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Al-Soufi L, Arana ÁJ, Facal F, Flórez G, Vázquez FL, Arrojo M, Sánchez L, Costas J. Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111136. [PMID: 39237023 DOI: 10.1016/j.pnpbp.2024.111136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 09/02/2024] [Indexed: 09/07/2024]
Abstract
Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.
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Affiliation(s)
- Laila Al-Soufi
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Psychiatric Genetics Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain; Department of Zoology, Genetics and Physical Anthropology, Faculty of Veterinary, University of Santiago de Compostela, Lugo, Spain
| | - Álvaro J Arana
- Department of Zoology, Genetics and Physical Anthropology, Faculty of Veterinary, University of Santiago de Compostela, Lugo, Spain
| | - Fernando Facal
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Psychiatric Genetics Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain; Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Gerardo Flórez
- Addictive Treatment Unit, Ourense University Hospital, Ourense, Galicia, Spain; Centre for Biomedical Research in the Mental Health Network (CIBERSAM), Oviedo, Spain
| | - Fernando L Vázquez
- Department of Clinical Psychology and Psychobiology, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Manuel Arrojo
- Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Laura Sánchez
- Department of Zoology, Genetics and Physical Anthropology, Faculty of Veterinary, University of Santiago de Compostela, Lugo, Spain
| | - Javier Costas
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Psychiatric Genetics Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain; Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain.
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Petrovska J, Coynel D, Freytag V, de Quervain DJF, Papassotiropoulos A. Polygenic susceptibility for multiple sclerosis is associated with working memory in low-performing young adults. J Neurol Sci 2024; 463:123138. [PMID: 39059048 DOI: 10.1016/j.jns.2024.123138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND Multiple sclerosis (MS) is a complex disease with substantial heritability estimates. Besides typical clinical manifestations such as motor and sensory deficits, MS is characterized by structural and functional brain abnormalities, and by cognitive impairment such as decreased working memory (WM) performance. OBJECTIVES We investigated the possible link between the polygenic risk for MS and WM performance in healthy adults (18-35 years). Additionally, we addressed the relationship between polygenic risk for MS and white matter fractional anisotropy (FA). METHODS We generated a polygenic risk score (PRS) of MS susceptibility and investigated its association with WM performance in 3282 healthy adults (two subsamples, N1 = 1803, N2 = 1479). The association between MS-PRS and FA was studied in the second subsample. MS severity PRS associations were also investigated for the WM and FA measurements. RESULTS MS-PRS was significantly associated with WM performance within the 10% lowest WM-performing individuals (p = 0.001; pFDR = 0.018). It was not significantly associated with any of the investigated FA measurements. MS severity PRS was significantly associated with brain-wide mean FA (p = 0.041) and showed suggestive associations with additional FA measurements. CONCLUSIONS By identifying a genetic link between MS and WM performance this study contributes to the understanding of the genetic complexity of MS, and hopefully to the possible identification of molecular pathways linked to cognitive deficits in MS. It also contributes to the understanding of genetic associations with MS severity, as these associations seem to involve distinct biological pathways compared to genetic variants linked to the overall risk of developing MS.
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Affiliation(s)
- J Petrovska
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland.
| | - D Coynel
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland
| | - V Freytag
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland
| | - D J-F de Quervain
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
| | - A Papassotiropoulos
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
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3
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Senkevich K, Liu L, Alvarado CX, Leonard HL, Nalls MA, Gan-Or Z. Lack of genetic evidence for NLRP3 inflammasome involvement in Parkinson's disease pathogenesis. NPJ Parkinsons Dis 2024; 10:145. [PMID: 39103393 DOI: 10.1038/s41531-024-00744-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 06/26/2024] [Indexed: 08/07/2024] Open
Abstract
Activation of the NLRP3 inflammasome has been implicated in Parkinson's disease (PD) based on in vitro and in vivo studies. Clinical trials targeting the NLRP3 inflammasome in PD are ongoing. However, the evidence supporting NLRP3's involvement in PD from human genetics data is limited. We analyzed common and rare variants in NLRP3 inflammasome-related genes in PD cohorts, performed pathway-specific polygenic risk score (PRS) analyses, and studied causal associations using Mendelian randomization (MR) with the NLRP3 components and the cytokines IL-1β and IL-18. Our findings showed no associations of common or rare variants, nor of the pathway PRS with PD. MR suggests that altering the expression of the NLRP3 inflammasome, IL-1β, or IL-18, does not affect PD risk or progression. Therefore, our results do not support a role for the NLRP3 inflammasome in PD pathogenesis or as a target for drug development.
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Affiliation(s)
- Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, QC, Canada
- Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada
| | - Lang Liu
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, QC, Canada
- Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Chelsea X Alvarado
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20814, USA
- Data Tecnica, Washington, DC, 200373, USA
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20814, USA
- Data Tecnica, Washington, DC, 200373, USA
- DZNE Tübingen, Tübingen, Germany
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20814, USA
- Data Tecnica, Washington, DC, 200373, USA
| | - Ziv Gan-Or
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, QC, Canada.
- Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
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4
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Jansen PR, Vos N, van Uhm J, Dekkers IA, van der Meer R, Mannens MMAM, van Haelst MM. The utility of obesity polygenic risk scores from research to clinical practice: A review. Obes Rev 2024:e13810. [PMID: 39075585 DOI: 10.1111/obr.13810] [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] [Received: 10/01/2023] [Revised: 06/13/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024]
Abstract
Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.
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Affiliation(s)
- Philip R Jansen
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Niels Vos
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Jorrit van Uhm
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rieneke van der Meer
- Netherlands Obesity Clinic, Huis ter Heide, Netherlands
- Amsterdam UMC, Department of Endocrinology and Metabolism, University of Amsterdam, Amsterdam, Netherlands
| | - Marcel M A M Mannens
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Mieke M van Haelst
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Amsterdam UMC, Emma Center for Personalized Medicine, University of Amsterdam, Amsterdam, Netherlands
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5
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Lorenzini L, Collij LE, Tesi N, Vilor-Tejedor N, Ingala S, Blennow K, Foley C, Frisoni GB, Haller S, Holstege H, van der van der Lee S, Martinez-Lage P, Marioni RE, McCartney DL, O' Brien J, Oliveira TG, Payoux P, Reinders M, Ritchie C, Scheltens P, Schwarz AJ, Sudre CH, Waldman AD, Wolz R, Chatelat G, Ewers M, Wink AM, Mutsaerts HJMM, Gispert JD, Visser PJ, Tijms BM, Altmann A, Barkhof F. Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals. Alzheimers Dement 2024. [PMID: 39073684 DOI: 10.1002/alz.14096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/20/2024] [Accepted: 06/03/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.
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Affiliation(s)
- Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Niccoló Tesi
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Natàlia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Cerebriu A/S, Copenhagen, Denmark
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - Sven Haller
- CIMC - Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
| | - Henne Holstege
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sven van der van der Lee
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pablo Martinez-Lage
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - John O' Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- ToNIC, Toulouse NeuroImaging Center, University of Toulouse, Inserm, Toulouse, France
| | - Marcel Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Brain Health Scotland, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adam J Schwarz
- Takeda Pharmaceuticals Ltd., Cambridge, Massachusetts, USA
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Department of Medicine, Imperial College London, London, UK
| | | | - Gael Chatelat
- Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", institut Blood-and-Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Henk J M M Mutsaerts
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, Department of Psychiatry & Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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Li X, Fernandes BS, Liu A, Lu Y, Chen J, Zhao Z, Dai Y. GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.19.23291621. [PMID: 37425929 PMCID: PMC10327215 DOI: 10.1101/2023.06.19.23291621] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease. We hypothesized that unknown counterfactors might be involved in reversing the PRS prediction, which might provide new insights into the pathogenesis, prevention, and early intervention of diseases. Methods We built a novel computational framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery (disc) and the replication (rep) datasets include 2722 and 2854 individuals, respectively. First, we calculated the optimized PRS model based on the three recent AD GWAS summary statistics for each cohort. Then, we stratified the individuals by their PRS and clinical diagnosis into six biologically meaningful PRS strata, such as AD cases with low/high risk and cognitively normal (CN) with low/high risk. Lastly, we imputed individual genetically-regulated expression (GReX) and identified differential GReX and GRPas between risk strata using gene-set enrichment and variational analyses in two models, with and without APOE effects. An orthogonality test was further conducted to verify those GRPas are independent of PRS risk. To verify the generalizability of other polygenic diseases, we further applied a default model of GRPa-PRS for schizophrenia (SCZ). Results For each stratum, we conducted the same procedures in both the disc and rep datasets for comparison. In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocyte response to oxidative stress. Additionally, we discovered resilience-related GRPs that are orthogonal to AD PRS, such as the calcium signaling pathway and divalent inorganic cation homeostasis. In SCZ, pathways related to mitochondrial function and muscle development were highlighted. Finally, our GRPa-PRS method identified more consistent differential pathways compared to another variant-based pathway PRS method. Conclusions We developed a framework, GRPa-PRS, to systematically explore the differential GReX and GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those strata unveiled new insights into the pathways associated with disease risk and resilience. Our framework is extendable to other polygenic complex diseases.
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Affiliation(s)
- Xiaoyang Li
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brisa S. Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yimei Lu
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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7
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Trastulla L, Dolgalev G, Moser S, Jiménez-Barrón LT, Andlauer TFM, von Scheidt M, Budde M, Heilbronner U, Papiol S, Teumer A, Homuth G, Völzke H, Dörr M, Falkai P, Schulze TG, Gagneur J, Iorio F, Müller-Myhsok B, Schunkert H, Ziller MJ. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. Nat Commun 2024; 15:5534. [PMID: 38951512 PMCID: PMC11217418 DOI: 10.1038/s41467-024-49338-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.
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Affiliation(s)
- Lucia Trastulla
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- Human Technopole, Milan, Italy
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Georgii Dolgalev
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Sylvain Moser
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Laura T Jiménez-Barrón
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Sergi Papiol
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Alexander Teumer
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Peter Falkai
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Heribert Schunkert
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael J Ziller
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Psychiatry, University of Münster, Münster, Germany.
- Center for Soft Nanoscience, University of Münster, Münster, Germany.
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8
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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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9
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD, Sofer T. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 2024; 14:12436. [PMID: 38816422 PMCID: PMC11139858 DOI: 10.1038/s41598-024-62945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, West Roxbury, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Myriam Fornage
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O'Connor
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Health Equity and Population Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Center for Life Sciences CLS-934, 3 Blackfan St., Boston, MA, 02115, USA.
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10
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Senkevich K, Liu L, Alvarado CX, Leonard HL, Nalls MA, Gan-Or Z. Lack of genetic evidence for NLRP3-inflammasome involvement in Parkinson's disease pathogenesis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.20.23295790. [PMID: 37886468 PMCID: PMC10602039 DOI: 10.1101/2023.09.20.23295790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Activation of the NLRP3-inflammasome has been implicated in Parkinson's disease based on in vitro and in vivo studies. Clinical trials targeting the NLRP3-inflammasome in Parkinson's disease are ongoing. However, the evidence supporting NLRP3's involvement in Parkinson's disease from human genetics data is limited. In this study, we conducted analyses of common and rare variants in NLRP3-inflammasome related genes in Parkinson's disease cohorts. We performed pathway-specific analyses using polygenic risk scores and studied potential causal associations using Mendelian randomization with the NLRP3 components and the cytokines IL-1β and IL-18. Our findings showed no associations of common or rare variants, nor of the pathway polygenic risk score with Parkinson's disease. Mendelian randomization suggests that altering the expression of the NLRP3-inflammasome, IL-1β or IL-18, does not affect Parkinson's disease risk or progression. Therefore, our results do not support a role for the NLRP3-inflammasome in Parkinson's disease pathogenesis or as a target for drug development.
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11
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Sportelli L, Eisenberg DP, Passiatore R, D'Ambrosio E, Antonucci LA, Bettina JS, Chen Q, Goldman AL, Gregory MD, Griffiths K, Hyde TM, Kleinman JE, Pardiñas AF, Parihar M, Popolizio T, Rampino A, Shin JH, Veronese M, Ulrich WS, Zink CF, Bertolino A, Howes OD, Berman KF, Weinberger DR, Pergola G. Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo. Nat Commun 2024; 15:3342. [PMID: 38688917 PMCID: PMC11061310 DOI: 10.1038/s41467-024-47456-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 03/22/2024] [Indexed: 05/02/2024] Open
Abstract
The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.
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Affiliation(s)
- Leonardo Sportelli
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Daniel P Eisenberg
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Roberta Passiatore
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Enrico D'Ambrosio
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Linda A Antonucci
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Jasmine S Bettina
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Aaron L Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Michael D Gregory
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Kira Griffiths
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
- Holmusk Technologies, New York, NY, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Teresa Popolizio
- Radiology Department, IRCCS Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Mattia Veronese
- Department of Information Engineering, University of Padua, Padua, Italy
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - William S Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Caroline F Zink
- Baltimore Research and Education Foundation, Baltimore, MD, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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12
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Warren TL, Tubbs JD, Lesh TA, Corona MB, Pakzad SS, Albuquerque MD, Singh P, Zarubin V, Morse SJ, Sham PC, Carter CS, Nord AS. Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02457-0. [PMID: 38491343 DOI: 10.1038/s41380-024-02457-0] [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: 04/12/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/18/2024]
Abstract
A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 205 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.
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Affiliation(s)
- Tracy L Warren
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Justin D Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Mylena B Corona
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
| | - Sarvenaz S Pakzad
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Marina D Albuquerque
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Praveena Singh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Vanessa Zarubin
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Sarah J Morse
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
- Center for Neuroscience, University of California, Davis, CA, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
| | - Cameron S Carter
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA.
| | - Alex S Nord
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.
- Center for Neuroscience, University of California, Davis, CA, USA.
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13
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Lanore A, Basset A, Lesage S. A machine learning approach for gene prioritization in Parkinson's disease. Brain 2024; 147:743-745. [PMID: 38428999 DOI: 10.1093/brain/awae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
This scientific commentary refers to ‘Machine learning nominates the inositol pathway and novel genes in Parkinson’s disease’ by Yu et al. (https://doi.org/10.1093/brain/awad345).
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Affiliation(s)
- Aymeric Lanore
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
- Assistance Publique Hôpitaux de Paris, Département de Neurologie, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Aymeric Basset
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
| | - Suzanne Lesage
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
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14
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Yu E, Larivière R, Thomas RA, Liu L, Senkevich K, Rahayel S, Trempe JF, Fon EA, Gan-Or Z. Machine learning nominates the inositol pathway and novel genes in Parkinson's disease. Brain 2024; 147:887-899. [PMID: 37804111 PMCID: PMC10907089 DOI: 10.1093/brain/awad345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.
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Affiliation(s)
- Eric Yu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Roxanne Larivière
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Rhalena A Thomas
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Lang Liu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Shady Rahayel
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec H4J 1C5, Canada
- Department of Medicine, University of Montreal, Montreal, Quebec H3C 3J7, Canada
| | - Jean-François Trempe
- Department of Pharmacology and Therapeutics and Centre de Recherche en Biologie Structurale, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Edward A Fon
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
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15
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Zhuang Y, Kim NY, Fritsche LG, Mukherjee B, Lee S. Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction. BMC Bioinformatics 2024; 25:65. [PMID: 38336614 PMCID: PMC11323637 DOI: 10.1186/s12859-024-05664-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. RESULTS We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. CONCLUSIONS By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .
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Affiliation(s)
| | - Na Yeon Kim
- Seoul National University, Seoul, Republic of Korea
| | | | | | - Seunggeun Lee
- Seoul National University, Seoul, Republic of Korea.
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16
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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17
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Liu A, Fernandes BS, Citu C, Zhao Z. Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association. Alzheimers Res Ther 2024; 16:3. [PMID: 38167548 PMCID: PMC10762817 DOI: 10.1186/s13195-023-01372-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. METHODS We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. RESULTS We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. CONCLUSIONS Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.
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Affiliation(s)
- Andi Liu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA
| | - Brisa S Fernandes
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA
| | - Citu Citu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA
| | - Zhongming Zhao
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA.
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
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18
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Landoulsi Z, Pachchek S, Bobbili DR, Pavelka L, May P, Krüger R. Genetic landscape of Parkinson's disease and related diseases in Luxembourg. Front Aging Neurosci 2023; 15:1282174. [PMID: 38173558 PMCID: PMC10761438 DOI: 10.3389/fnagi.2023.1282174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Objectives To explore the genetic architecture of PD in the Luxembourg Parkinson's Study including cohorts of healthy people and patients with Parkinson's disease (PD) and atypical parkinsonism (AP). Methods 809 healthy controls, 680 PD and 103 AP were genotyped using the Neurochip array. We screened and validated rare single nucleotide variants (SNVs) and copy number variants (CNVs) within seven PD-causing genes (LRRK2, SNCA, VPS35, PRKN, PARK7, PINK1 and ATP13A2). Polygenic risk scores (PRSs) were generated using the latest genome-wide association study for PD. We then estimated the role of common variants in PD risk by applying gene-set-specific PRSs. Results We identified 60 rare SNVs in seven PD-causing genes, nine of which were pathogenic in LRRK2, PINK1 and PRKN. Eleven rare CNVs were detected in PRKN including seven duplications and four deletions. The majority of PRKN SNVs and CNVs carriers were heterozygous and not differentially distributed between cases and controls. The PRSs were significantly associated with PD and identified specific molecular pathways related to protein metabolism and signal transduction as drivers of PD risk. Conclusion We performed a comprehensive genetic characterization of the deep-phenotyped individuals of the Luxembourgish Parkinson's Study. Heterozygous SNVs and CNVs in PRKN were not associated with higher PD risk. In particular, we reported novel digenic variants in PD related genes and rare LRRK2 SNVs in AP patients. Our findings will help future studies to unravel the genetic complexity of PD.
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Affiliation(s)
- Zied Landoulsi
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sinthuja Pachchek
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dheeraj Reddy Bobbili
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lukas Pavelka
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Patrick May
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rejko Krüger
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
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19
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJ, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Der Ida Chen Y, Sofer T. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299909. [PMID: 38168328 PMCID: PMC10760279 DOI: 10.1101/2023.12.13.23299909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D. Mitchel
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M. Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C. Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, West Roxbury, MA, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ramon Casanova
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark, DK
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O’Connor
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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20
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Meloto CB. Genetics of Postsurgical Pain: Where Do We Go from Here? Anesthesiology 2023; 139:726-729. [PMID: 37934109 DOI: 10.1097/aln.0000000000004780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Affiliation(s)
- Carolina B Meloto
- Faculty of Dental Medicine and Oral Health Sciences and the Alan Edwards Centre for Research on Pain, McGill University, Montréal, Québec, Canada
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21
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Yang HS, Teng L, Kang D, Menon V, Ge T, Finucane HK, Schultz AP, Properzi M, Klein HU, Chibnik LB, Schneider JA, Bennett DA, Hohman TJ, Mayeux RP, Johnson KA, De Jager PL, Sperling RA. Cell-type-specific Alzheimer's disease polygenic risk scores are associated with distinct disease processes in Alzheimer's disease. Nat Commun 2023; 14:7659. [PMID: 38036535 PMCID: PMC10689816 DOI: 10.1038/s41467-023-43132-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Many of the Alzheimer's disease (AD) risk genes are specifically expressed in microglia and astrocytes, but how and when the genetic risk localizing to these cell types contributes to AD pathophysiology remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets and uncover the impact of cell-type-specific genetic risk on AD endophenotypes. In an autopsy dataset spanning all stages of AD (n = 1457), the astrocytic ADPRS affected diffuse and neuritic plaques (amyloid-β), while microglial ADPRS affected neuritic plaques, microglial activation, neurofibrillary tangles (tau), and cognitive decline. In an independent neuroimaging dataset of cognitively unimpaired elderly (n = 2921), astrocytic ADPRS was associated with amyloid-β, and microglial ADPRS was associated with amyloid-β and tau, connecting cell-type-specific genetic risk with AD pathology even before symptom onset. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.
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Affiliation(s)
- Hyun-Sik Yang
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ling Teng
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel Kang
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology and the Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Tian Ge
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Hilary K Finucane
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Properzi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Hans-Ulrich Klein
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology and the Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Lori B Chibnik
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard P Mayeux
- Department of Neurology and the Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Keith A Johnson
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology and the Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Reisa A Sperling
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Wang B, Irizar H, Thygesen JH, Zartaloudi E, Austin-Zimmerman I, Bhat A, Harju-Seppänen J, Pain O, Bass N, Gkofa V, Alizadeh BZ, van Amelsvoort T, Arranz MJ, Bender S, Cahn W, Stella Calafato M, Crespo-Facorro B, Di Forti M, Giegling I, de Haan L, Hall J, Hall MH, van Haren N, Iyegbe C, Kahn RS, Kravariti E, Lawrie SM, Lin K, Luykx JJ, Mata I, McDonald C, McIntosh AM, Murray RM, Picchioni M, Powell J, Prata DP, Rujescu D, Rutten BPF, Shaikh M, Simons CJP, Toulopoulou T, Weisbrod M, van Winkel R, Kuchenbaecker K, McQuillin A, Bramon E. Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis. Schizophr Bull 2023; 49:1625-1636. [PMID: 37582581 PMCID: PMC10686343 DOI: 10.1093/schbul/sbad088] [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] [Indexed: 08/17/2023]
Abstract
BACKGROUND AND HYPOTHESIS Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.
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Affiliation(s)
- Baihan Wang
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Haritz Irizar
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Johan H Thygesen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Eirini Zartaloudi
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Isabelle Austin-Zimmerman
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Anjali Bhat
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Jasmine Harju-Seppänen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Nick Bass
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Vasiliki Gkofa
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Behrooz Z Alizadeh
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Maria J Arranz
- Fundació Docència i Recerca Mutua Terrassa, Terrassa, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Institut de Recerca Biomédica Sant Pau (IIB-Sant Pau), Barcelona, Spain
| | - Stephan Bender
- Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wiepke Cahn
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Altrecht, General Mental Health Care, Utrecht, The Netherlands
| | - Maria Stella Calafato
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Sevilla, Spain
- Department of Psychiatry, University Hospital Virgen del Rocio, School of Medicine, University of Sevilla–IBiS, Sevilla, Spain
| | - Marta Di Forti
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Ina Giegling
- Comprehensive Centers for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Arkin, Institute for Mental Health, Amsterdam, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Innovation Institute, School of Medicine, Cardiff University, Hadyn Ellis Building, Mandy Road, Cardiff, UK
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, Harvard Medical School, McLean Hospital, Belmont, MA, USA
| | - Neeltje van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia’s Children Hospital, Rotterdam, The Netherlands
| | - Conrad Iyegbe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eugenia Kravariti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jurjen J Luykx
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ignacio Mata
- Fundacion Argibide, Pamplona, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Robin M Murray
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Marco Picchioni
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- St Magnus Hospital, Surrey, UK
| | - John Powell
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Diana P Prata
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciencias da Universidade de Lisboa, Portugal
| | - Dan Rujescu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Division of General Psychiatry, Medical University of Vienna, Austria
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Madiha Shaikh
- North East London Foundation Trust, London, UK
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Claudia J P Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- GGzE Institute for Mental Health Care, Eindhoven, The Netherlands
| | - Timothea Toulopoulou
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Türkiye
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Türkiye
- Department of Psychology, Bilkent University, Ankara, Türkiye
- School of Medicine, Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry and Behavioral Health System, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Matthias Weisbrod
- Department of General Psychiatry, Center of Psychosocial Medicine, University of Heidelberg, Germany
- SRH Klinikum, Karlsbad-Langensteinbach, Germany
| | - Ruud van Winkel
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- KU Leuven, Department of Neuroscience, Research Group Psychiatry, Leuven, Belgium
| | - Karoline Kuchenbaecker
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, Division of Biosciences, University College London, London, UK
| | - Andrew McQuillin
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Elvira Bramon
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
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23
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Griffiths K, Smart SE, Barker GJ, Deakin B, Lawrie SM, Lewis S, Lythgoe DJ, Pardiñas AF, Singh K, Semple S, Walters JTR, Williams SR, Egerton A, MacCabe JH. Treatment resistance NMDA receptor pathway polygenic score is associated with brain glutamate in schizophrenia. Schizophr Res 2023; 260:152-159. [PMID: 37657282 PMCID: PMC10873209 DOI: 10.1016/j.schres.2023.08.020] [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: 12/05/2022] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/03/2023]
Abstract
Dysfunction of glutamate neurotransmission has been implicated in the pathophysiology of schizophrenia and may be particularly relevant in severe, treatment-resistant symptoms. The underlying mechanism may involve hypofunction of the NMDA receptor. We investigated whether schizophrenia-related pathway polygenic scores, composed of genetic variants within NMDA receptor encoding genes, are associated with cortical glutamate in schizophrenia. Anterior cingulate cortex (ACC) glutamate was measured in 70 participants across 4 research sites using Proton Magnetic Resonance Spectroscopy (1H-MRS). Two NMDA receptor gene sets were sourced from the Molecular Signatories Database and NMDA receptor pathway polygenic scores were constructed using PRSet. The NMDA receptor pathway polygenic scores were weighted by single nucleotide polymorphism (SNP) associations with treatment-resistant schizophrenia, and associations with ACC glutamate were tested. We then tested whether NMDA receptor pathway polygenic scores with SNPs weighted by associations with non-treatment-resistant schizophrenia were associated with ACC glutamate. A higher NMDA receptor complex pathway polygenic score was significantly associated with lower ACC glutamate (β = -0.25, 95 % CI = -0.49, -0.02, competitive p = 0.03). When SNPs were weighted by associations with non-treatment-resistant schizophrenia, there was no association between the NMDA receptor complex pathway polygenic score and ACC glutamate (β = 0.05, 95 % CI = -0.18, 0.27, competitive p = 0.79). These results provide initial evidence of an association between common genetic variation implicated in NMDA receptor function and ACC glutamate levels in schizophrenia. This association was specific to when the NMDA receptor complex pathway polygenic score was weighted by SNP associations with treatment-resistant schizophrenia.
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Affiliation(s)
- Kira Griffiths
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Sophie E Smart
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Gareth J Barker
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK; Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Bill Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, M13 9PT, UK
| | | | - Shon Lewis
- Division of Psychology and Mental Health, University of Manchester, M13 9PT, UK; Greater Manchester Mental Health NHS Foundation Trust, Manchester M25 3BL, UK
| | - David J Lythgoe
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK; Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Krishna Singh
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff CF24 4HQ, UK
| | - Scott Semple
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Stephen R Williams
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Alice Egerton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - James H MacCabe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK.
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24
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Sportelli L, Eisenberg DP, Passiatore R, D'Ambrosio E, Antonucci LA, Chen Q, Czarapata J, Goldman AL, Gregory M, Griffiths K, Hyde TM, Kleinman JE, Pardiñas AF, Parihar M, Popolizio T, Rampino A, Shin JH, Veronese M, Ulrich WS, Zink CF, Bertolino A, Howes OD, Berman KF, Weinberger DR, Pergola G. Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558594. [PMID: 37786720 PMCID: PMC10541621 DOI: 10.1101/2023.09.20.558594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [ 18 F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a 'bench to bedside' translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.
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25
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Tubbs JD, Leung PB, Zhong Y, Zhan N, Hui TC, Ho KK, Hung KS, Cheung EF, So HC, Lui SS, Sham PC. Pathway-Specific Polygenic Scores Improve Cross-Ancestry Prediction of Psychosis and Clinical Outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.01.23294957. [PMID: 37790317 PMCID: PMC10543247 DOI: 10.1101/2023.09.01.23294957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Psychotic disorders are debilitating conditions with disproportionately high public health burden. Genetic studies indicate high heritability, but current polygenic scores (PGS) account for only a fraction of variance in psychosis risk. PGS often show poor portability across ancestries, performing significantly worse in non-European populations. Pathway-specific PGS (pPGS), which restrict PGS to genomic locations within distinct biological units, could lead to increased mechanistic understanding of pathways that lead to risk and improve cross-ancestry prediction by reducing noise in genetic predictors. This study examined the predictive power of genome-wide PGS and nine pathway-specific pPGS in a unique Chinese-ancestry sample of deeply-phenotyped psychosis patients and non-psychiatric controls. We found strong evidence for the involvement of schizophrenia-associated risk variants within "nervous system development" (p=2.5e-4) and "regulation of neuron differentiation" pathways (p=3.0e-4) in predicting risk for psychosis. We also found the "ion channel complex" pPGS, with weights derived from GWAS of bipolar disorder, to be strongly associated with the number of inpatient psychiatry admissions a patient experiences (p=1.5e-3) and account for a majority of the signal in the overall bipolar PGS. Importantly, although the schizophrenia genome-wide PGS alone explained only 3.7% of the variance in liability to psychosis in this Chinese ancestry sample, the addition of the schizophrenia-weighted pPGS for "nervous system development" and "regulation of neuron differentiation" increased the variance explained to 6.9%, which is on-par with the predictive power of PGS in European ancestry samples. Thus, not only can pPGS provide greater insight into mechanisms underlying genetic risk for disease and clinical outcomes, but may also improve cross-ancestry risk prediction accuracy.
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Affiliation(s)
- Justin D. Tubbs
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
| | - Perry B.M. Leung
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Yuanxin Zhong
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
| | - Na Zhan
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
| | - Tomy C.K. Hui
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
| | - Karen K.Y. Ho
- Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong SAR
| | - Karen S.Y. Hung
- Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong SAR
| | - Eric F.C. Cheung
- Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong SAR
| | - Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong SAR
| | - Simon S.Y. Lui
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
| | - Pak C. Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR
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26
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Liu A, Fernandes BS, Citu C, Zhao Z. Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association. RESEARCH SQUARE 2023:rs.3.rs-3335643. [PMID: 37790454 PMCID: PMC10543294 DOI: 10.21203/rs.3.rs-3335643/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Background Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. Methods We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. Results We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Conclusions Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.
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Affiliation(s)
- Andi Liu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston
| | - Brisa S Fernandes
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
| | - Citu Citu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
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27
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Chen J, Huang Y, Zhong C. Minimizing enzyme mass to decompose flux distribution for identifying biologically relevant elementary flux modes. Biosystems 2023; 231:104981. [PMID: 37442363 DOI: 10.1016/j.biosystems.2023.104981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/06/2023] [Accepted: 07/09/2023] [Indexed: 07/15/2023]
Abstract
The flux distribution in metabolic network can be decomposed as non-negative linear combinations of elementary flux modes (EFMs). Identifying biologically relevant EFM combination by decomposing flux distribution in metabolic network is a useful method to study metabolisms in systems biology. However, the occurrence of biologically irrelevant EFMs hinders the application of such methods. In this paper, we introduce a novel method for identifying EFM combination by minimizing enzyme mass. Our proposed method, called EMMD (Enzyme Mass Minimization Decomposition), takes into consideration both thermodynamic and enzymatic constraints in stoichiometry metabolic models. By implementing EMMD, we can decompose the flux distributions in metabolic network to detect biologically relevant EFM combinations. We demonstrate the effectiveness of our method by applying it to the core Escherichia coli metabolic network and show that the optimal EFM combinations identified by EMMD are unique. Moreover, the optimal EFM combination identified by EMMD not only aligns more closely with experimental values in terms of estimated growth rate, but it also demonstrates more favorable thermodynamics. Finally, we investigated the growth of the core Escherichia coli metabolic network in Luria-Bertani medium containing different carbon sources, revealing the impact of various carbon sources on the growth rate of flux distribution. EMMD thus could be a promising complement to the existing flux decomposition tools.
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Affiliation(s)
- Jingning Chen
- School of Computer and Electronics Information, Guangxi University, Nanning, 530004, China
| | - Yiran Huang
- School of Computer and Electronics Information, Guangxi University, Nanning, 530004, China; Guangxi Key Laboratory of Multimedia Communications Network Technology, Nanning, 530004, China.
| | - Cheng Zhong
- School of Computer and Electronics Information, Guangxi University, Nanning, 530004, China; Guangxi Key Laboratory of Multimedia Communications Network Technology, Nanning, 530004, China
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28
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Yang HS, Teng L, Kang D, Menon V, Ge T, Finucane HK, Schultz AP, Properzi M, Klein HU, Chibnik LB, Schneider JA, Bennett DA, Hohman TJ, Mayeux RP, Johnson KA, De Jager PL, Sperling RA. Cell-type-specific Alzheimer's disease polygenic risk scores are associated with distinct disease processes in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.01.23290850. [PMID: 37333223 PMCID: PMC10274993 DOI: 10.1101/2023.06.01.23290850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Alzheimer's disease (AD) heritability is enriched in glial genes, but how and when cell-type-specific genetic risk contributes to AD remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets. In an autopsy dataset spanning all stages of AD (n=1,457), astrocytic (Ast) ADPRS was associated with both diffuse and neuritic Aβ plaques, while microglial (Mic) ADPRS was associated with neuritic Aβ plaques, microglial activation, tau, and cognitive decline. Causal modeling analyses further clarified these relationships. In an independent neuroimaging dataset of cognitively unimpaired elderly (n=2,921), Ast-ADPRS were associated with Aβ, and Mic-ADPRS was associated with Aβ and tau, showing a consistent pattern with the autopsy dataset. Oligodendrocytic and excitatory neuronal ADPRSs were associated with tau, but only in the autopsy dataset including symptomatic AD cases. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.
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Affiliation(s)
- Hyun-Sik Yang
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Ling Teng
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Daniel Kang
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Vilas Menon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Tian Ge
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Hilary K. Finucane
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aaron P. Schultz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA
| | - Michael Properzi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Hans-Ulrich Klein
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lori B. Chibnik
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Timothy J. Hohman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard P. Mayeux
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Keith A. Johnson
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Philip L. De Jager
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Reisa A. Sperling
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA
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29
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Suarez-Pajes E, Tosco-Herrera E, Ramirez-Falcon M, Gonzalez-Barbuzano S, Hernandez-Beeftink T, Guillen-Guio B, Villar J, Flores C. Genetic Determinants of the Acute Respiratory Distress Syndrome. J Clin Med 2023; 12:3713. [PMID: 37297908 PMCID: PMC10253474 DOI: 10.3390/jcm12113713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/18/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.
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Affiliation(s)
- Eva Suarez-Pajes
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Eva Tosco-Herrera
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Melody Ramirez-Falcon
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Silvia Gonzalez-Barbuzano
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Tamara Hernandez-Beeftink
- Department of Population Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Beatriz Guillen-Guio
- Department of Population Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Jesús Villar
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Research Unit, Hospital Universitario de Gran Canaria Dr. Negrín, 35019 Las Palmas de Gran Canaria, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain
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30
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Trastulla L, Moser S, Jiménez-Barrón LT, Andlauer TF, von Scheidt M, Budde M, Heilbronner U, Papiol S, Teumer A, Homuth G, Falkai P, Völzke H, Dörr M, Schulze TG, Gagneur J, Iorio F, Müller-Myhsok B, Schunkert H, Ziller MJ. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.10.23289788. [PMID: 37214898 PMCID: PMC10197798 DOI: 10.1101/2023.05.10.23289788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.
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Affiliation(s)
- Lucia Trastulla
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- Human Technopole, Milan, Italy
| | - Sylvain Moser
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Laura T. Jiménez-Barrón
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | | | - Moritz von Scheidt
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | | | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Alexander Teumer
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich 80336, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Heribert Schunkert
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael J. Ziller
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry, University of Münster, Münster, Germany
- Center for Soft Nanoscience, University of Münster, Münster, Germany
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31
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Örd T, Lönnberg T, Nurminen V, Ravindran A, Niskanen H, Kiema M, Õunap K, Maria M, Moreau PR, Mishra PP, Palani S, Virta J, Liljenbäck H, Aavik E, Roivainen A, Ylä-Herttuala S, Laakkonen JP, Lehtimäki T, Kaikkonen MU. Dissecting the polygenic basis of atherosclerosis via disease-associated cell state signatures. Am J Hum Genet 2023; 110:722-740. [PMID: 37060905 PMCID: PMC10183377 DOI: 10.1016/j.ajhg.2023.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/21/2023] [Indexed: 04/17/2023] Open
Abstract
Coronary artery disease (CAD) is a pandemic disease where up to half of the risk is explained by genetic factors. Advanced insights into the genetic basis of CAD require deeper understanding of the contributions of different cell types, molecular pathways, and genes to disease heritability. Here, we investigate the biological diversity of atherosclerosis-associated cell states and interrogate their contribution to the genetic risk of CAD by using single-cell and bulk RNA sequencing (RNA-seq) of mouse and human lesions. We identified 12 disease-associated cell states that we characterized further by gene set functional profiling, ligand-receptor prediction, and transcription factor inference. Importantly, Vcam1+ smooth muscle cell state genes contributed most to SNP-based heritability of CAD. In line with this, genetic variants near smooth muscle cell state genes and regulatory elements explained the largest fraction of CAD-risk variance between individuals. Using this information for variant prioritization, we derived a hybrid polygenic risk score (PRS) that demonstrated improved performance over a classical PRS. Our results provide insights into the biological mechanisms associated with CAD risk, which could make a promising contribution to precision medicine and tailored therapeutic interventions in the future.
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Affiliation(s)
- Tiit Örd
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku
| | - Valtteri Nurminen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Aarthi Ravindran
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Henri Niskanen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Miika Kiema
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kadri Õunap
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Maleeha Maria
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pierre R Moreau
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Senthil Palani
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Jenni Virta
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Heidi Liljenbäck
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland; Turku Center for Disease Modeling, University of Turku, 20520 Turku, Finland
| | - Einari Aavik
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Anne Roivainen
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland; Turku Center for Disease Modeling, University of Turku, 20520 Turku, Finland; Turku PET Centre, Turku University Hospital, 20520 Turku, Finland
| | - Seppo Ylä-Herttuala
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Johanna P Laakkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Minna U Kaikkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
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32
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Deans PJM, Seah C, Johnson J, Gonzalez JG, Townsley K, Cao E, Schrode N, Stahl E, O’Reilly P, Huckins LM, Brennand KJ. Non-additive effects of schizophrenia risk genes reflect convergent downstream function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.20.23287497. [PMID: 36993466 PMCID: PMC10055596 DOI: 10.1101/2023.03.20.23287497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.
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Affiliation(s)
- PJ Michael Deans
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Judit Garcia Gonzalez
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kayla Townsley
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Evan Cao
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Nadine Schrode
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Eli Stahl
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Paul O’Reilly
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kristen J. Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
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