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Grigoroiu-Serbanescu M, van der Veen T, Bigdeli T, Herms S, Diaconu CC, Neagu AI, Bass N, Thygesen J, Forstner AJ, Nöthen MM, McQuillin A. Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder. J Affect Disord 2024; 356:507-518. [PMID: 38640977 DOI: 10.1016/j.jad.2024.04.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 04/21/2024]
Abstract
AIM We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. METHODS We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. RESULTS SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LIMITATIONS The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. CONCLUSION Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.
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Affiliation(s)
- Maria Grigoroiu-Serbanescu
- Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania.
| | - Tracey van der Veen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Tim Bigdeli
- SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Stefan Herms
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | | | | | - Nicholas Bass
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Johan Thygesen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 PMCID: PMC11144247 DOI: 10.1038/s41398-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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Joo YY, Lee E, Kim BG, Kim G, Seo J, Cha J. Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595444. [PMID: 38826224 PMCID: PMC11142157 DOI: 10.1101/2024.05.22.595444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.
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Arena G, Landoulsi Z, Grossmann D, Payne T, Vitali A, Delcambre S, Baron A, Antony P, Boussaad I, Bobbili DR, Sreelatha AAK, Pavelka L, J Diederich N, Klein C, Seibler P, Glaab E, Foltynie T, Bandmann O, Sharma M, Krüger R, May P, Grünewald A. Polygenic Risk Scores Validated in Patient-Derived Cells Stratify for Mitochondrial Subtypes of Parkinson's Disease. Ann Neurol 2024. [PMID: 38767023 DOI: 10.1002/ana.26949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE The aim of our study is to better understand the genetic architecture and pathological mechanisms underlying neurodegeneration in idiopathic Parkinson's disease (iPD). We hypothesized that a fraction of iPD patients may harbor a combination of common variants in nuclear-encoded mitochondrial genes ultimately resulting in neurodegeneration. METHODS We used mitochondria-specific polygenic risk scores (mitoPRSs) and created pathway-specific mitoPRSs using genotype data from different iPD case-control datasets worldwide, including the Luxembourg Parkinson's Study (412 iPD patients and 576 healthy controls) and COURAGE-PD cohorts (7,270 iPD cases and 6,819 healthy controls). Cellular models from individuals stratified according to the most significant mitoPRS were subsequently used to characterize different aspects of mitochondrial function. RESULTS Common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk in independent cohorts (Luxembourg Parkinson's Study odds ratio, OR = 1.31[1.14-1.50], p-value = 5.4e-04; COURAGE-PD OR = 1.23[1.18-1.27], p-value = 1.5e-29). Functional analyses in fibroblasts and induced pluripotent stem cells-derived neuronal progenitors revealed significant differences in mitochondrial respiration between iPD patients with high or low OXPHOS-PRS (p-values < 0.05). Clinically, iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients (false discovery rate [FDR]-adj p-value = 0.015), similar to prototypic monogenic forms of PD. Finally, iPD patients with high OXPHOS-PRS responded more effectively to treatment with mitochondrially active ursodeoxycholic acid. INTERPRETATION OXPHOS-PRS may provide a precision medicine tool to stratify iPD patients into a pathogenic subgroup genetically defined by specific mitochondrial impairment, making these individuals eligible for future intelligent clinical trial designs. ANN NEUROL 2024.
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Affiliation(s)
- Giuseppe Arena
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Zied Landoulsi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dajana Grossmann
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Translational Neurodegeneration Section "Albrecht-Kossel", Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Thomas Payne
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Armelle Vitali
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sylvie Delcambre
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexandre Baron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Paul Antony
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ibrahim Boussaad
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dheeraj Reddy Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ashwin Ashok Kumar Sreelatha
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Lukas Pavelka
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Nico J Diederich
- Department of Neurosciences, Centre Hospitalier de Luxembourg, Strassen, Luxembourg
| | - Christine Klein
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Philip Seibler
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
| | - Oliver Bandmann
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anne Grünewald
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
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Kopell BH, Kaji DA, Liharska LE, Vornholt E, Valentine A, Lund A, Hashemi A, Thompson RC, Lohrenz T, Johnson JS, Bussola N, Cheng E, Park YJ, Shah P, Ma W, Searfoss R, Qasim S, Miller GM, Chand NM, Aristel A, Humphrey J, Wilkins L, Ziafat K, Silk H, Linares LM, Sullivan B, Feng C, Batten SR, Bang D, Barbosa LS, Twomey T, White JP, Vannucci M, Hadj-Amar B, Cohen V, Kota P, Moya E, Rieder MK, Figee M, Nadkarni GN, Breen MS, Kishida KT, Scarpa J, Ruderfer DM, Narain NR, Wang P, Kiebish MA, Schadt EE, Saez I, Montague PR, Beckmann ND, Charney AW. Multiomic foundations of human prefrontal cortex tissue function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307537. [PMID: 38798344 PMCID: PMC11118644 DOI: 10.1101/2024.05.17.24307537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The prefrontal cortex (PFC) is a region of the brain that in humans is involved in the production of higher-order functions such as cognition, emotion, perception, and behavior. Neurotransmission in the PFC produces higher-order functions by integrating information from other areas of the brain. At the foundation of neurotransmission, and by extension at the foundation of higher-order brain functions, are an untold number of coordinated molecular processes involving the DNA sequence variants in the genome, RNA transcripts in the transcriptome, and proteins in the proteome. These "multiomic" foundations are poorly understood in humans, perhaps in part because most modern studies that characterize the molecular state of the human PFC use tissue obtained when neurotransmission and higher-order brain functions have ceased (i.e., the postmortem state). Here, analyses are presented on data generated for the Living Brain Project (LBP) to investigate whether PFC tissue from individuals with intact higher-order brain function has characteristic multiomic foundations. Two complementary strategies were employed towards this end. The first strategy was to identify in PFC samples obtained from living study participants a signature of RNA transcript expression associated with neurotransmission measured intracranially at the time of PFC sampling, in some cases while participants performed a task engaging higher-order brain functions. The second strategy was to perform multiomic comparisons between PFC samples obtained from individuals with intact higher-order brain function at the time of sampling (i.e., living study participants) and PFC samples obtained in the postmortem state. RNA transcript expression within multiple PFC cell types was associated with fluctuations of dopaminergic, serotonergic, and/or noradrenergic neurotransmission in the substantia nigra measured while participants played a computer game that engaged higher-order brain functions. A subset of these associations - termed the "transcriptional program associated with neurotransmission" (TPAWN) - were reproduced in analyses of brain RNA transcript expression and intracranial neurotransmission data obtained from a second LBP cohort and from a cohort in an independent study. RNA transcripts involved in TPAWN were found to be (1) enriched for RNA transcripts associated with measures of neurotransmission in rodent and cell models, (2) enriched for RNA transcripts encoded by evolutionarily constrained genes, (3) depleted of RNA transcripts regulated by common DNA sequence variants, and (4) enriched for RNA transcripts implicated in higher-order brain functions by human population genetic studies. In PFC excitatory neurons of living study participants, higher expression of the genes in TPAWN tracked with higher expression of RNA transcripts that in rodent PFC samples are markers of a class of excitatory neurons that connect the PFC to deep brain structures. TPAWN was further reproduced by RNA transcript expression patterns differentiating living PFC samples from postmortem PFC samples, and significant differences between living and postmortem PFC samples were additionally observed with respect to (1) the expression of most primary RNA transcripts, mature RNA transcripts, and proteins, (2) the splicing of most primary RNA transcripts into mature RNA transcripts, (3) the patterns of co-expression between RNA transcripts and proteins, and (4) the effects of some DNA sequence variants on RNA transcript and protein expression. Taken together, this report highlights that studies of brain tissue obtained in a safe and ethical manner from large cohorts of living individuals can help advance understanding of the multiomic foundations of brain function.
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Cuesta MJ, García de Jalón E, Sánchez-Torres AM, Gil-Berrozpe GJ, Aranguren L, Gutierrez G, Corrales A, Zarzuela A, Ibañez B, Peralta V. Additive effects of a family history of schizophrenia spectrum disorders and an environmental risk score for the outcome of patients with non-affective first-episode psychosis. Psychol Med 2024:1-9. [PMID: 38505954 DOI: 10.1017/s0033291724000576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
BACKGROUND First-episode psychotic disorders comprise a heterogeneous phenotype with a complex etiology involving numerous common small-effect genetic variations and a wide range of environmental exposures. We examined whether a family of schizophrenia spectrum disorder (FH-Sz) interacts with an environmental risk score (ERS-Sz) regarding the outcome of patients with non-affective first episode psychosis (NAFEP). METHODS We included 288 patients with NAFEP who were evaluated after discharge from an intensive 2-year program. We evaluated three outcome measures: symptomatic remission, psychosocial functioning, and personal recovery. We analyzed the main and joint associations of a FH-Sz and the ERS-Sz on the outcomes by using the relative excess risk due to interaction (RERI) approach. RESULTS A FH-Sz showed a significant association with poor symptomatic remission and psychosocial functioning outcomes, although there was no significant interaction between a FH-Sz and the ERS-Sz on these outcomes. The ERS-Sz did not show a significant association with poor symptomatic remission and psychosocial functioning outcomes, even though the magnitude of the interaction between ERS-Sz and FH-Sz with the later outcome was moderate (RERI = 6.89, 95% confidence interval -16.03 to 29.81). There was no association between a FH-Sz and the ERS-Sz and personal recovery. CONCLUSIONS Our results provide further empirical support regarding the contribution of FH-Sz to poor symptomatic remission and poor psychosocial functioning outcomes in patients with NAFEP.
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Affiliation(s)
- Manuel J Cuesta
- Department of Psychiatry, Hospital Universitario de Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Elena García de Jalón
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
| | - Ana M Sánchez-Torres
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Departament of Health Sciences, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Gustavo J Gil-Berrozpe
- Department of Psychiatry, Hospital Universitario de Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Lidia Aranguren
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
| | - Gerardo Gutierrez
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
| | - Asier Corrales
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
| | - Amalia Zarzuela
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
| | - Berta Ibañez
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Methodology Unit, Navarrabiomed - HUN - UPNA, RICAPPS, Pamplona, Spain
| | - Víctor Peralta
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
- Mental Health Department, Servicio Navarro de Salud - Osasunbidea, Pamplona, Spain
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Kochunov P, Ma Y, Hatch KS, Gao S, Acheson A, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der Vaart A, Chiappelli J, Du X, Sotiras A, Kvarta MD, Ma T, Chen S, Hong LE. Ancestral, Pregnancy, and Negative Early-Life Risks Shape Children's Brain (Dis)similarity to Schizophrenia. Biol Psychiatry 2023; 94:332-340. [PMID: 36948435 PMCID: PMC10511664 DOI: 10.1016/j.biopsych.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Familial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children. METHODS We used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child's cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control differences. RESULTS In children with a family history of SSD, the regional vulnerability index captured significantly more variance in ancestral history than traditional whole-brain and regional brain measurements. In children with and without family history of SSD, the regional vulnerability index also captured more variance associated with negative pre/perinatal environment and early-life experiences than traditional brain measurements. CONCLUSIONS In summary, in a cohort in which most children will not develop SSD, familial, pre/perinatal, and early developmental risks can alter brain patterns in the direction observed in adult patients with SSD. Individual similarity to adult SSD patterns may provide an early biomarker of the effects of genetic and developmental risks on the brain prior to psychotic or prodromal symptom onset.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Andrew Van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Aris Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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8
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de Hemptinne MC, Posthuma D. Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits. Nat Neurosci 2023:10.1038/s41593-023-01333-4. [PMID: 37217727 DOI: 10.1038/s41593-023-01333-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 04/14/2023] [Indexed: 05/24/2023]
Abstract
Genome-wide association studies have led to the identification of robust statistical associations of genetic variants with numerous brain-related traits, including neurological and psychiatric conditions, and psychological and behavioral measures. These results may provide insight into the biology underlying these traits and may facilitate clinically useful predictions. However, these results also carry the risk of harm, including possible negative effects of inaccurate predictions, violations of privacy, stigma and genomic discrimination, raising serious ethical and legal implications. Here, we discuss ethical concerns surrounding the results of genome-wide association studies for individuals, society and researchers. Given the success of genome-wide association studies and the increasing availability of nonclinical genomic prediction technologies, better laws and guidelines are urgently needed to regulate the storage, processing and responsible use of genetic data. Also, researchers should be aware of possible misuse of their results, and we provide guidance to help avoid such negative impacts on individuals and society.
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Affiliation(s)
- Matthieu C de Hemptinne
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
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9
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Miyahara K, Hino M, Shishido R, Nagaoka A, Izumi R, Hayashi H, Kakita A, Yabe H, Tomita H, Kunii Y. Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis. Transl Psychiatry 2023; 13:144. [PMID: 37142572 PMCID: PMC10160042 DOI: 10.1038/s41398-023-02449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023] Open
Abstract
Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.
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Affiliation(s)
- Kazusa Miyahara
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Mizuki Hino
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Risa Shishido
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Atsuko Nagaoka
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Ryuta Izumi
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hideki Hayashi
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hirooki Yabe
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Tohoku University Hospital, Miyagi, Japan
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Miyagi, Japan
| | - Yasuto Kunii
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan.
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan.
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10
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Nakamura T, Takata A. The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research. Mol Psychiatry 2023; 28:1868-1889. [PMID: 36878965 PMCID: PMC10575785 DOI: 10.1038/s41380-023-02005-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/08/2023]
Abstract
Despite enormous efforts employing various approaches, the molecular pathology in the schizophrenia brain remains elusive. On the other hand, the knowledge of the association between the disease risk and changes in the DNA sequences, in other words, our understanding of the genetic pathology of schizophrenia, has dramatically improved over the past two decades. As the consequence, now we can explain more than 20% of the liability to schizophrenia by considering all analyzable common genetic variants including those with weak or no statistically significant association. Also, a large-scale exome sequencing study identified single genes whose rare mutations substantially increase the risk for schizophrenia, of which six genes (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) showed odds ratios larger than ten. Based on these findings together with the preceding discovery of copy number variants (CNVs) with similarly large effect sizes, multiple disease models with high etiological validity have been generated and analyzed. Studies of the brains of these models, as well as transcriptomic and epigenomic analyses of patient postmortem tissues, have provided new insights into the molecular pathology of schizophrenia. In this review, we overview the current knowledge acquired from these studies, their limitations, and directions for future research that may redefine schizophrenia based on biological alterations in the responsible organ rather than operationalized criteria.
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Affiliation(s)
- Takumi Nakamura
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Atsushi Takata
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Research Institute for Diseases of Old Age, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
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11
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Xin J, Jiang X, Li H, Chen S, Zhang Z, Wang M, Gu D, Du M, Christiani DC. Prognostic evaluation of polygenic risk score underlying pan-cancer analysis: evidence from two large-scale cohorts. EBioMedicine 2023; 89:104454. [PMID: 36739632 PMCID: PMC9931923 DOI: 10.1016/j.ebiom.2023.104454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/07/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Polygenic risk score (PRS) has been demonstrated to be effective in identifying individuals at high risk of developing cancer, but its prognostic value remains unclear. METHODS We constructed site-specific PRSs by aggregating the risk effect of independent variants derived from previous genome-wide association studies (GWASs) across 17 cancer types. The Cox proportional hazards model was used to evaluate the association of each PRS with cancer survival, leveraging data from two prospective European cohorts, namely the UK Biobank involving 19,628 incident cases and The Cancer Genome Atlas involving 7079 prevalent cases. The combined PRS (CPRS), determined by merging site-specific PRSs, was further used to assess the prognostic effect of PRS on overall cancer in a sex-specific manner. FINDINGS We discovered that the cancer risk-related PRS was associated with neither overall survival (OS) nor cancer-specific survival (CSS) of each site-specific cancer with an underlying false discovery rate (FDR) P > 0.05, as evidenced by consistent findings from the two cohorts. Furthermore, the fixed-effect meta-analysis of the two cohorts provided no evidence to support for an association between CPRS and overall cancer survival in both males [OS: hazard ratio (HR)meta = 1.00, Pmeta = 0.760; CSS: HRmeta = 1.01, Pmeta = 0.447] and females (OS: HRmeta = 0.97, Pmeta = 0.067; CSS: HRmeta = 0.96, Pmeta = 0.054). Similar results were observed across multiple sensitivity analyses. INTERPRETATION Our findings indicate that the risk-specific PRS might not be a clinically useful tool in cancer prognosis prediction and further studies focusing on the development of polygenic prognostic score are warranted. FUNDING This project was funded by the National Natural Science Foundation of China (82173601 and 82073631), and Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).
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Affiliation(s)
- Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Xia Jiang
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huiqin Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Silu Chen
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA.
| | - David C Christiani
- Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
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12
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Kato H, Kimura H, Kushima I, Takahashi N, Aleksic B, Ozaki N. The genetic architecture of schizophrenia: review of large-scale genetic studies. J Hum Genet 2023; 68:175-182. [PMID: 35821406 DOI: 10.1038/s10038-022-01059-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/12/2022] [Accepted: 06/20/2022] [Indexed: 11/09/2022]
Abstract
Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.
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Affiliation(s)
- Hidekazu Kato
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Nagahide Takahashi
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Branko Aleksic
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan.,Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
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13
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Hara T, Owada Y, Takata A. Genetics of bipolar disorder: insights into its complex architecture and biology from common and rare variants. J Hum Genet 2023; 68:183-191. [PMID: 35614313 DOI: 10.1038/s10038-022-01046-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/28/2022] [Accepted: 05/11/2022] [Indexed: 11/09/2022]
Abstract
Bipolar disorder (BD) is a common mental disorder characterized by recurrent mood episodes, which causes major socioeconomic burdens globally. Though its disease pathogenesis is largely unknown, the high heritability of BD indicates strong contributions from genetic factors. In this review, we summarize the recent achievements in the genetics of BD, particularly those from genome-wide association study (GWAS) of common variants and next-generation sequencing analysis of rare variants. These include the identification of dozens of robust disease-associated loci, deepening of our understanding of the biology of BD, objective description of correlations with other psychiatric disorders and behavioral traits, formulation of methods for predicting disease risk and drug response, and the discovery of a single gene associated with bipolar disorder and schizophrenia spectrum with a large effect size. On the other hand, the findings to date have not yet made a clear contribution to the improvement of clinical psychiatry of BD. We overview the remaining challenges as well as possible paths to resolve them, referring to studies of other major neuropsychiatric disorders.
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Affiliation(s)
- Tomonori Hara
- Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.,Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Sendai, Miyagi, 980-8575, Japan
| | - Yuji Owada
- Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Sendai, Miyagi, 980-8575, Japan
| | - Atsushi Takata
- Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.
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14
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Allesøe RL, Thompson WK, Bybjerg-Grauholm J, Hougaard DM, Nordentoft M, Werge T, Rasmussen S, Benros ME. Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data. JAMA Psychiatry 2023; 80:146-155. [PMID: 36477816 PMCID: PMC9857190 DOI: 10.1001/jamapsychiatry.2022.4076] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Importance Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy. Objective To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics. Design, Setting, and Participants This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022. Exposure At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ. Main Outcomes and Measures The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance. Results A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72. Conclusions and Relevance Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Wesley K. Thompson
- Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - David M. Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Institute of Biological Psychiatry, Mental Health Centre Sct Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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15
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Wang Y, Namba S, Lopera E, Kerminen S, Tsuo K, Läll K, Kanai M, Zhou W, Wu KH, Favé MJ, Bhatta L, Awadalla P, Brumpton B, Deelen P, Hveem K, Lo Faro V, Mägi R, Murakami Y, Sanna S, Smoller JW, Uzunovic J, Wolford BN, Willer C, Gamazon ER, Cox NJ, Surakka I, Okada Y, Martin AR, Hirbo J. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. CELL GENOMICS 2023; 3:100241. [PMID: 36777179 PMCID: PMC9903818 DOI: 10.1016/j.xgen.2022.100241] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 08/28/2022] [Accepted: 12/03/2022] [Indexed: 01/06/2023]
Abstract
Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Esteban Lopera
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kuan-Han Wu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA
| | | | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olav’s Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Patrick Deelen
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Valeria Lo Faro
- Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Serena Sanna
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
- Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), 09100 Cagliari, Italy
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Brooke N. Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
| | - Cristen Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biostatistics and Center for Statistical Genetics, and Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eric R. Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC) and Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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16
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Gene set enrichment analysis of pathophysiological pathways highlights oxidative stress in psychosis. Mol Psychiatry 2022; 27:5135-5143. [PMID: 36131045 PMCID: PMC9763118 DOI: 10.1038/s41380-022-01779-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/14/2023]
Abstract
Polygenic risk prediction remains an important aim of genetic association studies. Currently, the predictive power of schizophrenia polygenic risk scores (PRSs) is not large enough to allow highly accurate discrimination between cases and controls and thus is not adequate for clinical integration. Since PRSs are rarely used to reveal biological functions or to validate candidate pathways, to fill this gap, we investigated whether their predictive ability could be improved by building genome-wide (GW-PRSs) and pathway-specific PRSs, using distance- or expression quantitative trait loci (eQTLs)- based mapping between genetic variants and genes. We focused on five pathways (glutamate, oxidative stress, GABA/interneurons, neuroimmune/neuroinflammation and myelin) which belong to a critical hub of schizophrenia pathophysiology, centred on redox dysregulation/oxidative stress. Analyses were first performed in the Lausanne Treatment and Early Intervention in Psychosis Program (TIPP) study (n = 340, cases/controls: 208/132), a sample of first-episode of psychosis patients and matched controls, and then validated in an independent study, the epidemiological and longitudinal intervention program of First-Episode Psychosis in Cantabria (PAFIP) (n = 352, 224/128). Our results highlighted two main findings. First, GW-PRSs for schizophrenia were significantly associated with early psychosis status. Second, oxidative stress was the only significantly associated pathway that showed an enrichment in both the TIPP (p = 0.03) and PAFIP samples (p = 0.002), and exclusively when gene-variant linking was done using eQTLs. The results suggest that the predictive accuracy of polygenic risk scores could be improved with the inclusion of information from functional annotations, and through a focus on specific pathways, emphasizing the need to build and study functionally informed risk scores.
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17
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Wang T, Kim CN, Bakken TE, Gillentine MA, Henning B, Mao Y, Gilissen C, Nowakowski TJ, Eichler EE. Integrated gene analyses of de novo variants from 46,612 trios with autism and developmental disorders. Proc Natl Acad Sci U S A 2022; 119:e2203491119. [PMID: 36350923 PMCID: PMC9674258 DOI: 10.1073/pnas.2203491119] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 09/28/2022] [Indexed: 08/15/2023] Open
Abstract
Most genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here, we analyzed de novo variants (DNVs) from 15,560 ASD (6,557 from SPARK) and 31,052 DD trios independently and also combined as broader neurodevelopmental disorders (NDDs) using three models. We identify 615 NDD candidate genes (false discovery rate [FDR] < 0.05) supported by ≥1 models, including 138 reaching Bonferroni exome-wide significance (P < 3.64e-7) in all models. The genes group into five functional networks associating with different brain developmental lineages based on single-cell nuclei transcriptomic data. We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes that show mutational bias, including enrichments for missense (n = 41) or truncating (n = 12) DNVs. We also find 10 genes with evidence of male- or female-bias enrichment, including 4 X chromosome genes with significant female burden (DDX3X, MECP2, WDR45, and HDAC8). This large-scale integrative analysis identifies candidates and functional subsets of NDD genes.
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Affiliation(s)
- Tianyun Wang
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, Beijing, 100191, China
- Neuroscience Research Institute, Peking University, Key Laboratory for Neuroscience, Ministry of Education of China & National Health Commission of China, Beijing, 100191, China
| | - Chang N. Kim
- Department of Anatomy, University of California, San Francisco, CA 94143
| | | | - Madelyn A. Gillentine
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195
| | - Barbara Henning
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195
| | - Yafei Mao
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Christian Gilissen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | | | - Tomasz J. Nowakowski
- Department of Anatomy, University of California, San Francisco, CA 94143
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195
- HHMI, University of Washington, Seattle, WA 98195
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18
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Takamiya A, Kishimoto T. Is this the end of precision medicine? Or the beginning? Lancet Psychiatry 2022; 9:849-850. [PMID: 36228646 DOI: 10.1016/s2215-0366(22)00336-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/07/2022] [Indexed: 11/24/2022]
Affiliation(s)
- Akihiro Takamiya
- Neuropsychiatry Department, Keio University School of Medicine, Tokyo, Japan
| | - Taishiro Kishimoto
- Neuropsychiatry Department, Keio University School of Medicine, Tokyo, Japan; Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan.
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19
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Fusar-Poli L, Rutten BPF, van Os J, Aguglia E, Guloksuz S. Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype? Int Rev Psychiatry 2022; 34:663-675. [PMID: 36786114 DOI: 10.1080/09540261.2022.2101352] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.
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Affiliation(s)
- Laura Fusar-Poli
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands.,UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eugenio Aguglia
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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20
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Affiliation(s)
- Cathryn M Lewis
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
| | - Evangelos Vassos
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
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21
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Kochunov P, Ma Y, Hatch KS, Gao S, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der vaart A, Goldwaser EL, Sotiras A, Kvarta MD, Ma T, Chen S, Nichols TE, Hong LE. Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses. Hum Brain Mapp 2022; 43:4970-4983. [PMID: 36040723 PMCID: PMC9582367 DOI: 10.1002/hbm.26056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 01/06/2023] Open
Abstract
Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain-wide similarity to the expected SMI patterns derived from meta-analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome-wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI-MDD (Cohen's d = 0.20, p = 1 × 10-23 ) and PRS-MDD (d = 0.17, p = 1 × 10-15 ) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10-5 and .009, respectively) and PRS (d = 0.57 and 1.34; p = .002 and .002, respectively). Elevated RVI-SSD were replicated in an independent sample (d = 0.53, p = 5 × 10-5 ). RVI-MDD and RVI-SSD but not RVI-BD were associated with childhood adversity (p < .01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10-5 ) in six out of seven domains and showed specificity with disorder-associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Kathryn S. Hatch
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Bhim M. Adhikari
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Andrew Van der vaart
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Eric L. Goldwaser
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Aris Sotiras
- Institute of Informatics, University of WashingtonSchool of MedicineSt. LouisMissouriUSA
| | - Mark D. Kvarta
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Tianzhou Ma
- Department of Epidemiology and BiostatisticsUniversity of MarylandCollege ParkMarylandUSA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Thomas E. Nichols
- Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
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22
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Barriers to genetic testing in clinical psychiatry and ways to overcome them: from clinicians' attitudes to sociocultural differences between patients across the globe. Transl Psychiatry 2022; 12:442. [PMID: 36220808 PMCID: PMC9553897 DOI: 10.1038/s41398-022-02203-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 09/15/2022] [Accepted: 09/23/2022] [Indexed: 11/08/2022] Open
Abstract
Genetic testing has evolved rapidly over recent years and new developments have the potential to provide insights that could improve the ability to diagnose, treat, and prevent diseases. Information obtained through genetic testing has proven useful in other specialties, such as cardiology and oncology. Nonetheless, a range of barriers impedes techniques, such as whole-exome or whole-genome sequencing, pharmacogenomics, and polygenic risk scoring, from being implemented in psychiatric practice. These barriers may be procedural (e.g., limitations in extrapolating results to the individual level), economic (e.g., perceived relatively elevated costs precluding insurance coverage), or related to clinicians' knowledge, attitudes, and practices (e.g., perceived unfavorable cost-effectiveness, insufficient understanding of probability statistics, and concerns regarding genetic counseling). Additionally, several ethical concerns may arise (e.g., increased stigma and discrimination through exclusion from health insurance). Here, we provide an overview of potential barriers for the implementation of genetic testing in psychiatry, as well as an in-depth discussion of strategies to address these challenges.
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23
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Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins ME, Dunn M, Tourneau CL, Mannikko M, Mollema T, Oliver D, Rietschel M, Reininghaus EZ, Squassina A, Valmaggia L, Kessing LV, Vieta E, Correll CU, Arango C, Andreassen OA. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol 2022; 63:17-34. [PMID: 36041245 DOI: 10.1016/j.euroneuro.2022.08.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
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Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | | | | | - Monica E Calkins
- Neurodevelopment and Psychosis Section and Lifespan Brain Institute of Penn/CHOP, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Michael Dunn
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore
| | - Christophe Le Tourneau
- Institut Curie, Department of Drug Development and Innovation (D3i), INSERM U900 Research unit, Paris-Saclay University, France
| | - Miia Mannikko
- European Federation of Associations of Families of People with Mental Illness (EUFAMI), Leuven, Belgium
| | - Tineke Mollema
- Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN), Brussels, Belgium
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
| | - Lucia Valmaggia
- South London and Maudsley NHS Foundation Trust, London, UK; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, KU Leuven, Belgium
| | - Lars Vedel Kessing
- Copenhagen Affective disorder Research Center (CADIC), Psychiatric Center Copenhagen, Denmark; Department of clinical Medicine, University of Copenhagen, Denmark
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA; Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Center for Psychiatric Neuroscience; The Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Gregorio Marañón; Health Research Institute (IiGSM), School of Medicine, Universidad Complutense de Madrid; Biomedical Research Center for Mental Health (CIBERSAM), Madrid, Spain
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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24
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Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5:293-320. [PMID: 35576555 PMCID: PMC9828290 DOI: 10.1146/annurev-biodatasci-111721-074830] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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25
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Harrison PJ, Mould A, Tunbridge EM. New drug targets in psychiatry: Neurobiological considerations in the genomics era. Neurosci Biobehav Rev 2022; 139:104763. [PMID: 35787892 DOI: 10.1016/j.neubiorev.2022.104763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 05/15/2022] [Accepted: 06/14/2022] [Indexed: 01/11/2023]
Abstract
After a period of withdrawal, pharmaceutical companies have begun to reinvest in neuropsychiatric disorders, due to improvements in our understanding of these disorders, stimulated in part by genomic studies. However, translating this information into disease insights and ultimately into tractable therapeutic targets is a major challenge. Here we consider how different sources of information might be integrated to guide this process. We review how an understanding of neurobiology has been used to advance therapeutic candidates identified in the pre-genomic era, using catechol-O-methyltransferase (COMT) as an exemplar. We then contrast with ZNF804A, the first genome-wide significant schizophrenia gene, and draw on some of the lessons that these and other examples provide. We highlight that, at least in the short term, the translation of potential targets for which there is orthogonal neurobiological support is likely to be more straightforward and productive than that those relying solely on genomic information. Although we focus here on information from genomic studies of schizophrenia, the points are broadly applicable across major psychiatric disorders and their symptoms.
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Affiliation(s)
- Paul J Harrison
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Arne Mould
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Elizabeth M Tunbridge
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK.
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26
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Reichenberg A, Akbarian S. Towards DSM 10: A bio-classification of developmental schizophrenia? Schizophr Res 2022; 242:4-6. [PMID: 34991947 PMCID: PMC8923980 DOI: 10.1016/j.schres.2021.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
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27
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Liddle PF, Liddle EB. Imprecise Predictive Coding Is at the Core of Classical Schizophrenia. Front Hum Neurosci 2022; 16:818711. [PMID: 35308615 PMCID: PMC8928728 DOI: 10.3389/fnhum.2022.818711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/14/2022] [Indexed: 12/23/2022] Open
Abstract
Current diagnostic criteria for schizophrenia place emphasis on delusions and hallucinations, whereas the classical descriptions of schizophrenia by Kraepelin and Bleuler emphasized disorganization and impoverishment of mental activity. Despite the availability of antipsychotic medication for treating delusions and hallucinations, many patients continue to experience persisting disability. Improving treatment requires a better understanding of the processes leading to persisting disability. We recently introduced the term classical schizophrenia to describe cases with disorganized and impoverished mental activity, cognitive impairment and predisposition to persisting disability. Recent evidence reveals that a polygenic score indicating risk for schizophrenia predicts severity of the features of classical schizophrenia: disorganization, and to a lesser extent, impoverishment of mental activity and cognitive impairment. Current understanding of brain function attributes a cardinal role to predictive coding: the process of generating models of the world that are successively updated in light of confirmation or contradiction by subsequent sensory information. It has been proposed that abnormalities of these predictive processes account for delusions and hallucinations. Here we examine the evidence provided by electrophysiology and fMRI indicating that imprecise predictive coding is the core pathological process in classical schizophrenia, accounting for disorganization, psychomotor poverty and cognitive impairment. Functional imaging reveals aberrant brain activity at network hubs engaged during encoding of predictions. We discuss the possibility that frequent prediction errors might promote excess release of the neurotransmitter, dopamine, thereby accounting for the occurrence of episodes of florid psychotic symptoms including delusions and hallucinations in classical schizophrenia. While the predictive coding hypotheses partially accounts for the time-course of classical schizophrenia, the overall body of evidence indicates that environmental factors also contribute. We discuss the evidence that chronic inflammation is a mechanism that might link diverse genetic and environmental etiological factors, and contribute to the proposed imprecision of predictive coding.
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Affiliation(s)
- Peter F. Liddle
- Centre for Translational Neuroimaging for Mental Health, School of Medicine, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
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28
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Cross B, Turner R, Pirmohamed M. Polygenic risk scores: An overview from bench to bedside for personalised medicine. Front Genet 2022; 13:1000667. [PMID: 36437929 PMCID: PMC9692112 DOI: 10.3389/fgene.2022.1000667] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.
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Affiliation(s)
- Benjamin Cross
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Richard Turner
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
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29
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Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease. Genes (Basel) 2021; 12:genes12121859. [PMID: 34946808 PMCID: PMC8700849 DOI: 10.3390/genes12121859] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/21/2021] [Indexed: 12/12/2022] Open
Abstract
Idiopathic Parkinson’s disease (PD) is a complex multifactorial disorder caused by the interplay of both genetic and non-genetic risk factors. Polygenic risk scores (PRSs) are one way to aggregate the effects of a large number of genetic variants upon the risk for a disease like PD in a single quantity. However, reassessment of the performance of a given PRS in independent data sets is a precondition for establishing the PRS as a valid tool to this end. We studied a previously proposed PRS for PD in a separate genetic data set, comprising 1914 PD cases and 4464 controls, and were able to replicate its ability to differentiate between cases and controls. We also assessed theoretically the prognostic value of the PD-PRS, i.e., its ability to predict the development of PD in later life for healthy individuals. As it turned out, the PD-PRS alone can be expected to perform poorly in this regard. Therefore, we conclude that the PD-PRS could serve as an important research tool, but that meaningful PRS-based prognosis of PD at an individual level is not feasible.
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