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Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2023; 273:113-127. [PMID: 35587279 PMCID: PMC9957888 DOI: 10.1007/s00406-022-01418-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/11/2022] [Indexed: 12/19/2022]
Abstract
Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Elisabeth B. Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
| | - Tanja M. Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
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2
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Chan RF, Turecki G, Shabalin AA, Guintivano J, Zhao M, Xie LY, van Grootheest G, Kaminsky ZA, Dean B, Penninx BW, Aberg KA, van den Oord EJ. Cell Type-Specific Methylome-wide Association Studies Implicate Neurotrophin and Innate Immune Signaling in Major Depressive Disorder. Biol Psychiatry 2020; 87:431-442. [PMID: 31889537 PMCID: PMC9933050 DOI: 10.1016/j.biopsych.2019.10.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 09/26/2019] [Accepted: 10/10/2019] [Indexed: 01/06/2023]
Abstract
BACKGROUND We sought to characterize methylation changes in brain and blood associated with major depressive disorder (MDD). As analyses of bulk tissue may obscure association signals and hamper the biological interpretation of findings, these changes were studied on a cell type-specific level. METHODS In 3 collections of human postmortem brain (n = 206) and 1 collection of blood samples (N = 1132) of MDD cases and controls, we used epigenomic deconvolution to perform cell type-specific methylome-wide association studies within subpopulations of neurons/glia for the brain data and granulocytes/T cells/B cells/monocytes for the blood data. Sorted neurons/glia from a fourth postmortem brain collection (n = 58) were used for validation purposes. RESULTS Cell type-specific methylome-wide association studies identified multiple findings in neurons/glia that were detected across brain collections and were reproducible in physically sorted nuclei. Cell type-specific analyses in blood samples identified methylome-wide significant associations in T cells, monocytes, and whole blood that replicated findings from a past methylation study of MDD. Pathway analyses implicated p75 neurotrophin receptor/nerve growth factor signaling and innate immune toll-like receptor signaling in MDD. Top results in neurons, glia, bulk brain, T cells, monocytes, and whole blood were enriched for genes supported by genome-wide association studies for MDD and other psychiatric disorders. CONCLUSIONS We both replicated and identified novel MDD-methylation associations in human brain and blood samples at a cell type-specific level. Our results provide mechanistic insights into how the immune system may interact with the brain to affect MDD susceptibility. Importantly, our findings involved associations with MDD in human samples that implicated many closely related biological pathways. These disease-linked sites and pathways represent promising new therapeutic targets for MDD.
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3
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Homan P, Argyelan M, DeRosse P, Szeszko PR, Gallego JA, Hanna L, Robinson DG, Kane JM, Lencz T, Malhotra AK. Structural similarity networks predict clinical outcome in early-phase psychosis. Neuropsychopharmacology 2019; 44:915-922. [PMID: 30679724 PMCID: PMC6461949 DOI: 10.1038/s41386-019-0322-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/17/2018] [Accepted: 01/16/2019] [Indexed: 02/06/2023]
Abstract
Despite recent advances, there is still a major need for prediction of treatment success in schizophrenia, a condition long considered a disorder of dysconnectivity in the brain. Graph theory provides a means to characterize the connectivity in both healthy and abnormal brains. We calculated structural similarity networks in each participant and hypothesized that the "hubness", i.e., the number of edges connecting a node to the rest of the network, would be associated with clinical outcome. This prospective controlled study took place at an academic research center and included 82 early-phase psychosis patients (23 females; mean age [SD] = 21.6 [5.5] years) and 58 healthy controls. Medications were administered in a double-blind randomized manner, and patients were scanned at baseline prior to treatment with second-generation antipsychotics. Symptoms were assessed with the Brief Psychiatric Rating Scale at baseline and over the course of 12 weeks. Nodal degree of structural similarity networks was computed for each subject and entered as a predictor of individual treatment response into a partial least squares (PLS) regression. The model fit was significant in a permutation test with 1000 permutations (P = 0.006), and the first two PLS regression components explained 29% (95% CI: 27; 30) of the variance in treatment response after cross-validation. Nodes loading strongly on the first PLS component were primarily located in the orbito- and prefrontal cortex, whereas nodes loading strongly on the second PLS component were primarily located in the superior temporal, precentral, and middle cingulate cortex. These data suggest a link between brain network morphology and clinical outcome in early-phase psychosis.
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Affiliation(s)
- Philipp Homan
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA. .,Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA. .,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA.
| | - Miklos Argyelan
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Pamela DeRosse
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Philip R. Szeszko
- 0000 0004 0420 1184grid.274295.fJames J. Peters Veterans Affairs Medical Center, Bronx, NY USA
| | - Juan A. Gallego
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Lauren Hanna
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Delbert G. Robinson
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - John M. Kane
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Todd Lencz
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Anil K. Malhotra
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
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Porcelli S, Lee SJ, Han C, Patkar AA, Albani D, Jun TY, Pae CU, Serretti A. Hot Genes in Schizophrenia: How Clinical Datasets Could Help to Refine their Role. J Mol Neurosci 2017; 64:273-286. [DOI: 10.1007/s12031-017-1016-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 12/12/2017] [Indexed: 11/25/2022]
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Palmer RHC, Beevers CG, McGeary JE, Brick LA, Knopik VS. A Preliminary Study of Genetic Variation in the Dopaminergic and Serotonergic Systems and Genome-wide Additive Genetic Effects on Depression Severity and Treatment Response. Clin Psychol Sci 2016; 5:158-165. [PMID: 28316879 DOI: 10.1177/2167702616651075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Major depression is a heritable disorder that is commonly treated with selective serotonin reuptake inhibitors. However, no study has quantified the overlap in genetic effects between pretreatment depression severity and treatment response and the extent to which genetic effects could be attributed to variation in the dopaminergic and serotonergic systems (DA/5-HT). Data (N=1618) from the clinician-rated Hamilton Rating Scale of Depression and the clinician-rated Quick Inventory of Depressive Symptomatology were obtained from participants of European ancestry in the Sequenced Treatment Alternatives to Relieve Depression clinical trial. Genetic variants explained 31%–64% of the variance across assessments of pretreatment depression severity and treatment response. However, effects from the DA/5-HT systems genes were negligible. There was also limited evidence for genetic overlap for pretreatment depression severity and treatment response. Despite the clear genetic contributions to these depression phenotypes, different genetic factors may contribute to depression severity and treatment response.
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Affiliation(s)
- Rohan H C Palmer
- Division of Behavioral Genetics, Department of Psychiatry, Rhode Island Hospital; Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University
| | - Christopher G Beevers
- Institute for Mental Health Research and Department of Psychology, The University of Texas at Austin
| | - John E McGeary
- Department of Veterans Affairs, Providence VA Medical Center; Division of Behavioral Genetics, Department of Psychiatry, Rhode Island Hospital; Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University
| | - Leslie A Brick
- Division of Behavioral Genetics, Department of Psychiatry, Rhode Island Hospital
| | - Valerie S Knopik
- Division of Behavioral Genetics, Department of Psychiatry, Rhode Island Hospital; Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University
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Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.
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Affiliation(s)
- Eugene Lin
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
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7
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Adkins DE, Clark SL, Copeland WE, Kennedy M, Conway K, Angold A, Maes H, Liu Y, Kumar G, Erkanli A, Patkar AA, Silberg J, Brown TH, Fergusson DM, Horwood LJ, Eaves L, van den Oord EJ, Sullivan PF, Costello EJ. Genome-Wide Meta-Analysis of Longitudinal Alcohol Consumption Across Youth and Early Adulthood. Twin Res Hum Genet 2015; 18:335-47. [PMID: 26081443 DOI: 10.1017/thg.2015.36] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N=2,126, obs=12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR<0.1) and six others met our 'suggestive' criterion (FDR<0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.
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8
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Cao C, Wang L, Wang R, Qing Y, Zhang J, Wu GWY. The COMT gene variant is associated with depression's decreased positive affect symptoms in Chinese adults. Psych J 2014; 3:264-72. [DOI: 10.1002/pchj.63] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 07/15/2014] [Indexed: 11/07/2022]
Affiliation(s)
- Chengqi Cao
- Key Laboratory of Mental Health; Institute of Psychology; Chinese Academy of Sciences; Beijing China
- University of Chinese Academy of Sciences; Beijing China
| | - Li Wang
- Key Laboratory of Mental Health; Institute of Psychology; Chinese Academy of Sciences; Beijing China
| | - Richu Wang
- Key Laboratory of Mental Health; Institute of Psychology; Chinese Academy of Sciences; Beijing China
- University of Chinese Academy of Sciences; Beijing China
| | - Yulan Qing
- Key Laboratory of Mental Health; Institute of Psychology; Chinese Academy of Sciences; Beijing China
- University of Chinese Academy of Sciences; Beijing China
| | - Jianxin Zhang
- Key Laboratory of Mental Health; Institute of Psychology; Chinese Academy of Sciences; Beijing China
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Lam YW. Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice. ISRN Pharmacol 2013; 2013:641089. [PMID: 23533802 DOI: 10.1155/2013/641089] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 02/04/2013] [Indexed: 12/20/2022]
Abstract
The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.
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Adkins DE, Souza RP, Aberg K, Clark SL, McClay JL, Sullivan PF, van den Oord EJCG. Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D. PLoS One 2013; 8:e55239. [PMID: 23405125 PMCID: PMC3566192 DOI: 10.1371/journal.pone.0055239] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2012] [Accepted: 12/27/2012] [Indexed: 11/18/2022] Open
Abstract
Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.
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Affiliation(s)
- Daniel E Adkins
- Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA, USA
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11
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GENDEP Investigators, MARS Investigators, STAR*D Investigators. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry 2013; 170:207-17. [PMID: 23377640 DOI: 10.1176/appi.ajp.2012.12020237] [Citation(s) in RCA: 166] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Indirect evidence suggests that common genetic variation contributes to individual differences in antidepressant efficacy among individuals with major depressive disorder, but previous studies may have been underpowered to detect these effects. METHOD A meta-analysis was performed on data from three genome-wide pharmacogenetic studies (the Genome-Based Therapeutic Drugs for Depression [GENDEP] project, the Munich Antidepressant Response Signature [MARS] project, and the Sequenced Treatment Alternatives to Relieve Depression [STAR*D] study), which included 2,256 individuals of Northern European descent with major depressive disorder, and antidepressant treatment outcomes were prospectively collected. After imputation, 1.2 million single-nucleotide polymorphisms were tested, capturing common variation for association with symptomatic improvement and remission after up to 12 weeks of antidepressant treatment. RESULTS No individual association met a genome-wide threshold for statistical significance in the primary analyses. A polygenic score derived from a meta-analysis of GENDEP and MARS participants accounted for up to approximately 1.2% of the variance in outcomes in STAR*D, suggesting a weakly concordant signal distributed over many polymorphisms. An analysis restricted to 1,354 individuals treated with citalopram (STAR*D) or escitalopram (GENDEP) identified an intergenic region on chromosome 5 associated with early improvement after 2 weeks of treatment. CONCLUSIONS Despite increased statistical power accorded by meta-analysis, the authors identified no reliable predictors of antidepressant treatment outcome, although they did identify modest, direct evidence that common genetic variation contributes to individual differences in antidepressant response.
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McClay JL. Institutional Profile: The Center for Biomarker Research and Personalized Medicine at Virginia Commonwealth University: advancing psychiatric drug treatment. Per Med 2012; 9:479-483. [PMID: 29768775 DOI: 10.2217/pme.12.52] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The Center for Biomarker Research and Personalized Medicine is a small, focused and technology-driven organization, sited within the School of Pharmacy on the Medical College of Virginia Campus of Virginia Commonwealth University. The Center was established in 2006, with a mission to improve understanding and treatment of psychiatric disease by employing the latest advances in molecular biology, informatics and statistics. We take the philosophy that large-scale, exploratory studies are crucial to achieve our aims because strong biological associations have been historically absent for psychiatric disorders. Our work follows two main streams: the first being disease biomarker research, such as discovering genes contributing risk for schizophrenia or depression. The second stream is the discovery of biomarkers for therapeutic drug response, where our genome-wide association studies of antipsychotic and antidepressant response have yielded multiple new leads. With the recent success of large-scale biological investigations of psychiatric disorders, we are very optimistic about the future. By engaging cutting-edge technologies such as next-generation DNA sequencing, coupled with biological data integration, we may further probe the biological underpinnings of psychiatric disorders and response to drug treatment.
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Affiliation(s)
- Joseph L McClay
- Center for Biomarker Research & Personalized Medicine, Virginia Commonwealth University, McGuire Hall, 1112 East Clay Street, Richmond, VA 23298, USA.
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Adkins DE, Clark SL, Åberg K, Hettema JM, Bukszár J, McClay JL, Souza RP, van den Oord EJ. Genome-wide pharmacogenomic study of citalopram-induced side effects in STAR*D. Transl Psychiatry 2012; 2:e129. [PMID: 22760553 DOI: 10.1038/tp.2012.57] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies (GWAS) to identify genetic variation influencing susceptibility to citalopram-induced side effects. The analysis sample consisted of 1762 depression patients, successfully genotyped for 421K single-nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study. Outcomes included five indicators of citalopram side effects: general side effect burden, overall tolerability, sexual side effects, dizziness and vision/hearing side effects. Two SNPs met our genome-wide significance criterion (q<0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q<0.5). The top finding was rs17135437, an intronic SNP within EMID2, mediating the effects of citalopram on vision/hearing side effects (P=3.27 × 10(-8), q=0.026). The second genome-wide significant finding, representing a haplotype spanning ∼30 kb and eight genotyped SNPs in a gene desert on chromosome 13, was associated with general side effect burden (P=3.22 × 10(-7), q=0.096). Suggestive findings were also found for SNPs at LAMA1, AOX2P, EGFLAM, FHIT and RTP2. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antidepressant medications.
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Abstract
BACKGROUND Understanding individual differences in susceptibility to antidepressant therapy side-effects is essential to optimize the treatment of depression. METHOD We performed genome-wide association studies (GWAS) to search for genetic variation affecting the susceptibility to side-effects. The analysis sample consisted of 1439 depression patients, successfully genotyped for 421K single nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included four indicators of side-effects: general side-effect burden, sexual side-effects, dizziness and vision/hearing-related side-effects. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries. RESULTS Thirty-four SNPs satisfied this criterion. The top finding indicated that 10 SNPs in SACM1L mediated the effects of bupropion on sexual side-effects (p = 4.98 × 10(-7), q = 0.023). Suggestive findings were also found for SNPs in MAGI2, DTWD1, WDFY4 and CHL1. CONCLUSIONS Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that could mediate adverse effects of antidepressant medication.
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Affiliation(s)
- S L Clark
- Center for Biomarker Research and Personalized Medicine, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA 23298-0581, USA.
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Åberg K, Khachane AN, Rudolf G, Nerella S, Fugman DA, Tischfield JA, van den Oord EJ. Methylome-wide comparison of human genomic DNA extracted from whole blood and from EBV-transformed lymphocyte cell lines. Eur J Hum Genet 2012; 20:953-5. [PMID: 22378283 DOI: 10.1038/ejhg.2012.33] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
DNA from Epstein-Barr virus-transformed lymphocyte cell lines (LCLs) has proven useful for studies of genetic sequence polymorphisms. Whether LCL DNA is suitable for methylation studies is less clear. We conduct a genome-wide methylation investigation using an array set with 45 million probes to investigate the methylome of LCL DNA and technical duplicates of WB DNA from the same 10 individuals. We focus specifically on methylation sites that show variation between individuals and, therefore, are potentially useful as biomarkers. The sample correlations for the methylation variable probes ranged from 0.69 to 0.78 for the WB duplicates and from 0.27 to 0.72 for WB vs LCL. To compare the pattern of the methylation signals, we grouped adjacent probes based on their inter-correlations. These analyses showed ∼29 000 and ∼14 000 blocks in WB and LCL, respectively. Merely 31% of the methylated regions detected in WB were detectable in LCLs. Furthermore, we observed significant differences in mean difference between WB and LCL as compared with duplicates of WB (P-value =2.2 × 10(-16)). Our study shows that there are substantial differences in the DNA methylation patterns between LCL and WB. Thus, LCL DNA should not be used as a proxy for WB DNA in methylome-wide studies.
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Abstract
OBJECTIVES To review neuroimaging intermediate phenotypes of MDD and their relation to genetic risk variants. METHODS A systematic literature search of peer-reviewed English language articels using PubMed ( www.pubmed.org ) was performed. RESULTS Comprehensive evidence on the influence of serotonergic genes (SLC6A4, HTR1A, MAOA, TPH2) and BDNF on the following neural intermediate phenotypes is displayed: amygdala reactivity, coupling of amygdala-anterior cingulate cortex (ACC) activity, ACC volume, hippocampal volume and serotonin receptor 1A (5-HT1A) binding potential (BP). CONCLUSIONS Intermediate phenotypes may bridge the gap between genotype and phenotype by reducing the impreciseness of psychiatric phenotypes and yield more insights into the underlying biology.
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Affiliation(s)
- Christian Scharinger
- Division of Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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