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Bogdan R, Baranger DAA, Agrawal A. Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences. Annu Rev Clin Psychol 2018; 14:119-157. [PMID: 29579395 PMCID: PMC7772939 DOI: 10.1146/annurev-clinpsy-050817-084847] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.
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Affiliation(s)
- Ryan Bogdan
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - David A A Baranger
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
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Kang HJ, Kim JW, Kim SY, Kim SW, Shin HY, Shin MG, Kim JM. The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology. Psychiatry Investig 2018; 15:538-545. [PMID: 29614851 PMCID: PMC5975999 DOI: 10.30773/pi.2017.10.2] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 10/02/2017] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE Depression is associated with a major disease burden, and many individuals suffer from depressive symptoms due to an insufficient response to ostensibly adequate antidepressant treatment. Therefore, it is important to identify reliable treatment response predictors for use in developing personalized treatment strategies. METHODS The MAKE Biomarker discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) study was performed to identify predictors of antidepressant response using a 2-year naturalistic prospective design. Participants in the MAKE BETTER study were consecutively recruited from patients who visited the Psychiatry Department of Chonnam National University Hospital, Gwangju, South Korea for treatment of a depressive disorder. Data on demographic and clinical characteristics, genetic markers measured by whole-exome sequencing, and blood markers were obtained. The types and doses of antidepressants were determined based on the clinical judgment of the psychiatrist, and the treatment outcomes (e.g., depressive and other psychiatric symptoms and issues related to safety) were assessed. RESULTS We will be able to use the data collected in this study to develop a treatment-response prediction index composed of biomarkers. CONCLUSION The MAKE BETTER study will provide an empirical basis for a personalized medicine approach to depression by enabling the prediction of antidepressant treatment response according the characteristics of each patient. It will thereby support evidence-based decision-making that decreases the use of a trial-and-error approach to the treatment of depressive disorders.
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Affiliation(s)
- Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Seon-Young Kim
- Mental Health Clinic, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Hee-Young Shin
- Department of Biomedical Science, Chonnam National University Medical School, and Clinical Trial Center, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Myung-Geun Shin
- Department of Laboratory Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea
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Fabbri C, Corponi F, Souery D, Kasper S, Montgomery S, Zohar J, Rujescu D, Mendlewicz J, Serretti A. The Genetics of Treatment-Resistant Depression: A Critical Review and Future Perspectives. Int J Neuropsychopharmacol 2018; 22:93-104. [PMID: 29688548 PMCID: PMC6368368 DOI: 10.1093/ijnp/pyy024] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 04/05/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND One-third of depressed patients develop treatment-resistant depression with the related sequelae in terms of poor functionality and worse prognosis. Solid evidence suggests that genetic variants are potentially valid predictors of antidepressant efficacy and could be used to provide personalized treatments. METHODS The present review summarizes genetic findings of treatment-resistant depression including results from candidate gene studies and genome-wide association studies. The limitations of these approaches are discussed, and suggestions to improve the design of future studies are provided. RESULTS Most studies used the candidate gene approach, and few genes showed replicated associations with treatment-resistant depression and/or evidence obtained through complementary approaches (e.g., gene expression studies). These genes included GRIK4, BDNF, SLC6A4, and KCNK2, but confirmatory evidence in large cohorts was often lacking. Genome-wide association studies did not identify any genome-wide significant association at variant level, but pathways including genes modulating actin cytoskeleton, neural plasticity, and neurogenesis may be associated with treatment-resistant depression, in line with results obtained by genome-wide association studies of antidepressant response. The improvement of aggregated tests (e.g., polygenic risk scores), possibly using variant/gene prioritization criteria, the increase in the covering of genetic variants, and the incorporation of clinical-demographic predictors of treatment-resistant depression are proposed as possible strategies to improve future pharmacogenomic studies. CONCLUSIONS Genetic biomarkers to identify patients with higher risk of treatment-resistant depression or to guide treatment in these patients are not available yet. Methodological improvements of future studies could lead to the identification of genetic biomarkers with clinical validity.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Filippo Corponi
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Daniel Souery
- Université Libre de Bruxelles and Psy Pluriel Centre Europèen de Psychologie Medicale, Brussels, Belgium
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Joseph Zohar
- Psychiatric Division, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Dan Rujescu
- Psychiatric Division, Chaim Sheba Medical Center, Ramat Gan, Israel,University Clinic for Psychiatry, Psychotherapy and Psychosomatic, Martin-Luther-University Halle-Wittenberg, Germany
| | - Julien Mendlewicz
- Psychiatric Division, Chaim Sheba Medical Center, Ramat Gan, Israel,Université Libre de Bruxelles, Brussels, Belgium
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy,Psychiatric Division, Chaim Sheba Medical Center, Ramat Gan, Israel,Correspondence: Alessandro Serretti, MD, PhD, Department of Biomedical and NeuroMotor Sciences, University of Bologna, Viale Carlo Pepoli 5, 40123 Bologna, Italy ()
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Santini SA, Panza F, Lozupone M, Bellomo A, Greco A, Seripa D. Genetics of tailored medicine: Focus on CNS drugs. Microchem J 2018. [DOI: 10.1016/j.microc.2017.02.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Amare AT, Schubert KO, Baune BT. Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry. EPMA J 2017; 8:211-227. [PMID: 29021832 DOI: 10.1007/s13167-017-0112-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 08/11/2017] [Indexed: 01/08/2023]
Abstract
Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.
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Affiliation(s)
- Azmeraw T Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA 5005 Australia
| | - Klaus Oliver Schubert
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA 5005 Australia.,Northern Adelaide Local Health Network, Mental Health Services, Adelaide, SA Australia
| | - Bernhard T Baune
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA 5005 Australia
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Maciukiewicz M, Marshe VS, Tiwari AK, Fonseka TM, Freeman N, Kennedy JL, Rotzinger S, Foster JA, Kennedy SH, Müller DJ. Genome-wide association studies of placebo and duloxetine response in major depressive disorder. THE PHARMACOGENOMICS JOURNAL 2017; 18:406-412. [PMID: 28696415 DOI: 10.1038/tpj.2017.29] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 03/01/2017] [Accepted: 03/16/2017] [Indexed: 12/28/2022]
Abstract
We investigated variants associated with treatment response in depressed patients treated with either the antidepressant duloxetine or placebo using a genome-wide approach. Our sample (N=391) included individuals aged 18-75 years, diagnosed with major depressive disorder and treated with either duloxetine or placebo for up to 8 weeks. We conducted genome-wide associations for treatment response as operationalized by percentage change in Montgomery-Åsberg Depression Rating Scale score from baseline, as well as mixed models analyses across five time points. In the placebo-treated subsample (N=205), we observed a genome-wide association with rs76767803 (β=0.69, P=1.25 × 10-8) upstream of STAC1. STAC1 rs76767803 was also associated with response using mixed model analysis (χ2=3.95; P=0.001). In the duloxetine-treated subsample (N=186), we observed suggestive associations with ZNF385D (rs4261893; β=-0.46, P=1.55 × 10-5), NCAM1 (rs2303377; β=0.45, P=1.76 × 10-5) and MLL5 (rs117986340; β=0.91, P=3.04 × 10-5). Our findings suggest that a variant upstream of STAC1 is associated with placebo response, which might have implications for treatment optimization, clinical trial design and drug development.
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Affiliation(s)
- M Maciukiewicz
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - V S Marshe
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - A K Tiwari
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - T M Fonseka
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada
| | - N Freeman
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - J L Kennedy
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - S Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University Health Network, Toronto, ON, Canada
| | - J A Foster
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
| | - S H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada
| | - D J Müller
- Pharmacogenetic Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Fabbri C. Is a polygenic predictor of antidepressant response a possibility? Pharmacogenomics 2017; 18:749-752. [PMID: 28592208 DOI: 10.2217/pgs-2017-0056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Chiara Fabbri
- Department of Biomedical & Neuromotor Sciences, University of Bologna, Viale Carlo Pepoli 5, 40123 Bologna, Italy.,Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
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