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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, JeanMichel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, HsiChung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil TekolaAyele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 221 / Issue 2 / August 2022
- Published online by Cambridge University Press:
- 04 May 2022, p. 494
- Print publication:
- August 2022
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean-Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola-Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 28 February 2022, pp. 219-228
- Print publication:
- April 2022
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Background
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
AimsTo use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
MethodThis study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
ResultsThe best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
ConclusionsUsing PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Characterisation of age and polarity at onset in bipolar disorder
- Janos L. Kalman, Loes M. Olde Loohuis, Annabel Vreeker, Andrew McQuillin, Eli A. Stahl, Douglas Ruderfer, Maria Grigoroiu-Serbanescu, Georgia Panagiotaropoulou, Stephan Ripke, Tim B. Bigdeli, Frederike Stein, Tina Meller, Susanne Meinert, Helena Pelin, Fabian Streit, Sergi Papiol, Mark J. Adams, Rolf Adolfsson, Kristina Adorjan, Ingrid Agartz, Sofie R. Aminoff, Heike Anderson-Schmidt, Ole A. Andreassen, Raffaella Ardau, Jean-Michel Aubry, Ceylan Balaban, Nicholas Bass, Bernhard T. Baune, Frank Bellivier, Antoni Benabarre, Susanne Bengesser, Wade H Berrettini, Marco P. Boks, Evelyn J. Bromet, Katharina Brosch, Monika Budde, William Byerley, Pablo Cervantes, Catina Chillotti, Sven Cichon, Scott R. Clark, Ashley L. Comes, Aiden Corvin, William Coryell, Nick Craddock, David W. Craig, Paul E. Croarkin, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Udo Dannlowski, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Srdjan Djurovic, Howard J. Edenberg, Mariam Al Eissa, Torbjørn Elvsåshagen, Bruno Etain, Ayman H. Fanous, Frederike Fellendorf, Alessia Fiorentino, Andreas J. Forstner, Mark A. Frye, Janice M. Fullerton, Katrin Gade, Julie Garnham, Elliot Gershon, Michael Gill, Fernando S. Goes, Katherine Gordon-Smith, Paul Grof, Jose Guzman-Parra, Tim Hahn, Roland Hasler, Maria Heilbronner, Urs Heilbronner, Stephane Jamain, Esther Jimenez, Ian Jones, Lisa Jones, Lina Jonsson, Rene S. Kahn, John R. Kelsoe, James L. Kennedy, Tilo Kircher, George Kirov, Sarah Kittel-Schneider, Farah Klöhn-Saghatolislam, James A. Knowles, Thorsten M. Kranz, Trine Vik Lagerberg, Mikael Landen, William B. Lawson, Marion Leboyer, Qingqin S. Li, Mario Maj, Dolores Malaspina, Mirko Manchia, Fermin Mayoral, Susan L. McElroy, Melvin G. McInnis, Andrew M. McIntosh, Helena Medeiros, Ingrid Melle, Vihra Milanova, Philip B. Mitchell, Palmiero Monteleone, Alessio Maria Monteleone, Markus M. Nöthen, Tomas Novak, John I. Nurnberger, Niamh O'Brien, Kevin S. O'Connell, Claire O'Donovan, Michael C. O'Donovan, Nils Opel, Abigail Ortiz, Michael J. Owen, Erik Pålsson, Carlos Pato, Michele T. Pato, Joanna Pawlak, Julia-Katharina Pfarr, Claudia Pisanu, James B. Potash, Mark H Rapaport, Daniela Reich-Erkelenz, Andreas Reif, Eva Reininghaus, Jonathan Repple, Hélène Richard-Lepouriel, Marcella Rietschel, Kai Ringwald, Gloria Roberts, Guy Rouleau, Sabrina Schaupp, William A Scheftner, Simon Schmitt, Peter R. Schofield, K. Oliver Schubert, Eva C. Schulte, Barbara Schweizer, Fanny Senner, Giovanni Severino, Sally Sharp, Claire Slaney, Olav B. Smeland, Janet L. Sobell, Alessio Squassina, Pavla Stopkova, John Strauss, Alfonso Tortorella, Gustavo Turecki, Joanna Twarowska-Hauser, Marin Veldic, Eduard Vieta, John B. Vincent, Wei Xu, Clement C. Zai, Peter P. Zandi, Psychiatric Genomics Consortium (PGC) Bipolar Disorder Working Group, International Consortium on Lithium Genetics (ConLiGen), Colombia-US Cross Disorder Collaboration in Psychiatric Genetics, Arianna Di Florio, Jordan W. Smoller, Joanna M. Biernacka, Francis J. McMahon, Martin Alda, Bertram Müller-Myhsok, Nikolaos Koutsouleris, Peter Falkai, Nelson B. Freimer, Till F.M. Andlauer, Thomas G. Schulze, Roel A. Ophoff
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- Journal:
- The British Journal of Psychiatry / Volume 219 / Issue 6 / December 2021
- Published online by Cambridge University Press:
- 25 August 2021, pp. 659-669
- Print publication:
- December 2021
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Background
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
AimsTo examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
MethodGenome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
ResultsEarlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
ConclusionsAAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
First diagnosis of psychosis in the prison: results from a data-linkage study
- Nabila Z. Chowdhury, Olayan Albalawi, Handan Wand, Armita Adily, Azar Kariminia, Stephen Allnutt, Grant Sara, Kimberlie Dean, Julia Lappin, Colman O'Driscoll, Luke Grant, Peter W. Schofield, David Greenberg, Tony Butler
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- Journal:
- BJPsych Open / Volume 5 / Issue 6 / November 2019
- Published online by Cambridge University Press:
- 14 October 2019, e89
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Background
Psychosis is more prevalent among people in prison compared with the community. Early detection is important to optimise health and justice outcomes; for some, this may be the first time they have been clinically assessed.
AimsDetermine factors associated with a first diagnosis of psychosis in prison and describe time to diagnosis from entry into prison.
MethodThis retrospective cohort study describes individuals identified for the first time with psychosis in New South Wales (NSW) prisons (2006–2012). Logistic regression was used to identify factors associated with a first diagnosis of psychosis. Cox regression was used to describe time to diagnosis from entry into prison.
ResultsOf the 38 489 diagnosed with psychosis for the first time, 1.7% (n = 659) occurred in prison. Factors associated with an increased likelihood of being diagnosed in prison (versus community) were: male gender (odds ratio (OR) = 2.27, 95% CI 1.79–2.89), Aboriginality (OR = 1.81, 95% CI 1.49–2.19), older age (OR = 1.70, 95% CI 1.37–2.11 for 25–34 years and OR = 1.63, 95% CI 1.29–2.06 for 35–44 years) and disadvantaged socioeconomic area (OR = 4.41, 95% CI 3.42–5.69). Eight out of ten were diagnosed within 3 months of reception.
ConclusionsAmong those diagnosed with psychosis for the first time, only a small number were identified during incarceration with most identified in the first 3 months following imprisonment. This suggests good screening processes are in place in NSW prisons for detecting those with serious mental illness. It is important these individuals receive appropriate care in prison, have the opportunity to have matters reheard and possibly diverted into treatment, and are subsequently connected to community mental health services on release.
Declaration of interestNone.
Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance
- Ming Li, Xiong-jian Luo, Mikael Landén, Sarah E. Bergen, Christina M. Hultman, Xiao Li, Wen Zhang, Yong-Gang Yao, Chen Zhang, Jiewei Liu, Manuel Mattheisen, Sven Cichon, Thomas W. Mühleisen, Franziska A. Degenhardt, Markus M. Nöthen, Thomas G. Schulze, Maria Grigoroiu-Serbanescu, Hao Li, Chris K. Fuller, Chunhui Chen, Qi Dong, Chuansheng Chen, Stéphane Jamain, Marion Leboyer, Frank Bellivier, Bruno Etain, Jean-Pierre Kahn, Chantal Henry, Martin Preisig, Zoltán Kutalik, Enrique Castelao, Adam Wright, Philip B. Mitchell, Janice M. Fullerton, Peter R. Schofield, Grant W. Montgomery, Sarah E. Medland, Scott D. Gordon, Nicholas G. Martin, MooDS Consortium, The Swedish Bipolar Study Group, Marcella Rietschel, Chunyu Liu, Joel E. Kleinman, Thomas M. Hyde, Daniel R. Weinberger, Bing Su
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- Journal:
- The British Journal of Psychiatry / Volume 208 / Issue 2 / February 2016
- Published online by Cambridge University Press:
- 02 January 2018, pp. 128-137
- Print publication:
- February 2016
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Background
Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.
AimsWe sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.
MethodTo detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.
ResultsIntegrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85×10–5). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54×10–8). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.
ConclusionsOur findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.
Application of the Audio Recorded Cognitive Screen and its relation to functioning in schizophrenia
- Brooke M. Gelder, Carmel M. Loughland, Vaughan J. Carr, Peter W. Schofield
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- Journal:
- Acta Neuropsychiatrica / Volume 27 / Issue 5 / October 2015
- Published online by Cambridge University Press:
- 11 May 2015, pp. 279-290
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Objective
This study investigated the ability of the Audio Recorded Cognitive Screen (ARCS) to detect cognitive deficit in individuals with schizophrenia, relative to the Mini Mental State Examination (MMSE) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and explored the associations between the ARCS and functional outcomes. We hypothesised that the ARCS would be able to better discriminate between individuals with schizophrenia and healthy controls than the MMSE, and that ARCS performance would be correlated with measures of social and vocational functioning.
MethodsThe participants were 19 community-dwelling individuals with schizophrenia or schizoaffective disorder and 19 healthy controls recruited from the Australian Schizophrenia Research Bank (ASRB). Participants completed the ARCS, MMSE, and self-report measures of social and vocational functioning. Clinical and diagnostic data stored by the ASRB were also utilised.
ResultsThe schizophrenia group performed worse than the control group on the ARCS, with memory, t(36)=2.49, p=0.02, 95% CI [−1.84, −18.79] and fluency, t(36)=2.40, p=0.02, 95% CI [−1.87, −22.24] domains being the main discriminating measures. The RBANS also discriminated between the two groups, and ARCS and RBANS total scores were moderately to strongly correlated. There was no difference between the two groups on the MMSE after controlling for demographic variables. ARCS performance was associated with employment status [χ2(1)=7.16, p=0.007].
ConclusionThe ARCS may be sensitive to the cognitive deficits in outpatients with schizophrenia and an indicator of functional outcomes in this population.
Brief neuropsychological profiles in psychosis: a pilot study using the Audio Recorded Cognitive Screen (ARCS)
- Carmel M Loughland, Joanne Allen, Louisa Gianacas, Peter W Schofield, Terry J Lewin, Mick Hunter, Vaughan J Carr
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- Journal:
- Acta Neuropsychiatrica / Volume 22 / Issue 5 / October 2010
- Published online by Cambridge University Press:
- 24 June 2014, pp. 243-252
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Loughland CM, Allen J, Gianacas L, Schofield PW, Lewin TJ, Hunter M, Carr VJ. Brief neuropsychological profiles in psychosis: a pilot study using the Audio Recorded Cognitive Screen (ARCS).
Objective:This pilot study examines the utility of a novel, standardised brief neuropsychological assessment tool (the ARCS, Audio Recorded Cognitive Screen) in a different clinical setting to that in which it was initially developed. We hypothesised that the ARCS would be feasible to administer to individuals with a psychotic illness and that it would detect cognitive deficits similar to those identified by an established instrument (the RBANS, Repeatable Battery for the Assessment of Neuropsychological Status).
Methods:Twenty-five people with psychosis (mean age = 43.72, SD = 9.78) and 25 age- and gender-matched controls were recruited from the Newcastle community (NSW, Australia). The ARCS and RBANS were completed about 1 week apart in a counterbalanced order.
Results:The ARCS was well received, performed satisfactorily and both the ARCS and RBANS were sensitive to deficits typically associated with psychosis (e.g. memory and attention). After controlling for memory deficits, the largest disparity between the psychosis and control groups was on the ARCS fluency domain [p < 0.001, partial Eta-squared (ηp2) = 0.21].
Conclusion:The ARCS uses audio administration (approximately 34 min) to reduce clinician time (to 3–5 min for scoring) and appears to be a useful brief assessment tool for examining the cognitive deficits associated with psychosis. However, the potential clinical utility of the ARCS needs to be investigated further in larger samples drawn from a wider variety of specialist and non-specialist settings.
Notes on Contributors
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- By Thomas M. Achenbach, Marc H. Bornstein, W. Thomas Boyce, Robert H. Bradley, Kelly Bridges, Jeanne Brooks-Gunn, Brenda K. Bryant, Sandra L. Calvert, Scott Coltrane, E. Mark Cummings, Stacey B. Daughters, Cindy DeCoste, Marc de Rosnay, Jacquelynne S. Eccles, Hadas Eidelman, Ruth Feldman, Peter Fonagy, Walter S. Gilliam, Andrea L. Gold, Elena L. Grigorenko, Sara Harkness, Sybil L. Hart, Jessica S. Henry, Erika Hoff, Tom Hollenstein, Stephanie M. Jones, Julia Kim-Cohen, Pamela K. Klebanov, Brett Laursen, Mary J. Levitt, Alicia F. Lieberman, Shoon Lio, Jessica F. Magidson, Ann S. Masten, David L. Molfese, Peter J. Molfese, Lynne Murray, Jelena Obradović, Lauren M. Papp, Ross D. Parke, Yaacov Petscher, Aelesia Pisciella, Aliza W. Pressman, Sarah Rabbitt, Craig T. Ramey, Sharon Landesman Ramey, Jessica M. Richards, Robert W. Roeser, Thomas J. Schofield, Ronald Seifer, Anne Shaffer, Michelle Sleed, Laura Stout Sosinsky, Nancy E. Suchman, Charles M. Super, Louis Tuthill, Patricia Van Horn, Eric Vega, Sarah Ward, Monica Yudron
- Edited by Linda Mayes, Yale University, Connecticut, Michael Lewis
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- Book:
- The Cambridge Handbook of Environment in Human Development
- Published online:
- 05 October 2012
- Print publication:
- 27 August 2012, pp ix-xvi
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Issues concerning feedback about genetic testing and risk of depression
- Kay Wilhelm, Bettina Meiser, Philip B. Mitchell, Adam W. Finch, Jennifer E. Siegel, Gordon Parker, Peter R. Schofield
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- Journal:
- The British Journal of Psychiatry / Volume 194 / Issue 5 / May 2009
- Published online by Cambridge University Press:
- 02 January 2018, pp. 404-410
- Print publication:
- May 2009
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Background
Recent studies show that adverse life events have a significantly greater impact on depression onset for those with the s/s allele of the genotype for the 5-HT gene-linked promoter region. Research in genes related to risk of depression leads to the question of how this information is received by individuals.
AimsTo investigate factors related to the response to receiving one's own serotonin transporter genotype results.
MethodPredictors of the impact of receiving individual genotype data were assessed in 128 participants in a study of gene–environment interaction in depression onset.
ResultsTwo-thirds decided to learn their individual genotype results (receivers) and prior to disclosure this decision was associated with a perception of greater benefit from receipt of the information (P=0.001). Receivers completing the 2-week (n=76) and 3-month follow-up (n=78) generally reported feeling pleased with the information and having had a more positive experience than distress. However, distress was related to genotype, with those with the s/s allele being most affected.
ConclusionsThere was high interest in, and satisfaction with, learning about one's serotonin transporter genotype. Participants appeared to understand that the gene conferred susceptibility to depression rather than a direct causal effect.