E., additional, Davies, Gail, additional, Deary, Ian J., additional, Degenhardt, Franziska, additional, Derks, Eske M., additional, Direk, Nese, additional, Dolan, Conor V., additional, Dunn, Erin C., additional, Eley, Thalia C., additional, Escott-Price, Valentina, additional, Hassan Kiadeh, Farnush Farhadi, additional, Finucane, Hilary K., additional, Foo, Jerome C., additional, Forstner, Andreas J., additional, Frank, Josef, additional, Gaspar, Héléna A., additional, Gill, Michael, additional, Goes, Fernando S., additional, Gordon, Scott D., additional, Grove, Jakob, additional, Hall, Lynsey S., additional, Hansen, Christine Søholm, additional, Hansen, Thomas F., additional, Herms, Stefan, additional, Hickie, Ian B., additional, Hoffmann, Per, additional, Homuth, Georg, additional, Horn, Carsten, additional, Hottenga, Jouke-Jan, additional, Hougaard, David M., additional, Howard, David M., additional, Ising, Marcus, additional, Jansen, Rick, additional, Jones, Ian, additional, Jones, Lisa A., additional, Jorgenson, Eric, additional, Knowles, James A., additional, Kohane, Isaac S., additional, Kraft, Julia, additional, Kretzschmar, Warren W., additional, Kutalik, Zoltán, additional, Li, Yihan, additional, Lind, Penelope A., additional, MacIntyre, Donald J., additional, MacKinnon, Dean F., additional, Maier, Robert M., additional, Marchini, Jonathan, additional, Mbarek, Hamdi, additional, McGrath, Patrick, additional, McGuffin, Peter, additional, Medland, Sarah E., additional, Mehta, Divya, additional, Middeldorp, Christel M., additional, Mihailov, Evelin, additional, Milaneschi, Yuri, additional, Milani, Lili, additional, Mondimore, Francis M., additional, Montgomery, Grant W., additional, Mostafavi, Sara, additional, Mullins, Niamh, additional, Nauck, Matthias, additional, Ng, Bernard, additional, Nivard, Michel G., additional, Nyholt, Dale R., additional, O’Reilly, Paul F., additional, Oskarsson, Hogni, additional, Owen, Michael J., additional, Painter, Jodie N., additional, Pedersen, Carsten Bøcker, additional, Pedersen, Marianne Giørtz, additional, Peterson, Roseann E., additional, Peyrot, Wouter J., additional, Pistis, Giorgio, additional, Posthuma, Danielle, additional, Quiroz, Jorge A., additional, Qvist, Per, additional, Rice, John P., additional, Riley, Brien P., additional, Rivera, Margarita, additional, Mirza, Saira Saeed, additional, Schoevers, Robert, additional, Schulte, Eva C., additional, Shen, Ling, additional, Shi, Jianxin, additional, Shyn, Stanley I., additional, Sigurdsson, Engilbert, additional, Sinnamon, Grant C.B., additional, Smit, Johannes H., additional, Smith, Daniel J., additional, Stefansson, Hreinn, additional, Steinberg, Stacy, additional, Streit, Fabian, additional, Strohmaier, Jana, additional, Tansey, Katherine E., additional, Teismann, Henning, additional, Teumer, Alexander, additional, Thompson, Wesley, additional, Thomson, Pippa A., additional, Thorgeirsson, Thorgeir E., additional, Traylor, Matthew, additional, Treutlein, Jens, additional, Uitterlinden, André G., additional, Umbricht, Daniel, additional, Van der Auwera, Sandra, additional, van Hemert, Albert M., additional, Viktorin, Alexander, additional, Visscher, Peter M., additional, Wang, Yunpeng, additional, Webb, Bradley T., additional, Weinsheimer, Shantel Marie, additional, Wellmann, Jürgen, additional, Willemsen, Gonneke, additional, Witt, Stephanie H., additional, Wu, Yang, additional, Xi, Hualin S., additional, Yang, Jian, additional, Zhang, Futao, additional, Berger, Klaus, additional, Boomsma, Dorret I., additional, Cichon, Sven, additional, Dannlowski, Udo, additional, de Geus, E.J.C., additional, DePaulo, J. Raymond, additional, Domenici, Enrico, additional, Esko, Tõnu, additional, Grabe, Hans J., additional, Hamilton, Steven P., additional, Heath, Andrew C., additional, Kendler, Kenneth S., additional, Kloiber, Stefan, additional, Lucae, Susanne, additional, Madden, Pamela A.F., additional, Magnusson, Patrik K., additional, Metspalu, Andres, additional, Mortensen, Preben Bo, additional, Müller-Myhsok, Bertram, additional, Nordentoft, Merete, additional, Nöthen, Markus M., additional, O’Donovan, Michael C., additional, Paciga, Sara A., additional, Pedersen, Nancy L., additional, Penninx, Brenda W.J.H., additional, Perlis, Roy H., additional, Potash, James B., additional, Preisig, Martin, additional, Schaefer, Catherine, additional, Schulze, Thomas G., additional, Smoller, Jordan W., additional, Stefansson, Kari, additional, Tiemeier, Henning, additional, Völzke, Henry, additional, Weissman, Myrna M., additional, Werge, Thomas, additional, Levinson, Douglas F., additional, Breen, Gerome, additional, Børglum, Anders D., additional, and Sullivan, Patrick F., additional

Published
2022
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20. List of Contributors

Author

Addy, Nii A., primary, Almasy, Laura, additional, Almli, Lynn M., additional, AuYong, Nicholas, additional, Bai, Hanwen, additional, Barrera, Daniel J., additional, Bearden, Carrie E., additional, Berry, James D., additional, Bettcher, Brianne M., additional, Bevan, Carolyn, additional, Blangero, John, additional, Bordelon, Yvette M., additional, Brown, Jesse A., additional, Bulik, Cynthia M., additional, Clark, Victoria E., additional, Corvin, Aiden, additional, Curran, Joanne E., additional, Cuthbert, Bruce N., additional, D'Gama, Alissa M., additional, Daly, Mark J., additional, Dhindsa, Ryan S., additional, Dölen, Gül, additional, Evans, Andrew, additional, Freimer, Nelson B., additional, Geschwind, Daniel H., additional, Geschwind, Michael D., additional, Glahn, David C., additional, Goldstein, David B., additional, Greally, John M., additional, Greicius, Michael, additional, Grinberg, Lea T., additional, Günel, Jennifer M., additional, Günel, Murat, additional, Gur, Raquel E., additional, Gur, Ruben C., additional, Haase, Claudia M., additional, Heinsen, Helmut, additional, Hibar, Derrek P., additional, Hodgson, Karen, additional, Hooli, Basavaraj, additional, Hrvoj-Mihic, Branka, additional, Jacob, Louis, additional, Jahanshad, Neda, additional, Jamuar, Saumya S., additional, Keener, Adrienne M., additional, Knowles, Emma E.M., additional, Lammel, Stephan, additional, Lee, Suzee E., additional, Levenson, Robert W., additional, Li, Mingfeng, additional, Lins, Mauro F.C., additional, Lowenstein, Daniel H., additional, Mahlios, Joshua, additional, Malenka, Robert C., additional, Mason, Christopher E., additional, Mathias, Samuel R., additional, Mendez, Mario F., additional, Merikangas, Kathleen R., additional, Merikangas, Alison K., additional, Mignot, Emmanuel, additional, Mocellin, Ramon, additional, Muhle, Rebecca A., additional, Muotri, Alysson R., additional, Naasan, Georges, additional, Nagarajan, Srikantan S., additional, Neale, Benjamin M., additional, Negraes, Priscilla D., additional, Nestler, Eric J., additional, Ollila, Hanna M., additional, Olvera, Rene L., additional, Paholpak, Pongsatorn, additional, Parikshak, Neelroop N., additional, Perlis, Roy H., additional, Pochareddy, Sirisha, additional, Portera-Cailliau, Carlos, additional, Pouratian, Nader, additional, Purcell, Shaun M., additional, Rabinovici, Gil D., additional, Ranasinghe, Kamalini G., additional, Ratti, Elena, additional, Reed, Hannah E., additional, Ressler, Kerry J., additional, Robinson, Elise B., additional, Sanders, Stephan J., additional, Schadt, Eric E., additional, Schonhaut, Daniel R., additional, Sebat, Jonathan, additional, Seeley, William W., additional, Semendeferi, Katerina, additional, Sestan, Nenad, additional, Shapiro, Kevin A., additional, Sherr, Elliot H., additional, Silbereis, John C., additional, Solecki, Wojciech B., additional, Spinelli, Edoardo G., additional, State, Matthew W., additional, Stein, Jason L., additional, Stuber, Garret D., additional, Sturm, Virginia E., additional, Sullivan, Patrick F., additional, Tanzi, Rudolph E., additional, Tempini, Maria L.G., additional, Theofilas, Panos, additional, Thompson, Paul M., additional, Trujillo, Cleber A., additional, Velakoulis, Dennis, additional, Vossel, Keith A., additional, Walsh, Christopher A., additional, Walterfang, Mark, additional, Wingo, Aliza P., additional, Yao, Nailin, additional, Yokoyama, Jennifer S., additional, Youngblood, Mark W., additional, Zandi, Peter, additional, and Zhu, Ying, additional

Published
2016
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21. Conceptualizing Major Depression

Author

Glahn, David C., primary, Knowles, Emma E.M., additional, Mathias, Samuel R., additional, Almasy, Laura, additional, Hodgson, Karen, additional, Yao, Nailin, additional, Olvera, Rene L., additional, Curran, Joanne E., additional, and Blangero, John, additional

Published
2016
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28. Purpose, not product: why 'entry control' is such an important term for integrators to know--and why its definition is so hard to nail down

Author

Hodgson, Karen

Subjects
Security systems industry -- Forecasts and trends -- Access control, Market trend/market analysis, Business, Electronics and electrical industries, High technology industry
Abstract

If you ask 12 different security people what 'entry control' means, you will likely get 12 different answers. Perhaps the term is not that widely used in the industry; but [...]

Published
2013

29. An enterprise state of mind: making a profit in the enterprise access space requires a shift in focus, commitment and culture

Author

Hodgson, Karen

Subjects
Decision-making, Security systems industry, Industry -- Safety and security measures, Business, Electronics and electrical industries, High technology industry
Abstract

Remember when an end user's security director was the go-to contact for an access control sale and the information technology (IT) manager was brought in to consult on the network [...]

Published
2012

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

Author

Hagenaars, Saskia P., primary, Coleman, Jonathan R. I., additional, Choi, Shing Wan, additional, Gaspar, Héléna, additional, Adams, Mark J., additional, Howard, David M., additional, Hodgson, Karen, additional, Traylor, Matthew, additional, Air, Tracy M., additional, Andlauer, Till F. M., additional, Arolt, Volker, additional, Baune, Bernhard T., additional, Binder, Elisabeth B., additional, Blackwood, Douglas H. R., additional, Boomsma, Dorret I., additional, Campbell, Archie, additional, Cearns, Micah, additional, Czamara, Darina, additional, Dannlowski, Udo, additional, Domschke, Katharina, additional, Geus, Eco J. C., additional, Hamilton, Steven P., additional, Hayward, Caroline, additional, Hickie, Ian B., additional, Hottenga, Jouke Jan, additional, Ising, Marcus, additional, Jones, Ian, additional, Jones, Lisa, additional, Kutalik, Zoltan, additional, Lucae, Susanne, additional, Martin, Nicholas G., additional, Milaneschi, Yuri, additional, Mueller‐Myhsok, Bertram, additional, Owen, Michael J., additional, Padmanabhan, Sandosh, additional, Penninx, Brenda W. J. H., additional, Pistis, Giorgio, additional, Porteous, David J., additional, Preisig, Martin, additional, Ripke, Stephan, additional, Shyn, Stanley I., additional, Sullivan, Patrick F., additional, Whitfield, John B., additional, Wray, Naomi R., additional, McIntosh, Andrew M., additional, Deary, Ian J., additional, Breen, Gerome, additional, and Lewis, Cathryn M., additional

Published
2020
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33. The genome‐wide expression effects of escitalopram and its relationship to neurogenesis, hippocampal volume, and antidepressant response

Author

Powell, Timothy R., Murphy, Tytus, de Jong, Simone, Lee, Sang Hyuck, Tansey, Katherine E., Hodgson, Karen, Uher, Rudolf, Price, Jack, Thuret, Sandrine, and Breen, Gerome

Subjects
Neurons, Depressive Disorder, Major, genetic pathway analysis, Genome, Human, Neurogenesis, hippocampal volume, Citalopram, hippocampal neurogenesis, Hippocampus, transcriptomics, Gene Expression Regulation, antidepressants, Antidepressive Agents, Second-Generation, Humans, antidepressant response, Gene Regulatory Networks, Research Articles, Cells, Cultured, Research Article, Genome-Wide Association Study
Abstract

Antidepressant-induced hippocampal neurogenesis (AHN) is hypothesized to contribute to increases in hippocampal volume among major depressive disorder patients after long-term treatment. Furthermore, rodent studies suggest AHN may be the cellular mechanism mediating the therapeutic benefits of antidepressants. Here, we perform the first investigation of genome-wide expression changes associated with AHN in human cells. We identify gene expression networks significantly activated during AHN, and we perform gene set analyses to probe the molecular relationship between AHN, hippocampal volume, and antidepressant response. The latter were achieved using genome-wide association summary data collected from 30,717 individuals as part of the ENIGMA Consortium (genetic predictors of hippocampal volume dataset), and data collected from 1,222 major depressed patients as part of the NEWMEDS Project (genetic predictors of response to antidepressants dataset). Our results showed that the selective serotonin reuptake inhibitor, escitalopram evoked AHN in human cells; dose-dependently increasing the differentiation of cells into neuroblasts, as well as increasing gliogenesis. Activated genome-wide expression networks relate to axon and microtubule formation, and ribosomal biogenesis. Gene set analysis revealed that gene expression changes associated with AHN were nominally enriched for genes predictive of hippocampal volume, but not for genes predictive of therapeutic response.

Published
2017
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34. 57 INVESTIGATING THE COMMON GENETIC BASIS OF ANTIDEPRESSANT RESPONSE

Author

Pain, Oliver, primary, Hodgson, Karen, additional, Trubetskoy, Vassily, additional, Baune, Bernhard, additional, Biernacka, Joanna, additional, Fabbri, Chiara, additional, Lewis, Glyn, additional, Li, Qingqin, additional, Müller-Myhsok, Bertram, additional, Perroud, Nader, additional, Serretti, Alessandro, additional, McIntosh, Andrew, additional, and Lewis, Cathryn, additional

Published
2019
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41. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Author

Iniesta, Raquel, Dernovsek, Mojca Zvezdana, Souery, Daniel, Dobson, Richard J B, Aitchison, Katherine, Farmer, Anne, McGuffin, Peter, Lewis, Cathryn C.M., Uher, Rudolf, Hodgson, Karen, Stahl, Daniel, Malki, Karim, Maier, Wolfgang, Rietschel, Marcella, Mors, Ole, Hauser, Joanna Wiktoria, Henigsberg, Neven, Iniesta, Raquel, Dernovsek, Mojca Zvezdana, Souery, Daniel, Dobson, Richard J B, Aitchison, Katherine, Farmer, Anne, McGuffin, Peter, Lewis, Cathryn C.M., Uher, Rudolf, Hodgson, Karen, Stahl, Daniel, Malki, Karim, Maier, Wolfgang, Rietschel, Marcella, Mors, Ole, Hauser, Joanna Wiktoria, and Henigsberg, Neven

Abstract

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2018

42. The new kid on the block: managed access: in the 'new' economy, managed access control is getting more attention as a revenue source for dealers and a savings for end users

Author

Hodgson, Karen

Subjects
Business enterprises -- Safety and security measures -- Management -- Customer relations -- Economic aspects, Company business management, Business, Electronics and electrical industries, High technology industry
Abstract

For integrators and dealers alike, offering some form of managed access control can open up a whole new world of opportunities--and revenue sources. Whether you have your own central station [...]

Published
2009

43. Cannabis use, depression and self‐harm: phenotypic and genetic relationships.

Author

Hodgson, Karen, Coleman, Jonathan R. I., Hagenaars, Saskia P., Purves, Kirstin L., Glanville, Kylie, Choi, Shing Wan, O'Reilly, Paul, Breen, Gerome, and Lewis, Cathryn M.

Subjects
*CANNABIS (Genus), *MENTAL depression, *SELF-mutilation, *PHENOTYPES, *GENETICS, *MOLECULAR epidemiology, *REGRESSION analysis, *TISSUE banks
Abstract

Background and Aims: The use of cannabis has previously been linked to both depression and self‐harm; however, the role of genetics in this relationship is unclear. This study aimed to estimate the phenotypic and genetic associations between cannabis use and depression and self‐harm. Design Cross‐sectional data collected through UK Biobank were used to test the phenotypic association between cannabis use, depression and self‐harm. UK Biobank genetic data were then combined with consortia genome‐wide association study summary statistics to further test the genetic relationships between these traits using LD score regression, polygenic risk scoring and Mendelian randomization methods. Setting: United Kingdom, with additional international consortia data. Participants: A total of 126 291 British adults aged between 40 and 70 years, recruited into UK Biobank. Measurements Phenotypic outcomes were life‐time history of cannabis use (including initial and continued cannabis use), depression (including single‐episode and recurrent depression) and self‐harm. Genome‐wide genetic data were used and assessment centre, batch and the first six principal components were included as key covariates when handling genetic data. Findings In UK Biobank, cannabis use is associated with an increased likelihood of depression [odds ratio (OR) = 1.64, 95% confidence interval (CI) = 1.59–1.70] and self‐harm (OR = 2.85, 95% CI = 2.69–3.01). The strength of this phenotypic association is stronger when more severe trait definitions of cannabis use and depression are considered. Using consortia genome‐wide summary statistics, significant genetic correlations are seen between cannabis use and depression [rg = 0.289, standard error (SE) = 0.036]. Polygenic risk scores for cannabis use and depression explain a small but significant proportion of variance in cannabis use, depression and self‐harm within a UK Biobank target sample. However, two‐sample Mendelian randomization analyses were not significant. Conclusions: Cannabis use appeared to be both phenotypically and genetically associated with depression and self‐harm. Limitations in statistical power mean that conclusions could not be made on the direction of causality between these traits. [ABSTRACT FROM AUTHOR]

Published
2020
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45. Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts

Author

Ward, Joey, primary, Graham, Nicholas, additional, Strawbridge, Rona J., additional, Ferguson, Amy, additional, Jenkins, Gregory, additional, Chen, Wenan, additional, Hodgson, Karen, additional, Frye, Mark, additional, Weinshilboum, Richard, additional, Uher, Rudolf, additional, Lewis, Cathryn M., additional, Biernacka, Joanna, additional, and Smith, Daniel J., additional

Published
2018
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47. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Author

Iniesta, Raquel, primary, Hodgson, Karen, additional, Stahl, Daniel, additional, Malki, Karim, additional, Maier, Wolfgang, additional, Rietschel, Marcella, additional, Mors, Ole, additional, Hauser, Joanna, additional, Henigsberg, Neven, additional, Dernovsek, Mojca Zvezdana, additional, Souery, Daniel, additional, Dobson, Richard, additional, Aitchison, Katherine J., additional, Farmer, Anne, additional, McGuffin, Peter, additional, Lewis, Cathryn M., additional, and Uher, Rudolf, additional

Published
2018
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49. 959. Inferring Pathobiology from Structural MRI in Schizophrenia and Bipolar Disorder: Modeling Head Motion and Neuroanatomical Specificity

Author

Glahn, David, primary, Yao, Nailin, additional, Winkler, Anderson, additional, Barrett, Jennifer, additional, Book, Gregory, additional, Beetham, Tamara, additional, Horseman, Rachel, additional, Leach, Olivia, additional, Hodgson, Karen, additional, Knowles, Emma, additional, Mathias, Samuel, additional, Stevens, Michael, additional, Assaf, Michal, additional, van Erp, Theo, additional, and Pearlson, Godfrey, additional

Published
2017
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50. Epigenetic Age Acceleration Assessed with Human White-Matter Images

Author

Hodgson, Karen, primary, Carless, Melanie A., additional, Kulkarni, Hemant, additional, Curran, Joanne E., additional, Sprooten, Emma, additional, Knowles, Emma E., additional, Mathias, Samuel, additional, Göring, Harald H.H., additional, Yao, Nailin, additional, Olvera, Rene L., additional, Fox, Peter T., additional, Almasy, Laura, additional, Duggirala, Ravi, additional, Blangero, John, additional, and Glahn, David C., additional

Published
2017
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