1
|
Wu Q, Dai J, Liu J, Wu L. Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations. Curr Osteoporos Rep 2025; 23:24. [PMID: 40411668 PMCID: PMC12103327 DOI: 10.1007/s11914-025-00917-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2025] [Indexed: 05/26/2025]
Abstract
PURPOSE OF REVIEW Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research. RECENT FINDINGS European-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery. Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.
Collapse
Affiliation(s)
- Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
| | - Jingyuan Dai
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
| | - Jianing Liu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
| | - Lang Wu
- Pacific Center for Genome Research, University of Hawai'i at Mānoa, Honolulu, HI, USA
- Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI, USA
| |
Collapse
|
2
|
Koprulu M, Wheeler E, Kerrison ND, Denaxas S, Carrasco-Zanini J, Orkin CM, Hemingway H, Wareham NJ, Pietzner M, Langenberg C. Sex differences in the genetic regulation of the human plasma proteome. Nat Commun 2025; 16:4001. [PMID: 40360480 PMCID: PMC12075630 DOI: 10.1038/s41467-025-59034-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 04/07/2025] [Indexed: 05/15/2025] Open
Abstract
Mechanisms underlying sex differences in the development and prognosis of many diseases remain largely elusive. Here, we systematically investigated sex differences in the genetic regulation of plasma proteome (>5800 protein targets) across two cohorts (30,307 females; 26,058 males). Plasma levels of two-thirds of protein targets differ significantly by sex. In contrast, genetic effects on protein targets are remarkably similar across sexes, with only 103 sex-differential protein quantitative loci (sd-pQTLs; for 2.9% and 0.3% of protein targets from antibody- and aptamer-based platforms, respectively). A third of those show evidence of sexual discordance, i.e., effects observed in one sex only (n = 30) or opposite effect directions (n = 1 for CDH15). Phenome-wide analyses of 365 outcomes in UK Biobank did not provide evidence that the identified sd-pQTLs accounted for sex-differential disease risk. Our results demonstrate similarities in the genetic regulation of protein levels by sex with important implications for genetically-guided drug target discovery and validation.
Collapse
Grants
- MC_UU_00006/1 RCUK | Medical Research Council (MRC)
- MC_PC_13046 RCUK | Medical Research Council (MRC)
- MC_UU_00006/1 RCUK | Medical Research Council (MRC)
- SP/19/3/34678 British Heart Foundation (BHF)
- The Fenland Study (DOI 10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046). This work is supported by the Medical Research Council (MC_UU_00006/1 - Etiology and Mechanisms) (C.L., E.W., M.P., N.K., and N.J.W.). M.K. is supported by Gates Cambridge Trust. H.H. is supported by Health Data Research UK and the NIHR University College London Hospitals Biomedical Research Centre. S.D. is supported by a) the BHF Data Science Centre led by HDR UK (grant SP/19/3/34678), b) BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement 116074, c) the NIHR Biomedical Research Centre at University College London Hospital NHS Trust (UCLH BRC), d) a BHF Accelerator Award (AA/18/6/24223), e) the CVD-COVID-UK/COVID-IMPACT consortium and f) the Multimorbidity Mechanism and Therapeutic Research Collaborative (MMTRC, grant number MR/V033867/1). J.C.Z. was supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust.
Collapse
Affiliation(s)
- Mine Koprulu
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Julia Carrasco-Zanini
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Chloe M Orkin
- Blizard Institute and SHARE Collaborative, Queen Mary University of London, London, UK
- Department of Infection and Immunity, Barts Health NHS Trust, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
| |
Collapse
|
3
|
Davis CN, Khan Y, Toikumo S, Jinwala Z, Boomsma DI, Levey DF, Gelernter J, Kember RL, Kranzler HR. Integrating HiTOP and RDoC frameworks part II: shared and distinct biological mechanisms of externalizing and internalizing psychopathology. Psychol Med 2025; 55:e137. [PMID: 40340892 DOI: 10.1017/s0033291725000819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/10/2025]
Abstract
BACKGROUND The Hierarchical Taxonomy of Psychopathology (HiTOP) and Research Domain Criteria (RDoC) frameworks emphasize transdiagnostic and mechanistic aspects of psychopathology. We used a multi-omics approach to examine how HiTOP's psychopathology spectra (externalizing [EXT], internalizing [INT], and shared EXT + INT) map onto RDoC's units of analysis. METHODS We conducted analyses across five RDoC units of analysis: genes, molecules, cells, circuits, and physiology. Using genome-wide association studies from the companion Part I article, we identified genes and tissue-specific expression patterns. We used drug repurposing analyses that integrate gene annotations to identify potential therapeutic targets and single-cell RNA sequencing data to implicate brain cell types. We then used magnetic resonance imaging data to examine brain regions and circuits associated with psychopathology. Finally, we tested causal relationships between each spectrum and physical health conditions. RESULTS Using five gene identification methods, EXT was associated with 1,759 genes, INT with 454 genes, and EXT + INT with 1,138 genes. Drug repurposing analyses identified potential therapeutic targets, including those that affect dopamine and serotonin pathways. Expression of EXT genes was enriched in GABAergic, cortical, and hippocampal neurons, while INT genes were more narrowly linked to GABAergic neurons. EXT + INT liability was associated with reduced gray matter volume in the amygdala and subcallosal cortex. INT genetic liability showed stronger causal effects on physical health - including chronic pain and cardiovascular diseases - than EXT. CONCLUSIONS Our findings revealed shared and distinct pathways underlying psychopathology. Integrating genomic insights with the RDoC and HiTOP frameworks advanced our understanding of mechanisms that underlie EXT and INT psychopathology.
Collapse
Affiliation(s)
- Christal N Davis
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Yousef Khan
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Zeal Jinwala
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Dorret I Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, The Netherlands and Amsterdam Reproduction and Development Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Daniel F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Psychiatry Division, VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Joel Gelernter
- Psychiatry Division, VA Connecticut Healthcare Center, West Haven, CT, USA
- Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, Center for Studies of Addiction, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| |
Collapse
|
4
|
Bao Y, Chen J, Han X, He Y, Yang T, Shi X, Chen J, Gu L, Wang S, Xie L, Wang H, Wang L. Calbindin 2 as a Novel Biomarker and Therapeutic Target for Abdominal Aortic Aneurysm: Integrative Analysis of Human Proteomes and Genetics. J Am Heart Assoc 2025; 14:e039195. [PMID: 40314374 DOI: 10.1161/jaha.124.039195] [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: 10/01/2024] [Accepted: 04/08/2025] [Indexed: 05/03/2025]
Abstract
BACKGROUND Abdominal aortic aneurysm (AAA) is a clinical life-threatening issue. No pharmacological treatments are currently approved for the prevention and treatment of AAA. Therefore, identifying novel biomarkers and therapeutic targets is crucial for improving AAA management and outcomes. METHODS To identify plasma proteins with potential causal effects on AAA, we integrated genetic evidence from proteome-wide Mendelian randomization, genetic correlation, and colocalization analysis. The role of identified proteins in AAA was further explored through the phenome-wide association study and mediation analysis. Multiomics data analysis, including bulk RNA sequencing, single-cell/single-nucleus RNA sequencing, and spatial transcriptomics, was employed to characterize the expression patterns of these proteins. Experimental validation was performed using an AAA model in apolipoprotein E-deficient mice infused with angiotensin II. Druggability analysis was conducted to identify drug candidates, which were tested in preclinical mouse models. RESULTS CALB2 (calbindin 2) was identified as having a causal effect on AAA and may influence the progression of AAA through the regulation of lipid metabolism. Multiomics analysis revealed that CALB2 is predominantly expressed in the mesothelial cells of adipose tissues. Inhibition of CALB2 in an AAA mouse model alleviated AAA progression. Druggability analysis identified lenalidomide and genistein as potential therapeutic candidates, and experiments confirmed their efficacy in preventing AAA development. CONCLUSIONS This study identifies CALB2 as being associated with an increased risk of AAA and suggests that i might be a novel biomarker and therapeutic molecule for AAA management. Lenalidomide and genistein hold promising potential as treatments for patients with AAA.
Collapse
Affiliation(s)
- Yulin Bao
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Jiayi Chen
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Xudong Han
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Ye He
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Tongtong Yang
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Xinying Shi
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Jiawen Chen
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Lingfeng Gu
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Sibo Wang
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Liping Xie
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine Nanjing Medical University Nanjing Jiangsu China
| | - Hao Wang
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| | - Liansheng Wang
- Department of Cardiology The First Affiliated Hospital with Nanjing Medical University Nanjing Jiangsu China
| |
Collapse
|
5
|
Khodursky S, Mimouni N, Levin MG. Recent developments in population biobanks and the genetic architecture of complex disease. Hum Mol Genet 2025:ddaf036. [PMID: 40292753 DOI: 10.1093/hmg/ddaf036] [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: 11/25/2024] [Revised: 02/05/2025] [Accepted: 03/09/2025] [Indexed: 04/30/2025] Open
Abstract
Population biobanks have radically transformed our understanding of complex disease genetics. Recent technological advances and the inclusion of diverse populations have accelerated the discovery and interpretation of variant associations. For instance, population-scale whole-genome sequencing now allows deep exploration of rare and structural variant associations, while multi-omics approaches integrating genome-wide association studies with proteomics, metabolomics, and advanced statistical methods like Mendelian randomization provide nuanced insights into genetic disease mechanisms. Additionally, cross-biobank collaborations and meta-analyses have been particularly impactful, dramatically increasing the statistical power for discovery. These efforts have identified novel genetic associations across numerous complex diseases, with significant contributions from non-European populations. However, data integration complexities, privacy concerns, and methodological limitations continue to constrain research. Here we review how recent advances have contributed to genetic discovery.
Collapse
Affiliation(s)
- Samuel Khodursky
- University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd., Philadelphia, PA 19104, United States
| | - Nour Mimouni
- University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd., Philadelphia, PA 19104, United States
| | - Michael G Levin
- University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd., Philadelphia, PA 19104, United States
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd., Philadelphia, PA 19104, United States
- Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, 3900 Woodland Ave., Philadelphia, PA 19104, United States
| |
Collapse
|
6
|
Guo Y, Zhao J, Hou S, Chen Z. Exploring the effect of SGLT2 inhibitors on the risk of primary open-angle glaucoma using Mendelian randomization analysis. Sci Rep 2025; 15:13946. [PMID: 40263428 PMCID: PMC12015256 DOI: 10.1038/s41598-025-98997-8] [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: 09/06/2024] [Accepted: 04/16/2025] [Indexed: 04/24/2025] Open
Abstract
This study aimed to evaluate the causal effect of sodium-glucose cotransporter protein 2 (SGLT2) inhibition on primary open-angle glaucoma (POAG) and explore potential mechanisms. A drug-targeted Mendelian randomization (MR) study was conducted using genetic variation related to SGLT2 inhibition, based on SGLT2 gene expression and glycated hemoglobin levels. Genetic summary statistics for POAG were obtained from the FinnGen consortium and a multi-ancestry genome-wide association study. Glaucomatous endophenotype data were also incorporated. A two-step MR analysis was performed to examine whether pathways related to obesity, blood pressure, lipid levels, oxidative stress, and inflammation mediated the association between SGLT2 inhibition and POAG. Genetically predicted SGLT2 inhibition was associated with a reduced risk of POAG (OR: 0.28; 95% CI: 0.12 to 0.63; P = 2.22 × 10- 3), confirmed in a multi-ancestry validation cohort. It was also associated with decreased optic cup area, reduced vertical cup-disc ratio, and increased optic disc area. Mediation analysis indicated that the effect of SGLT2 inhibition on POAG was partly mediated by diastolic blood pressure (4.8%). This study suggests that SGLT2 inhibition is a promising therapeutic target for POAG. However, further large-scale randomized controlled trials are required to confirm these findings.
Collapse
Affiliation(s)
- Yujin Guo
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, China
- Department of Ophthalmology, Children's Hospital Affiliated to Shandong University, Jinan, China
| | - Jing Zhao
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, China
| | - Shuai Hou
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
| | - Zhiqing Chen
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China.
| |
Collapse
|
7
|
Schmidt AF, Finan C, van Setten J, Puyol-Antón E, Ruijsink B, Bourfiss M, Alasiri AI, Velthuis BK, Asselbergs FW, Te Riele ASJM. A Mendelian randomization analysis of cardiac MRI measurements as surrogate outcomes for heart failure and atrial fibrillation. COMMUNICATIONS MEDICINE 2025; 5:130. [PMID: 40253538 PMCID: PMC12009341 DOI: 10.1038/s43856-025-00855-1] [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: 06/21/2023] [Accepted: 04/07/2025] [Indexed: 04/21/2025] Open
Abstract
BACKGROUND Drug development and disease prevention of heart failure (HF) and atrial fibrillation (AF) are impeded by a lack of robust early-stage surrogates. We determined to what extent cardiac magnetic resonance (CMR) measurements act as surrogates for the development of HF or AF. METHODS Genetic data were sourced on the association with 21 atrial and ventricular CMR measurements. Mendelian randomization was used to determine CMR associations with AF, HF, non-ischaemic cardiomyopathy (NICM), and dilated cardiomyopathy (DCM), noting that the definition of NICM includes DCM as a subset. Additionally, for the CMR surrogates of AF and HF, we explored their association with non-cardiac traits potentially influenced by cardiac disease liability. RESULTS In total we find that 7 CMR measures (biventricular ejection fraction (EF) and end-systolic volume (ESV), as well as LV systolic volume (SV), end-diastolic volume (EDV), and mass to volume ratio (MVR)) associate with the development of HF, 5 with the development of NICM (biventricular EDV and ESV, LV-EF), 7 with DCM (biventricular EF, ESV, EDV, and LV end-diastolic mass (EDM), and 3 associate with AF (LV-ESV, RV-EF, RV-ESV). Higher EF of both ventricles associate with lower risk of HF and DCM, with biventricular ESV associating with all four cardiac outcomes. Higher values of biventricular EDV associate with lower risk of HF, and DCM. Exploring the associations of these CMR cardiac disease surrogates with non-cardiac traits confirms a strong link with diastolic blood pressure, as well as more specific associations with lung function (LV-ESV), HbA1c (LV-EDM), and type 2 diabetes (LV-SV). CONCLUSIONS The current paper identifies key CMR measurements that may act as surrogate endpoints for the development of HF (including NICM and DCM) or AF.
Collapse
Affiliation(s)
- A F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
- UCL BHF Research Accelerator Centre, London, UK.
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
| | - C Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL BHF Research Accelerator Centre, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - J van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - E Puyol-Antón
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - B Ruijsink
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - M Bourfiss
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - A I Alasiri
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - B K Velthuis
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - F W Asselbergs
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
- Institute of Health Informatics, Faculty of Population Health, University College London, London, UK
| | - A S J M Te Riele
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| |
Collapse
|
8
|
Xie A, He Z, Song C, Wang R, Wu L, Chen R, Jiang G, Liu W, Liu J, Mao W. Decoding the causal association between immune cells and three chronic respiratory diseases: Insights from a bi-directional Mendelian randomization study. BMC Pulm Med 2025; 25:183. [PMID: 40234829 PMCID: PMC11998255 DOI: 10.1186/s12890-025-03641-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: 03/15/2024] [Accepted: 04/01/2025] [Indexed: 04/17/2025] Open
Abstract
BACKGROUND Numerous studies have indicated the correlations of immune traits and chronic respiratory diseases (CRDs). Whereas, causality is still implicative. Hence, our study was designed to investigate the causal relations utilizing bidirectional Mendelian randomization (MR) and to identify the immune traits of potential significance. METHODS Using GWAS datasets, we performed Mendelian randomization (MR) analyses to examine 731 immune traits associated with three CRDs: asthma, bronchiectasis and chronic obstructive pulmonary disease (COPD). Six widely applied MR approaches, along with Bayesian weighted Mendelian randomization analysis, were utilized to assess causality. Through extensive sensitivity assessments, heterogeneity and pleiotropy have been examined. For integrity, leave-one-out analysis was implemented as the final step. RESULTS Our study reveals 13 immune traits that may have a genetic basis for predicting the occurrence of CRDs, which include two risk traits (CD62L- myeloid dendritic cell (DC) absolute count (AC), CD8 on CD28+ CD45RA- CD8+ T cell) and four protective traits (CD39+ CD8+ %T cell, CD4 on CD39+ activated CD4 regulatory T (Treg) cell, herpes virus entry mediator (HVEM) on Central Memory (CM) CD8+ T cell, CD16 on CD14+ CD16+ monocyte) in COPD, three protective traits (IgD- CD27- %B cell, CD3 on CM CD8+ T cell, CD16 on CD14+ CD16+ monocyte) and one risk trait (CD62L- %DC) in bronchiectasis. Additionally, two risk traits (CD14- CD16- AC monocyte, CD19 on IgD+ CD38+ B cell) and one protective trait (HVEM on CD45RA- CD4+ T cell) were identified in asthma. Sensitivity analyses showed no indications of pleiotropy or signs of heterogeneity. The inverse MR assessment results gave no evidence of reverse causations, ultimately validating the soundness of the findings. CONCLUSIONS Our investigation identifies latent correlations of immune traits and three major CRDs, offering novel perspectives on the preventive and therapeutical strategies for CRDs.
Collapse
Affiliation(s)
- Anqi Xie
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Zhao He
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Chenghu Song
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Ruixin Wang
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Lei Wu
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Ruo Chen
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Guanyu Jiang
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China
| | - Weici Liu
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China.
| | - Jiwei Liu
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China.
| | - Wenjun Mao
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Rd., Wuxi , Jiangsu, 214023, China.
| |
Collapse
|
9
|
Tambets R, Kronberg J, van der Graaf A, Jesse M, Abner E, Võsa U, Rahu I, Taba N, Kolde A, Yarish D, Fischer K, Kutalik Z, Esko T, Alasoo K, Palta P. Genome-wide association study for circulating metabolic traits in 619,372 individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2024.10.15.24315557. [PMID: 40297438 PMCID: PMC12036396 DOI: 10.1101/2024.10.15.24315557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2025]
Abstract
Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis-MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.
Collapse
Affiliation(s)
- Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Mihkel Jesse
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Erik Abner
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ida Rahu
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Nele Taba
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anastassia Kolde
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | | | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Unisanté, University of Lausanne, Lausanne, Switzerland
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| |
Collapse
|
10
|
Milani L, Alver M, Laur S, Reisberg S, Haller T, Aasmets O, Abner E, Alavere H, Allik A, Annilo T, Fischer K, Hofmeister R, Hudjashov G, Jõeloo M, Kals M, Karo-Astover L, Kasela S, Kolde A, Krebs K, Krigul KL, Kronberg J, Kruusmaa K, Kukuškina V, Kõiv K, Lehto K, Leitsalu L, Lind S, Luitva LB, Läll K, Lüll K, Metsalu K, Metspalu M, Mõttus R, Nelis M, Nikopensius T, Nurm M, Nõukas M, Oja M, Org E, Palover M, Palta P, Pankratov V, Pantiukh K, Pervjakova N, Pujol-Gualdo N, Reigo A, Reimann E, Smit S, Rogozina D, Särg D, Taba N, Talvik HA, Teder-Laving M, Tõnisson N, Vaht M, Vainik U, Võsa U, Yelmen B, Esko T, Kolde R, Mägi R, Vilo J, Laisk T, Metspalu A. The Estonian Biobank's journey from biobanking to personalized medicine. Nat Commun 2025; 16:3270. [PMID: 40188112 PMCID: PMC11972354 DOI: 10.1038/s41467-025-58465-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 03/04/2025] [Indexed: 04/07/2025] Open
Abstract
Large biobanks have set a new standard for research and innovation in human genomics and implementation of personalized medicine. The Estonian Biobank was founded a quarter of a century ago, and its biological specimens, clinical, health, omics, and lifestyle data have been included in over 800 publications to date. What makes the biobank unique internationally is its translational focus, with active efforts to conduct clinical studies based on genetic findings, and to explore the effects of return of results on participants. In this review, we provide an overview of the Estonian Biobank, highlight its strengths for studying the effects of genetic variation and quantitative phenotypes on health-related traits, development of methods and frameworks for bringing genomics into the clinic, and its role as a driving force for implementing personalized medicine on a national level and beyond.
Collapse
Affiliation(s)
- Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Maris Alver
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Sven Laur
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- STACC, Tartu, Estonia
| | - Sulev Reisberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- STACC, Tartu, Estonia
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Oliver Aasmets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Erik Abner
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Helene Alavere
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Annely Allik
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tarmo Annilo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Robin Hofmeister
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Liis Karo-Astover
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anastassia Kolde
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kertu Liis Krigul
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Karoliina Kruusmaa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kadri Kõiv
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Liis Leitsalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Sirje Lind
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Laura Birgit Luitva
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kreete Lüll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristjan Metsalu
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - René Mõttus
- Institute of Psychology, University of Tartu, Tartu, Estonia
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Mari Nelis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tiit Nikopensius
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Miriam Nurm
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Margit Nõukas
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Marek Oja
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Elin Org
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Marili Palover
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Vasili Pankratov
- Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kateryna Pantiukh
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Natàlia Pujol-Gualdo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anu Reigo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ene Reimann
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Steven Smit
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Diana Rogozina
- Estonian Biobank, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Dage Särg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nele Taba
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Harry-Anton Talvik
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- STACC, Tartu, Estonia
| | - Maris Teder-Laving
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Neeme Tõnisson
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mariliis Vaht
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Uku Vainik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Psychology, University of Tartu, Tartu, Estonia
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Burak Yelmen
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jaak Vilo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- STACC, Tartu, Estonia
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| |
Collapse
|
11
|
Luan B, Yang Y, Yang Q, Li Z, Xu Z, Chen Y, Wang M, Chen W, Ge F. Gut microbiota, blood metabolites, & pan-cancer: a bidirectional Mendelian randomization & mediation analysis. AMB Express 2025; 15:59. [PMID: 40175810 PMCID: PMC11965084 DOI: 10.1186/s13568-025-01866-w] [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: 10/05/2024] [Accepted: 03/14/2025] [Indexed: 04/04/2025] Open
Abstract
We propose using Mendelian randomization analysis on GWAS data and MetaboAnalyst to model gut microbiota, metabolic pathways, blood metabolites, and cancer risk. We examined 473 gut microbiota, 205 pathways, 1400 metabolites, and 8 cancers. Results were validated through bidirectional two-sample Mendelian Randomization (MR), heterogeneity tests, and pathway enrichment, leading to a mediation pathway model. We identified 129 gut microbiota, 57 pathways, and 463 metabolites linked to cancer, and 34 significant plasma pathways. 15 microbiota, 8 pathways, and 58 metabolites implicated in multiple cancers. Eight plasma metabolic pathways are involved in the development of multiple types of cancer. Through Multivariate Mendelian Randomization (MVMR) and mediation analysis, we found 9 mediation pathways, offering novel targets and research directions for cancer pathogenesis and treatment.
Collapse
Affiliation(s)
- Biqing Luan
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yang Yang
- Yunnan Key Laboratory of Breast Cancer Precision Medicine, Department of breast surgery, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Qizhi Yang
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Zhiqiang Li
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Zhihui Xu
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yaqin Chen
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Meiting Wang
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wenlin Chen
- Yunnan Key Laboratory of Breast Cancer Precision Medicine, Department of breast surgery, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, Kunming, Yunnan, China.
| | - Fei Ge
- Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
| |
Collapse
|
12
|
Dron JS, Natarajan P, Peloso GM. The breadth and impact of the Global Lipids Genetics Consortium. Curr Opin Lipidol 2025; 36:61-70. [PMID: 39602359 PMCID: PMC11888832 DOI: 10.1097/mol.0000000000000966] [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: 11/29/2024]
Abstract
PURPOSE OF REVIEW This review highlights contributions of the Global Lipids Genetics Consortium (GLGC) in advancing the understanding of the genetic etiology of blood lipid traits, including total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, and non-HDL cholesterol. We emphasize the consortium's collaborative efforts, discoveries related to lipid and lipoprotein biology, methodological advancements, and utilization in areas extending beyond lipid research. RECENT FINDINGS The GLGC has identified over 923 genomic loci associated with lipid traits through genome-wide association studies (GWASs), involving more than 1.65 million individuals from globally diverse populations. Many loci have been functionally validated by individuals inside and outside the GLGC community. Recent GLGC studies show increased population diversity enhances variant discovery, fine-mapping of causal loci, and polygenic score prediction for blood lipid levels. Moreover, publicly available GWAS summary statistics have facilitated the exploration of lipid-related genetic influences on cardiovascular and noncardiovascular diseases, with implications for therapeutic development and drug repurposing. SUMMARY The GLGC has significantly advanced the understanding of the genetic basis of lipid levels and serves as the leading resource of GWAS summary statistics for these traits. Continued collaboration will be critical to further understand lipid and lipoprotein biology through large-scale genetic assessments in diverse populations.
Collapse
Affiliation(s)
- Jacqueline S. Dron
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Department of Medicine, Harvard Medical School
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| |
Collapse
|
13
|
Lu J, Wang Z. Mendelian Randomization Provides No Evidence for the Bidirectional Relationship between Type 2 Diabetes and Venous Thromboembolism in East Asians and African Americans. Semin Thromb Hemost 2025; 51:348-350. [PMID: 39029518 DOI: 10.1055/s-0044-1788568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2024]
Affiliation(s)
- Jiawen Lu
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhenqian Wang
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| |
Collapse
|
14
|
Henry A, Mo X, Finan C, Chaffin MD, Speed D, Issa H, Denaxas S, Ware JS, Zheng SL, Malarstig A, Gratton J, Bond I, Roselli C, Miller D, Chopade S, Schmidt AF, Abner E, Adams L, Andersson C, Aragam KG, Ärnlöv J, Asselin G, Raja AA, Backman JD, Bartz TM, Biddinger KJ, Biggs ML, Bloom HL, Boersma E, Brandimarto J, Brown MR, Brunak S, Bruun MT, Buckbinder L, Bundgaard H, Carey DJ, Chasman DI, Chen X, Cook JP, Czuba T, de Denus S, Dehghan A, Delgado GE, Doney AS, Dörr M, Dowsett J, Dudley SC, Engström G, Erikstrup C, Esko T, Farber-Eger EH, Felix SB, Finer S, Ford I, Ghanbari M, Ghasemi S, Ghouse J, Giedraitis V, Giulianini F, Gottdiener JS, Gross S, Guðbjartsson DF, Gui H, Gutmann R, Hägg S, Haggerty CM, Hedman ÅK, Helgadottir A, Hemingway H, Hillege H, Hyde CL, Aagaard Jensen B, Jukema JW, Kardys I, Karra R, Kavousi M, Kizer JR, Kleber ME, Køber L, Koekemoer A, Kuchenbaecker K, Lai YP, Lanfear D, Langenberg C, Lin H, Lind L, Lindgren CM, Liu PP, London B, Lowery BD, Luan J, Lubitz SA, Magnusson P, Margulies KB, Marston NA, Martin H, März W, Melander O, Mordi IR, Morley MP, et alHenry A, Mo X, Finan C, Chaffin MD, Speed D, Issa H, Denaxas S, Ware JS, Zheng SL, Malarstig A, Gratton J, Bond I, Roselli C, Miller D, Chopade S, Schmidt AF, Abner E, Adams L, Andersson C, Aragam KG, Ärnlöv J, Asselin G, Raja AA, Backman JD, Bartz TM, Biddinger KJ, Biggs ML, Bloom HL, Boersma E, Brandimarto J, Brown MR, Brunak S, Bruun MT, Buckbinder L, Bundgaard H, Carey DJ, Chasman DI, Chen X, Cook JP, Czuba T, de Denus S, Dehghan A, Delgado GE, Doney AS, Dörr M, Dowsett J, Dudley SC, Engström G, Erikstrup C, Esko T, Farber-Eger EH, Felix SB, Finer S, Ford I, Ghanbari M, Ghasemi S, Ghouse J, Giedraitis V, Giulianini F, Gottdiener JS, Gross S, Guðbjartsson DF, Gui H, Gutmann R, Hägg S, Haggerty CM, Hedman ÅK, Helgadottir A, Hemingway H, Hillege H, Hyde CL, Aagaard Jensen B, Jukema JW, Kardys I, Karra R, Kavousi M, Kizer JR, Kleber ME, Køber L, Koekemoer A, Kuchenbaecker K, Lai YP, Lanfear D, Langenberg C, Lin H, Lind L, Lindgren CM, Liu PP, London B, Lowery BD, Luan J, Lubitz SA, Magnusson P, Margulies KB, Marston NA, Martin H, März W, Melander O, Mordi IR, Morley MP, Morris AP, Morrison AC, Morton L, Nagle MW, Nelson CP, Niessner A, Niiranen T, Noordam R, Nowak C, O'Donoghue ML, Ostrowski SR, Owens AT, Palmer CNA, Paré G, Pedersen OB, Perola M, Pigeyre M, Psaty BM, Rice KM, Ridker PM, Romaine SPR, Rotter JI, Ruff CT, Sabatine MS, Sallah N, Salomaa V, Sattar N, Shalaby AA, Shekhar A, Smelser DT, Smith NL, Sørensen E, Srinivasan S, Stefansson K, Sveinbjörnsson G, Svensson P, Tammesoo ML, Tardif JC, Teder-Laving M, Teumer A, Thorgeirsson G, Thorsteinsdottir U, Torp-Pedersen C, Tragante V, Trompet S, Uitterlinden AG, Ullum H, van der Harst P, van Heel D, van Setten J, van Vugt M, Veluchamy A, Verschuuren M, Verweij N, Vissing CR, Völker U, Voors AA, Wallentin L, Wang Y, Weeke PE, Wiggins KL, Williams LK, Yang Y, Yu B, Zannad F, Zheng C, Asselbergs FW, Cappola TP, Dubé MP, Dunn ME, Lang CC, Samani NJ, Shah S, Vasan RS, Smith JG, Holm H, Shah S, Ellinor PT, Hingorani AD, Wells Q, Lumbers RT. Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes. Nat Genet 2025; 57:815-828. [PMID: 40038546 PMCID: PMC11985341 DOI: 10.1038/s41588-024-02064-3] [Show More Authors] [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: 10/09/2023] [Accepted: 12/17/2024] [Indexed: 03/06/2025]
Abstract
Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.
Collapse
Affiliation(s)
- Albert Henry
- Institute of Cardiovascular Science, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Xiaodong Mo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
| | - Mark D Chaffin
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Doug Speed
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Hanane Issa
- Institute of Health Informatics, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - James S Ware
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- Hammersmith Hospital, Imperial College Hospitals NHS Trust, London, UK
| | - Sean L Zheng
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Anders Malarstig
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Jasmine Gratton
- Institute of Cardiovascular Science, University College London, London, UK
| | - Isabelle Bond
- Institute of Cardiovascular Science, University College London, London, UK
| | - Carolina Roselli
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - David Miller
- Division of Biosciences, University College London, London, UK
| | - Sandesh Chopade
- Institute of Cardiovascular Science, University College London, London, UK
| | - A Floriaan Schmidt
- Institute of Cardiovascular Science, University College London, London, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Charlotte Andersson
- Department of Cardiology, Herlev Gentofte Hospital, Herlev, Denmark
- National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Krishna G Aragam
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Sciences, Dalarna University, Falun, Sweden
| | | | - Anna Axelsson Raja
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Joshua D Backman
- Analytical Genetics, Regeneron Genetics Center, Tarrytown, NY, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kiran J Biddinger
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Heather L Bloom
- Department of Medicine, Division of Cardiology, Emory University Medical Center, Atlanta, GA, USA
| | - Eric Boersma
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jeffrey Brandimarto
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael R Brown
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Henning Bundgaard
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Xing Chen
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Tomasz Czuba
- Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Simon de Denus
- Montreal Heart Institute, Montreal, Quebec, Canada
- Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - Abbas Dehghan
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Alexander S Doney
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Joseph Dowsett
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Samuel C Dudley
- Department of Medicine, Cardiovascular Division, University of Minnesota, Minneapolis, MN, USA
| | - Gunnar Engström
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Deparment of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Tõnu Esko
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eric H Farber-Eger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephan B Felix
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Sarah Finer
- Centre for Primary Care and Public Health, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Ian Ford
- Robertson Center for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sahar Ghasemi
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - John S Gottdiener
- Department of Medicine, Division of Cardiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stefan Gross
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Daníel F Guðbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Rebecca Gutmann
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Åsa K Hedman
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | | | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Hans Hillege
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Craig L Hyde
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | | | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, the Netherlands
| | - Isabella Kardys
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ravi Karra
- Department of Medicine, Division of Cardiology, Duke University Medical Center, Durham, NC, USA
- Department of Pathology, Duke University Medical Center, Durham, NC, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jorge R Kizer
- Cardiology Section, San Francisco Veterans Affairs Health System, and Departments of Medicine, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Lars Køber
- Department of Cardiology, Nordsjaellands Hospital, Copenhagen, Denmark
| | - Andrea Koekemoer
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Yi-Pin Lai
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - David Lanfear
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA
- Heart and Vascular Institute, Henry Ford Hospital, Detroit, MI, USA
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Honghuang Lin
- National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Peter P Liu
- University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Barry London
- Division of Cardiovascular Medicine and Abboud Cardiovascular Research Center, University of Iowa, Iowa City, IA, USA
| | - Brandon D Lowery
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Steven A Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrik Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kenneth B Margulies
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Hilary Martin
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
| | - Olle Melander
- Department of Internal Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Ify R Mordi
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Michael P Morley
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX, USA
| | - Lori Morton
- Cardiovascular Research, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Alexander Niessner
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Teemu Niiranen
- Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
| | - Michelle L O'Donoghue
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anjali T Owens
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin N A Palmer
- Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Ole Birger Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
| | - Marie Pigeyre
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, CA, USA
- Departments of Pediatrics and Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA
- Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Marc S Sabatine
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Neneh Sallah
- Institute of Health Informatics, University College London, London, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Naveed Sattar
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Alaa A Shalaby
- Department of Medicine, Division of Cardiology, University of Pittsburgh Medical Center and VA Pittsburgh HCS, Pittsburgh, PA, USA
| | - Akshay Shekhar
- Cardiovascular Research, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Diane T Smelser
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Nicholas L Smith
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Veterans Affairs Office of Research & Development, Seattle Epidemiologic Research and Information Center, Seattle, WA, USA
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sundararajan Srinivasan
- Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Per Svensson
- Department of Cardiology, Söderjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education-Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Mari-Liis Tammesoo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada
- Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexander Teumer
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Guðmundur Thorgeirsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Internal Medicine, Division of Cardiology, National University Hospital of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
| | | | | | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | - David van Heel
- Centre for Genomics and Child Health, Blizard Institute, Queen Mary University of London, London, UK
| | - Jessica van Setten
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marion van Vugt
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Abirami Veluchamy
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
- Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Monique Verschuuren
- Department Life Course and Health, Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Niek Verweij
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Christoffer Rasmus Vissing
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Adriaan A Voors
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Lars Wallentin
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter E Weeke
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Kerri L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Yifan Yang
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX, USA
| | - Faiez Zannad
- Université de Lorraine, CHU de Nancy, Inserm and INI-CRCT (F-CRIN), Institut Lorrain du Coeur et des Vaisseaux, Vandoeuvre Lès Nancy, France
| | - Chaoqun Zheng
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Thomas P Cappola
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, Quebec, Canada
- Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Michael E Dunn
- Cardiovascular Research, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Chim C Lang
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Svati Shah
- Department of Medicine, Division of Cardiology, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Ramachandran S Vasan
- National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Sections of Cardiology, Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - J Gustav Smith
- Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Sonia Shah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
| | - Quinn Wells
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK.
- Health Data Research UK, London, UK.
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK.
| |
Collapse
|
15
|
You D, Wu Y, Lu M, Shao F, Tang Y, Liu S, Liu L, Zhou Z, Zhang R, Shen S, Lange T, Xu H, Ma H, Yin Y, Shen H, Chen F, Christiani DC, Jin G, Zhao Y. A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases. Nat Commun 2025; 16:3032. [PMID: 40155373 PMCID: PMC11953465 DOI: 10.1038/s41467-025-58248-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 03/11/2025] [Indexed: 04/01/2025] Open
Abstract
Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.
Collapse
Affiliation(s)
- Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yaqian Wu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Mengyi Lu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fang Shao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yingdan Tang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sisi Liu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Liya Liu
- Health Science Center, Ningbo University, Ningbo, Zhejiang, China
| | - Zewei Zhou
- Department of Immunology, Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruyang Zhang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sipeng Shen
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Theis Lange
- Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hongyang Xu
- Department of Critical Care Medicine, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongmei Yin
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Feng Chen
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Ministry of Education Key Laboratory for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Guangfu Jin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China.
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, Jiangsu, China.
- Ministry of Education Key Laboratory for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
| |
Collapse
|
16
|
Chandratre P, Sabido-Sauri R, Zhao SS, Abhishek A. Gout, Hyperuricemia and Psoriatic Arthritis: An Evolving Conundrum. Curr Rheumatol Rep 2025; 27:22. [PMID: 40146321 DOI: 10.1007/s11926-025-01187-8] [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] [Accepted: 03/05/2025] [Indexed: 03/28/2025]
Abstract
PURPOSE OF REVIEW The co-existence of gout and psoriatic disease (PD) is long standing but more recently frequently encountered in clinical settings due to increased awareness of their shared comorbidities and clinical phenotypes, often posing diagnostic and management challenges. Here we review the overlap in gout and PD focusing on shared clinical features, common inflammatory pathophysiology and comorbidities which may prompt a diagnosis of 'Psout' and lead to changes in management. RECENT FINDINGS Several epidemiological studies have highlighted the increased incidence of hyperuricemia and gout in those with PD and vice versa. Although the role of monosodium urate (MSU) crystals is well recognized in activation of innate immunity via inflammasome and NETosis, it is likely that they have a role in triggering adaptive immunity via antigen presenting cells and their autocrine effect on keratinocytes in psoriasis (PSO), ultimately leading to T cell secretion of proinflammatory cytokines such as IL17. Hyperuricemia (HU) is common in PD (up to 30%) and underpins metabolic syndrome comorbidities that are common to both gout and PD. Shared clinical phenotypes and co-morbidities are routinely observed in clinical practice yet there is a paucity of evidence evaluating the effect of treating hyperuricemia/gout on PD activity, with small scale clinical trials showing a positive effect. There were no studies to our knowledge assessing gout disease activity with concurrent treatment of PD. The association between gout and PD is likely due to shared multimorbidity and perhaps to a smaller extent, the direct role of HU in triggering the release of proinflammatory cytokines in PD. There is often a significant overlap in clinical and radiological presentation of gout and Psoriatic arthritis (PsA). In those with atypical response to standard treatments of the primary condition (either gout or PsA), it would be plausible to investigate and treat for the other 'secondary' condition. This is particularly relevant and relatively feasible in those with PsA (and features of HU and multimorbidity) who respond poorly to standard immunomodulating treatments.
Collapse
Affiliation(s)
- Priyanka Chandratre
- Department of Medicine, Division of Rheumatology, The Ottawa Hospital and Ottawa Hospital Research Institute, Ottawa, Canada.
| | - Ricardo Sabido-Sauri
- Department of Medicine, Division of Rheumatology, The Ottawa Hospital and Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sizheng Steven Zhao
- Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology Medicine and Health, Centre for Musculoskeletal Research, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | | |
Collapse
|
17
|
Kottyan LC, Richards S, Tracy ME, Lawson LP, Cobb B, Esslinger S, Gerwe M, Morgan J, Chandel A, Travitz L, Huang Y, Black C, Sobowale A, Akintobi T, Mitchell M, Beck AF, Unaka N, Seid M, Fairbanks S, Adams M, Mersha T, Namjou B, Pauciulo MW, Strawn JR, Ammerman RT, Santel D, Pestian J, Glauser T, Prows CA, Martin LJ, Muglia L, Harley JB, Chepelev I, Kaufman KM. Sequencing and health data resource of children of African ancestry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.03.22.25324419. [PMID: 40196241 PMCID: PMC11974803 DOI: 10.1101/2025.03.22.25324419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Purpose Individuals who self-report as Black or African American are historically underrepresented in genome-wide studies of disease risk, a disparity particularly evident in pediatric disease research. To address this gap, Cincinnati Children's Hospital Medical Center (CCHMC) established a biorepository and developed a comprehensive DNA sequencing resource including 15,684 individuals who self-identified as African American or Black and received care at CCHMC. Methods Participants were enrolled through the CCHMC Discover Together Biobank and sequenced. Admixture analyses confirmed the genetic ancestry of the cohort, which was then linked to electronic medical records. Results High-quality genome-wide genotypes from common variants accompanied by medical recordsourced data are available through the Genomic Information Commons. This dataset performs well in genetic studies. Specifically, we replicated known associations in sickle cell disease (HBB, p = 4.05 × 10-1), anxiety (PLAA3, p = 6.93 × 10-), and asthma (PCDH15, p = 5.6 × 10-1), while also identifying novel loci associated with asthma severity. Conclusion We present the acquisition and quality of genetic and disease-associated data and present an analytical framework for using this resource. In partnership with a community advisory council, we have co-developed a valuable framework for data use and future research.
Collapse
Affiliation(s)
- Leah C. Kottyan
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Allergy & Immunology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Scott Richards
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Morgan E. Tracy
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Discover Together Biobank. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Lucinda P. Lawson
- Division of Allergy & Immunology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Beth Cobb
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Stem Cell & Organoid Medicine (CuSTOM), Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Steve Esslinger
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Discover Together Biobank. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Margaret Gerwe
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Discover Together Biobank. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - James Morgan
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Discover Together Biobank. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Alka Chandel
- Information Services for Research (IS4R). Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Leksi Travitz
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Yongbo Huang
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Catherine Black
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Agboade Sobowale
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Office of Community Relations. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Tinuke Akintobi
- Office of Community Relations. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Monica Mitchell
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Office of Community Relations. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Division of Behavioral Medicine and Clinical Psychology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Andrew F. Beck
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of General & Community Pediatrics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Division of Hospital Medicine. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Office of Population Health and Michael Fisher Child Health Equity Center. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Anderson Center. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Ndidi Unaka
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of General & Community Pediatrics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Department of Pediatrics, Stanford University School of Medicine. Stanford, California
| | - Michael Seid
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Anderson Center. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Division of Pulmonary Medicine. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Sonja Fairbanks
- Division of Hospital Medicine. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Michelle Adams
- Cincinnati Children’s Research Foundation. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Tesfaye Mersha
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Asthma Research. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Bahram Namjou
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Michael W. Pauciulo
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Discover Together Biobank. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Jeffrey R. Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati School of Medicine. Cincinnati, Ohio
| | - Robert T. Ammerman
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Department of Pediatrics, University of Cincinnati. Cincinnati, Ohio
| | - Daniel Santel
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center
| | - John Pestian
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center
- Computational Medicine Center, Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Tracy Glauser
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Neurology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Cynthia A. Prows
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Lisa J. Martin
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - Louis Muglia
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
| | - John B. Harley
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio. Cincinnati, Ohio
| | - Iouri Chepelev
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio. Cincinnati, Ohio
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio
| | - Kenneth M. Kaufman
- Department of Pediatrics. College of Medicine. University of Cincinnati. Cincinnati, Ohio
- Division of Human Genetics. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- Center for Autoimmune Genomics and Etiology. Cincinnati Children’s Hospital Medical Center. Cincinnati, Ohio
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio. Cincinnati, Ohio
| |
Collapse
|
18
|
Wen Z, Liang W, Yang Z, Liu J, Yang J, Xu R, Lin K, Pan J, Chen Z. Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets. J Transl Med 2025; 23:337. [PMID: 40091050 PMCID: PMC11912729 DOI: 10.1186/s12967-025-06368-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Accepted: 03/07/2025] [Indexed: 03/19/2025] Open
Abstract
OBJECTIVE To identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach. METHOD We integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes. RESULT We identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects. CONCLUSION We identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.
Collapse
Affiliation(s)
- Zhuofeng Wen
- 1The Sixth School of Clinical Medicine, Department of Respiratory and Critical Care Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China
| | - Weixuan Liang
- The First School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Ziyang Yang
- The Third School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Junjie Liu
- The Second School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Jing Yang
- 1The Sixth School of Clinical Medicine, Department of Respiratory and Critical Care Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China
| | - Runge Xu
- The First School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Keye Lin
- The First School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Jia Pan
- The First School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China
| | - Zisheng Chen
- 1The Sixth School of Clinical Medicine, Department of Respiratory and Critical Care Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China.
| |
Collapse
|
19
|
Liu W, Sun Y, Zhang Y, Yin D. The causal relationships between inflammatory cytokines, blood metabolites, and thyroid cancer: a two-step Mendelian randomization analysis. Discov Oncol 2025; 16:301. [PMID: 40072746 PMCID: PMC11904021 DOI: 10.1007/s12672-025-02029-w] [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: 10/09/2024] [Accepted: 03/03/2025] [Indexed: 03/14/2025] Open
Abstract
BACKGROUND Thyroid cancer is a prevalent malignant tumor, especially with a higher incidence in women. Tumor microenvironment changes induced by inflammation and alterations in metabolic characteristics are critical in the development of thyroid cancer. Nevertheless, their causal relationships remain unclear. METHODS We utilized thyroid cancer GWAS data from the Global Biobank Meta-Analysis Initiative and GWAS data of 91 inflammatory cytokines and 1400 blood metabolites obtained from the GWAS Catalog to evaluate the causality between inflammatory cytokines, blood metabolites, and thyroid cancer using Mendelian randomization (MR). Initially, we identified inflammatory cytokines having a significant causal effect on thyroid cancer. Subsequently, for the identified positive blood metabolites, we applied a two-step mediation MR method to examine their mediating role in the causal effect of specific inflammatory cytokines on thyroid cancer. RESULTS Our forward MR analysis identified suggestive associations between 7 inflammatory cytokines and thyroid cancer risks, and found that tumor necrosis factor ligand superfamily member 14 (TNFSF14) (IVW-OR: 1.25, 95% CI 1.10-1.42, p = 0.0004) is a significant risk factor in thyroid cancer, and this causal relationship remained significant after Bonferroni correction. The reverse MR analysis identified suggestive causal associations between thyroid cancer and 3 inflammatory cytokines and ruled out the reverse causality between TNFSF14 and thyroid cancer. Then, we identified suggestive associations between 35 blood metabolites and 24 blood metabolite ratios with thyroid cancer, and found that 5-hydroxymethyl-2-furoylcarnitine (IVW-OR: 1.38, 95% CI 1.19-1.61, p = 0.00003) is a significant risk factor for thyroid cancer, with this causality remaining significant after Bonferroni correction. Finally, our two-step MR analysis indicated that Lactosyl-N-palmitoyl-sphingosine (d18:1/16:0) and X-12013 have a mediating effect in the causal relationship between TNFSF14 and thyroid cancer, with mediation proportions of 8.55% and 5.78%, respectively. Our MR analysis did not identify significant heterogeneity or horizontal pleiotropy. CONCLUSION This study identified some inflammatory cytokines and blood metabolites associated with thyroid cancer risk and revealed the mediating role of specific blood metabolites between TNFSF14 and thyroid cancer, highlighting the critical role of inflammatory and metabolic pathways in the pathogenesis of thyroid cancer.
Collapse
Affiliation(s)
- Weihao Liu
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yuxiao Sun
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yifei Zhang
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Detao Yin
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Zhengzhou, 450052, China.
| |
Collapse
|
20
|
Jung HU, Jung H, Baek EJ, Kang JO, Kwon SY, You J, Lim JE, Oh B. Assessment of polygenic risk score performance in East Asian populations for ten common diseases. Commun Biol 2025; 8:374. [PMID: 40045046 PMCID: PMC11882803 DOI: 10.1038/s42003-025-07767-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 02/18/2025] [Indexed: 03/09/2025] Open
Abstract
Polygenic risk score (PRS) uses genetic variants to assess disease susceptibility. While PRS performance is well-studied in Europeans, its accuracy in East Asians is less explored. This study evaluated PRSs for ten diseases in the Health Examinees (HEXA) cohort (n = 55,870) in Korea. Single-population PRSs were constructed using PRS-CS, LDpred2, and Lassosum based on East Asian GWAS summary statistics (sample sizes: 51,442-341,204), while cross-population PRSs were developed using PRS-CSx and CT-SLEB by integrating European and East Asian GWAS data. PRS-CS consistently outperformed other single-population methods across key metrics, including the likelihood ratio test (LRT), odds ratio per standard deviation (perSD OR), net reclassification improvement (NRI), and area under the curve (AUC). Cross-population PRSs further improved predictive performance, with average increases of 1.08-fold (LRT), 1.07-fold (perSD OR), and 1.15-fold (NRI) across seven diseases with statistical significance, and a 1.01-fold improvement in AUC. Differences in R² between single- and cross-population PRSs were statistically significant for five diseases, showing an average increase of 1.13%. Cross-population PRSs achieved 87.8% of the predictive performance observed in European PRSs. These findings highlight the benefits of integrating European GWAS data while underscoring the need for larger East Asian datasets to improve prediction accuracy.
Collapse
Affiliation(s)
- Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
- Mendel Inc, Seoul, Republic of Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
| |
Collapse
|
21
|
Palma-Martínez MJ, Posadas-García YS, Shaukat A, López-Ángeles BE, Sohail M. Evolution, genetic diversity, and health. Nat Med 2025; 31:751-761. [PMID: 40055519 DOI: 10.1038/s41591-025-03558-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 02/03/2025] [Indexed: 03/21/2025]
Abstract
Human genetic diversity in today's world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies-integrating ancestral and environmental factors-is crucial for fully understanding the genetic basis of human health and disease.
Collapse
Affiliation(s)
- María J Palma-Martínez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | | | - Amara Shaukat
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Brenda E López-Ángeles
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Mashaal Sohail
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México.
| |
Collapse
|
22
|
Lin S, Li YE, Wang Y. Multi-Cohort Analysis Reveals Genetic Predispositions to Clonal Hematopoiesis as Mutation-Specific Risk Factors for Stroke. ADVANCED GENETICS (HOBOKEN, N.J.) 2025; 6:2400047. [PMID: 40093911 PMCID: PMC11909397 DOI: 10.1002/ggn2.202400047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 01/17/2025] [Indexed: 03/19/2025]
Abstract
Recent observational studies have found an association between Clonal Hematopoesis (CH) and strokes but with incomplete results. This study aims to comprehensively characterize mutation-specific effects of CH on ischemic and hemorrhagic stroke subtypes and 90-day functional outcomes through publicly available genome-wide association study (GWAS) cohorts and Mendelian Randomization. TET2 is associated with an increased risk of overall stroke (OR = 1.06, P = 0.02), ischemic stroke (OR = 1.05, P = 0.03), transient ischemic attack (OR = 1.07, P = 0.01) and small vessel stroke (OR = 1.29, P = 0.01), as well as adverse 90-day modified Rankin scale (mRS ≥ 3) before (OR = 1.34, P = 0.005) and after adjusted for age, sex, and stroke severity (OR = 1.30, P = 0.02). While the presence of any CH mutation is associated with intracerebral hemorrhage (ICH) (OR = 1.21, P = 0.02), specific mutations, SRSF2 and ASXL1 are protective against ICH (OR = 0.9, P = 0.04) and nontraumatic subarachnoid hemorrhage (OR = 0.92, P = 0.03), respectively. In conclusion, the study provided genetic evidence that TET2 is strongly associated with an increased risk of ischemic stroke and poor functional recovery. Future studies clarifying the relationship between CH and hemorrhagic stroke subtypes are needed.
Collapse
Affiliation(s)
- Shuyang Lin
- Department of HematologyWashington University School of Medicine in St LouisSt. LouisMO63110USA
- Department of GeneticsWashington University School of Medicine in St LouisSt. LouisMO63110USA
| | - Yang E. Li
- Department of GeneticsWashington University School of Medicine in St LouisSt. LouisMO63110USA
- Department of NeurosurgeryWashington University School of Medicine in St LouisSt. LouisMO63110USA
| | - Yan Wang
- Department of NeurologyWashington University School of Medicine in St LouisSt. LouisMO63110USA
| |
Collapse
|
23
|
Rasooly D, Giambartolomei C, Peloso GM, Dashti H, Ferolito BR, Golden D, Horimoto ARVR, Pietzner M, Farber-Eger EH, Wells QS, Bini G, Proietti G, Tartaglia GG, Kosik NM, Wilson PWF, Phillips LS, Munroe PB, Petersen SE, Cho K, Gaziano JM, Leach AR, Whittaker J, Langenberg C, Aung N, Sun YV, Pereira AC, Casas JP, Joseph J. Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction. NATURE CARDIOVASCULAR RESEARCH 2025; 4:293-311. [PMID: 39915329 DOI: 10.1038/s44161-025-00609-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 01/07/2025] [Indexed: 03/19/2025]
Abstract
Heart failure (HF) has limited therapeutic options. In this study, we differentiated the pathophysiological underpinnings of the HF subtypes-HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)-and uncovered subtype-specific therapeutic strategies. We investigated the causal roles of the human proteome and transcriptome using Mendelian randomization on more than 420,000 participants from the Million Veteran Program (27,799 HFrEF and 27,579 HFpEF cases). We created therapeutic target profiles covering efficacy, safety, novelty, druggability and mechanism of action. We replicated findings on more than 175,000 participants of diverse ancestries. We identified 70 HFrEF and 10 HFpEF targets, of which 58 were not previously reported; notably, the HFrEF and HFpEF targets are non-overlapping, suggesting the need for subtype-specific therapies. We classified 14 previously unclassified HF loci as HFrEF. We substantiated the role of ubiquitin-proteasome system, small ubiquitin-related modifier pathway, inflammation and mitochondrial metabolism in HFrEF. Among druggable genes, IL6R, ADM and EDNRA emerged as potential HFrEF targets, and LPA emerged as a potential target for both subtypes.
Collapse
Affiliation(s)
- Danielle Rasooly
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA.
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Claudia Giambartolomei
- Integrative Data Analysis Unit, Health Data Science Centre, Human Technopole, Milan, Italy
| | - Gina M Peloso
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Hesam Dashti
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian R Ferolito
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
| | - Daniel Golden
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrea R V R Horimoto
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eric H Farber-Eger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn Stanton Wells
- Departments of Medicine (Cardiology), Biomedical Informatics and Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giorgio Bini
- Istituto Italiano di Tecnologia, CHT@Erzelli, Genova, Italy
- Dipartimento di Fisica Via Dodecaneso, Genova, Italy
| | | | - Gian Gaetano Tartaglia
- Istituto Italiano di Tecnologia, CHT@Erzelli, Genova, Italy
- ICREA - Institució Catalana de Recerca I Estudis Avançats, Barcelona, Spain
| | - Nicole M Kosik
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Kelly Cho
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew R Leach
- Department of Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - John Whittaker
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Nay Aung
- William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Alexandre C Pereira
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil
| | - Juan P Casas
- Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA
| | - Jacob Joseph
- Cardiology Section, VA Providence Healthcare System, Providence, RI, USA.
- Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
| |
Collapse
|
24
|
Gallagher CS, Ginsburg GS, Musick A. Biobanking with genetics shapes precision medicine and global health. Nat Rev Genet 2025; 26:191-202. [PMID: 39567741 DOI: 10.1038/s41576-024-00794-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2024] [Indexed: 11/22/2024]
Abstract
Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.
Collapse
Affiliation(s)
- C Scott Gallagher
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Geoffrey S Ginsburg
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Anjené Musick
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
25
|
Dand N, Stuart PE, Bowes J, Ellinghaus D, Nititham J, Saklatvala JR, Teder-Laving M, Thomas LF, Traks T, Uebe S, Assmann G, Baudry D, Behrens F, Billi AC, Brown MA, Burkhardt H, Capon F, Chung R, Curtis CJ, Duckworth M, Ellinghaus E, FitzGerald O, Gerdes S, Griffiths CEM, Gulliver S, Helliwell PS, Ho P, Hoffmann P, Holmen OL, Huang ZM, Hveem K, Jadon D, Köhm M, Kraus C, Lamacchia C, Lee SH, Ma F, Mahil SK, McHugh N, McManus R, Modalsli EH, Nissen MJ, Nöthen M, Oji V, Oksenberg JR, Patrick MT, Perez White BE, Ramming A, Rech J, Rosen C, Sarkar MK, Schett G, Schmidt B, Tejasvi T, Traupe H, Voorhees JJ, Wacker EM, Warren RB, Wasikowski R, Weidinger S, Wen X, Zhang Z, Barton A, Chandran V, Esko T, Foerster J, Franke A, Gladman DD, Gudjonsson JE, Gulliver W, Hüffmeier U, Kingo K, Kõks S, Liao W, Løset M, Mägi R, Nair RP, Rahman P, Reis A, Smith CH, Di Meglio P, Barker JN, Tsoi LC, Simpson MA, Elder JT. GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets. Nat Commun 2025; 16:2051. [PMID: 40021644 PMCID: PMC11871359 DOI: 10.1038/s41467-025-56719-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/28/2025] [Indexed: 03/03/2025] Open
Abstract
Psoriasis is a common, debilitating immune-mediated skin disease. Genetic studies have identified biological mechanisms of psoriasis risk, including those targeted by effective therapies. However, the genetic liability to psoriasis is not fully explained by variation at robustly identified risk loci. To refine the genetic map of psoriasis susceptibility we meta-analysed 18 GWAS comprising 36,466 cases and 458,078 controls and identified 109 distinct psoriasis susceptibility loci, including 46 that have not been previously reported. These include susceptibility variants at loci in which the therapeutic targets IL17RA and AHR are encoded, and deleterious coding variants supporting potential new drug targets (including in STAP2, CPVL and POU2F3). We conducted a transcriptome-wide association study to identify regulatory effects of psoriasis susceptibility variants and cross-referenced these against single cell expression profiles in psoriasis-affected skin, highlighting roles for the transcriptional regulation of haematopoietic cell development and epigenetic modulation of interferon signalling in psoriasis pathobiology.
Collapse
Grants
- R01 ES033634 NIEHS NIH HHS
- R01AR050511, R01AR054966, R01AR063611, R01AR065183 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- BRC_1215_20006, NIHR302258, NIHR203308, BRC-1215-20014 DH | National Institute for Health Research (NIHR)
- 980 Maudsley Charity
- RG2/10, ST1/19, ST3/20 Psoriasis Association
- EXC 2167-390884018, CRC1181-2/project A05 Deutsche Forschungsgemeinschaft (German Research Foundation)
- STR130505 Guy's and St Thomas' Charity
- K01 AR072129, P30 AR075043, UC2 AR081033, R01AR042742 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- K08 AR078251 NIAMS NIH HHS
- P30 AR075043 NIAMS NIH HHS
- K01 AR072129 NIAMS NIH HHS
- 814364 National Psoriasis Foundation (NPF)
- R01 AR042742 NIAMS NIH HHS
- PUT1465, PRG1189, PRG1911, PRG1291 Eesti Teadusagentuur (Estonian Research Council)
- 2014-2020.4.01.15-0012 EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj)
- U01AI119125 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- LF-OC-22-001033 LEO Pharma Research Foundation
- 821511 Innovative Medicines Initiative (IMI)
- RG-1611-26299 National Multiple Sclerosis Society (National MS Society)
- MR/S003126/1 RCUK | Medical Research Council (MRC)
- U01 AI119125 NIAID NIH HHS
- R01ES033634, R35GM138121, K08 AR078251, R01AR065174 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- R01 AR054966 NIAMS NIH HHS
- R01 AR050511 NIAMS NIH HHS
- R01 AR065174 NIAMS NIH HHS
- R35 GM138121 NIGMS NIH HHS
- 01EC1407A, 01EC1401C Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research)
- SI 236/8-1, SI236/9-1, ER 155/6-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- UC2 AR081033 NIAMS NIH HHS
- R01 AR065183 NIAMS NIH HHS
- R01 AR063611 NIAMS NIH HHS
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- Versus Arthritis - grant reference number 21754 Additional funding support from the following bodies are also acknowledged, as detailed in the funding section of the manuscript: Ann Arbor Veterans Hospital; Babcock Memorial Trust; Cambridge Arthritis Research Endeavour (CARE); Dermatology Foundation; Faculty of Medicine and Health Sciences, NTNU; German Centre for Neurodegenerative Disorders (DZNE), Bonn; German Ministry of Education and Science; Heinz Nixdorf Foundation (Germany); Joint Research Committee between St Olav’s Hospital and the Faculty of Medicine and Health Sciences, NTNU; Krembil Foundation; Liaison Committee for Education, Research, and Innovation in Central Norway; The Michael J. Fox Foundation; MSWA; National Institutes of Health; Perron Institute for Neurological and Translational Science; Pfizer Chair Research Award in Rheumatology; Research Council of Norway; Shake It Up Australia; Stiftelsen Kristian Gerhard Jebsen; Taubman Medical Research Institute; University of Michigan
Collapse
Affiliation(s)
- Nick Dand
- Department of Medical & Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Health Data Research UK, London, UK
| | - Philip E Stuart
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John Bowes
- Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre, The University of Manchester, Manchester, UK
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Joanne Nititham
- Deparment of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Jake R Saklatvala
- Department of Medical & Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | | | - Laurent F Thomas
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Tanel Traks
- Department of Dermatology and Venereology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Steffen Uebe
- Institute of Human Genetics, Universitätsklinikum Erlangen, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Gunter Assmann
- RUB University Hospital JWK Minden, Department of Rheumatology, Minden, Germany
- Jose-Carreras Centrum for Immuno- and Gene Therapy, University of Saarland Medical School, Homburg, Germany
| | - David Baudry
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Frank Behrens
- Division of Translational Rheumatology, Immunology - Inflammation Medicine, University Hospital, Goethe University, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
- Fraunhofer Cluster of Excellence Immune-mediated Diseases CIMD, Frankfurt am Main, Germany
- Division of Rheumatology, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Allison C Billi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matthew A Brown
- Department of Medical & Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Genomics England, Canary Wharf, London, UK
| | - Harald Burkhardt
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
- Fraunhofer Cluster of Excellence Immune-mediated Diseases CIMD, Frankfurt am Main, Germany
- Division of Rheumatology, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Francesca Capon
- Department of Medical & Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Raymond Chung
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Charles J Curtis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Michael Duckworth
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Eva Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Oliver FitzGerald
- UCD School of Medicine and Medical Sciences and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Sascha Gerdes
- Department of Dermatology, Venereology and Allergy, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Christopher E M Griffiths
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Centre for Dermatology Research, University of Manchester, NIHR Manchester Biomedical Research Centre, Manchester, UK
- Department of Dermatology, King's College Hospital NHS Foundation Trust, London, UK
| | | | - Philip S Helliwell
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Pauline Ho
- Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre, The University of Manchester, Manchester, UK
- The Kellgren Centre for Rheumatology, Manchester University NHS Foundation Trust, Manchester, UK
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Oddgeir L Holmen
- HUNT Research Centre, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Zhi-Ming Huang
- Deparment of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Deepak Jadon
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Michaela Köhm
- Division of Translational Rheumatology, Immunology - Inflammation Medicine, University Hospital, Goethe University, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
- Fraunhofer Cluster of Excellence Immune-mediated Diseases CIMD, Frankfurt am Main, Germany
- Division of Rheumatology, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Cornelia Kraus
- Institute of Human Genetics, Universitätsklinikum Erlangen, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Céline Lamacchia
- Division of Rheumatology, Geneva University Hospital, Geneva, Switzerland
| | - Sang Hyuck Lee
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Feiyang Ma
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Satveer K Mahil
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' National Health Service (NHS) Foundation Trust, London, UK
| | - Neil McHugh
- Department of Life Sciences, University of Bath, Bath, UK
| | - Ross McManus
- Department of Clinical Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Ellen H Modalsli
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Dermatology, Clinic of Orthopedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Michael J Nissen
- Division of Rheumatology, Geneva University Hospital, Geneva, Switzerland
| | - Markus Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Vinzenz Oji
- Department of Dermatology, University of Münster, Münster, Germany
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Andreas Ramming
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jürgen Rech
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cheryl Rosen
- Division of Dermatology, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Mrinal K Sarkar
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Börge Schmidt
- Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Trilokraj Tejasvi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA
| | - Heiko Traupe
- Department of Dermatology, University of Münster, Münster, Germany
| | - John J Voorhees
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Eike Matthias Wacker
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Richard B Warren
- Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Centre for Dermatology Research, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M6 8HD, UK
| | - Rachael Wasikowski
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Stephan Weidinger
- Department of Dermatology, Venereology and Allergy, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Xiaoquan Wen
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Zhaolin Zhang
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Anne Barton
- Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre, The University of Manchester, Manchester, UK
- The Kellgren Centre for Rheumatology, Manchester University NHS Foundation Trust, Manchester, UK
| | - Vinod Chandran
- Schroeder Arthritis Institute, Krembil Research Institute and Toronto Western Hospital, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John Foerster
- College of Medicine, Dentistry, and Nursing, University of Dundee, Dundee, UK
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Dafna D Gladman
- Schroeder Arthritis Institute, Krembil Research Institute and Toronto Western Hospital, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Johann E Gudjonsson
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Wayne Gulliver
- Newlab Clinical Research Inc, St. John's, NL, Canada
- Department of Dermatology, Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Ulrike Hüffmeier
- Institute of Human Genetics, Universitätsklinikum Erlangen, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Külli Kingo
- Department of Dermatology and Venereology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Dermatology Clinic, Tartu University Hospital, Tartu, Estonia
| | - Sulev Kõks
- Perron Institute for Neurological and Translational Science, Nedlands, WA, 6009, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Health Futures Institute, Murdoch University, Perth, WA, 6150, Australia
| | - Wilson Liao
- Deparment of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Dermatology, Clinic of Orthopedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Rajan P Nair
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Proton Rahman
- Memorial University of Newfoundland, St. John's, NL, Canada
| | - André Reis
- Institute of Human Genetics, Universitätsklinikum Erlangen, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Catherine H Smith
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' National Health Service (NHS) Foundation Trust, London, UK
| | - Paola Di Meglio
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Jonathan N Barker
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' National Health Service (NHS) Foundation Trust, London, UK
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michael A Simpson
- Department of Medical & Molecular Genetics, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, UK.
| | - James T Elder
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA.
| |
Collapse
|
26
|
Jin J, Li B, Wang X, Yang X, Li Y, Wang R, Ye C, Shu J, Fan Z, Xue F, Ge T, Ritchie MD, Pasaniuc B, Wojcik G, Zhao B. PennPRS: a centralized cloud computing platform for efficient polygenic risk score training in precision medicine. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.02.07.25321875. [PMID: 39990574 PMCID: PMC11844566 DOI: 10.1101/2025.02.07.25321875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2025]
Abstract
Polygenic risk scores (PRS) are becoming increasingly vital for risk prediction and stratification in precision medicine. However, PRS model training presents significant challenges for broader adoption of PRS, including limited access to computational resources, difficulties in implementing advanced PRS methods, and availability and privacy concerns over individual-level genetic data. Cloud computing provides a promising solution with centralized computing and data resources. Here we introduce PennPRS (https://pennprs.org), a scalable cloud computing platform for online PRS model training in precision medicine. We developed novel pseudo-training algorithms for multiple PRS methods and ensemble approaches, enabling model training without requiring individual-level data. These methods were rigorously validated through extensive simulations and large-scale real data analyses involving over 6,000 phenotypes across various data sources. PennPRS supports online single- and multi-ancestry PRS training with seven methods, allowing users to upload their own data or query from more than 27,000 datasets in the GWAS Catalog, submit jobs, and download trained PRS models. Additionally, we applied our pseudo-training pipeline to train PRS models for over 8,000 phenotypes and made their PRS weights publicly accessible. In summary, PennPRS provides a novel cloud computing solution to improve the accessibility of PRS applications and reduce disparities in computational resources for the global PRS research community.
Collapse
Affiliation(s)
- Jin Jin
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Center for Eye-Brain Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Xiyao Wang
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Ruofan Wang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenglong Ye
- Department of Statistics, University of Kentucky, Lexington, KY 40536, USA
| | - Juan Shu
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fei Xue
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bogdan Pasaniuc
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Bingxin Zhao
- Penn Center for Eye-Brain Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
27
|
Davis CN, Khan Y, Toikumo S, Jinwala Z, Boomsma DI, Levey DF, Gelernter J, Kember RL, Kranzler HR. Integrating HiTOP and RDoC Frameworks Part I: Genetic Architecture of Externalizing and Internalizing Psychopathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2024.04.06.24305166. [PMID: 38645045 PMCID: PMC11030494 DOI: 10.1101/2024.04.06.24305166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP). Methods We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Traits included clinical (e.g., major depressive disorder, alcohol use disorder) and subclinical measures (e.g., risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors. Results A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits. Conclusions The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.
Collapse
Affiliation(s)
- Christal N. Davis
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Yousef Khan
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Zeal Jinwala
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Dorret I. Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, The Netherlands and Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rachel L. Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Henry R. Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| |
Collapse
|
28
|
Adli M, Przybyla L, Burdett T, Burridge PW, Cacheiro P, Chang HY, Engreitz JM, Gilbert LA, Greenleaf WJ, Hsu L, Huangfu D, Hung LH, Kundaje A, Li S, Parkinson H, Qiu X, Robson P, Schürer SC, Shojaie A, Skarnes WC, Smedley D, Studer L, Sun W, Vidović D, Vierbuchen T, White BS, Yeung KY, Yue F, Zhou T. MorPhiC Consortium: towards functional characterization of all human genes. Nature 2025; 638:351-359. [PMID: 39939790 DOI: 10.1038/s41586-024-08243-w] [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: 10/31/2023] [Accepted: 10/17/2024] [Indexed: 02/14/2025]
Abstract
Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions.
Collapse
Affiliation(s)
- Mazhar Adli
- Robert H. Lurie Comprehensive Cancer Center, Department of Obstetrics and Gynecology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.
| | - Laralynne Przybyla
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
| | - Tony Burdett
- Omics Section, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, UK
| | - Paul W Burridge
- Department of Pharmacology, Center for Pharmacogenomics, Northwestern University, Feinberg School of Medicine, Evanston, IL, USA
| | - Pilar Cacheiro
- William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK
| | - Howard Y Chang
- Department of Dermatology, Stanford University, Stanford, CA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University, Stanford, CA, USA
- Basic Science and Engineering (BASE) Initiative, Stanford University, Stanford, CA, USA
| | - Luke A Gilbert
- Department of Urology, University of California, San Francisco, CA, USA
| | | | - Li Hsu
- Department of Biostatistics, Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Danwei Huangfu
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
| | - Ling-Hong Hung
- School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA, USA
| | - Anshul Kundaje
- Departments of Genetics and Computer Science, Stanford University, Stanford, CA, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Helen Parkinson
- Knowledge Management Section, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, UK
| | - Xiaojie Qiu
- Basic Science and Engineering (BASE) Initiative, Stanford University, Stanford, CA, USA
- Departments of Genetics and Computer Science, Stanford University, Stanford, CA, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Stephan C Schürer
- Molecular and Cellular Pharmacology; Sylvester Comprehensive Cancer Center, University of Miami, Coral Gables, FL, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK
| | - Lorenz Studer
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
| | - Wei Sun
- Department of Biostatistics, Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Dušica Vidović
- Molecular and Cellular Pharmacology; Sylvester Comprehensive Cancer Center, University of Miami, Coral Gables, FL, USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
| | - Brian S White
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Ka Yee Yeung
- School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA, USA
| | - Feng Yue
- Department of Biochemistry and Molecular Genetics, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Ting Zhou
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
| |
Collapse
|
29
|
Loya H, Kalantzis G, Cooper F, Palamara PF. A scalable variational inference approach for increased mixed-model association power. Nat Genet 2025; 57:461-468. [PMID: 39789286 PMCID: PMC11821521 DOI: 10.1038/s41588-024-02044-7] [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: 10/10/2023] [Accepted: 11/27/2024] [Indexed: 01/12/2025]
Abstract
The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.
Collapse
Affiliation(s)
- Hrushikesh Loya
- Department of Statistics, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Georgios Kalantzis
- Department of Statistics, University of Oxford, Oxford, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Fergus Cooper
- Doctoral Training Centre, University of Oxford, Oxford, UK
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Centre for Human Genetics, University of Oxford, Oxford, UK.
| |
Collapse
|
30
|
Burgess S, Cronjé HT, deGoma E, Chyung Y, Gill D. Human Genetic Evidence to Inform Clinical Development of IL-6 Signaling Inhibition for Abdominal Aortic Aneurysm. Arterioscler Thromb Vasc Biol 2025; 45:323-331. [PMID: 39633572 PMCID: PMC7617413 DOI: 10.1161/atvbaha.124.321988] [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: 10/11/2024] [Accepted: 11/21/2024] [Indexed: 12/07/2024]
Abstract
BACKGROUND Abdominal aortic aneurysm (AAA) represents a significant cause of mortality, yet no medical therapies have proven efficacious. The aim of the current study was to leverage human genetic evidence to inform clinical development of IL-6 (interleukin-6) signaling inhibition for the treatment of AAA. METHODS Associations of rs2228145, a missense variant in the IL6R gene region, are expressed per additional copy of the C allele, corresponding to the genetically predicted effect of IL-6 signaling inhibition. We consider genetic associations with AAA risk in the AAAgen consortium (39 221 cases and 1 086 107 controls) and UK Biobank (1963 cases and 365 680 controls). To validate against known effects of IL-6 signaling inhibition, we present associations with rheumatoid arthritis, polymyalgia rheumatica, and severe COVID-19. To explore mechanism specificity, we present associations with thoracic aortic aneurysm, intracranial aneurysm, and coronary artery disease. We further explored genetic associations in clinically relevant subgroups of the population. RESULTS We observed strong genetic associations with AAA risk in the AAAgen consortium, UK Biobank, and FinnGen (odds ratios: 0.91 [95% CI, 0.90-0.92], P=4×10-30; 0.90 [95% CI, 0.84-0.96], P=0.001; and 0.86 [95% CI, 0.82-0.91], P=7×10-9, respectively). The association was similar for fatal AAA but with greater uncertainty due to the lower number of events. The association with AAA was of greater magnitude than associations with coronary artery disease and even rheumatological disorders for which IL-6 inhibitors have been approved. No strong associations were observed with thoracic aortic aneurysm or intracranial aneurysm. Associations attenuated toward the null in populations with concomitant rheumatological or connective tissue disease. CONCLUSIONS Inhibition of IL-6 signaling is a promising strategy for treating AAA but not other types of aneurysmal disease. These findings serve to help inform clinical development of IL-6 signaling inhibition for AAA treatment.
Collapse
Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Sequoia Genetics, London, United Kingdom
| | - Héléne T. Cronjé
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Sequoia Genetics, London, United Kingdom
| | | | | | | |
Collapse
|
31
|
Schipper M, de Leeuw CA, Maciel BAPC, Wightman DP, Hubers N, Boomsma DI, O'Donovan MC, Posthuma D. Prioritizing effector genes at trait-associated loci using multimodal evidence. Nat Genet 2025; 57:323-333. [PMID: 39930082 DOI: 10.1038/s41588-025-02084-7] [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: 01/29/2024] [Accepted: 01/08/2025] [Indexed: 02/14/2025]
Abstract
Genome-wide association studies (GWAS) yield large numbers of genetic loci associated with traits and diseases. Predicting the effector genes that mediate these locus-trait associations remains challenging. Here we present the FLAMES (fine-mapped locus assessment model of effector genes) framework, which predicts the most likely effector gene in a locus. FLAMES creates machine learning predictions from biological data linking single-nucleotide polymorphisms to genes, and then evaluates these scores together with gene-centric evidence of convergence of the GWAS signal in functional networks. We benchmark FLAMES on gene-locus pairs derived by expert curation, rare variant implication and domain knowledge of molecular traits. We demonstrate that combining single-nucleotide-polymorphism-based and convergence-based modalities outperforms prioritization strategies using a single line of evidence. Applying FLAMES, we resolve the FSHB locus in the GWAS for dizygotic twinning and further leverage this framework to find schizophrenia risk genes that converge with rare coding evidence and are relevant in different stages of life.
Collapse
Affiliation(s)
- Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernardo A P C Maciel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) research institute, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) research institute, Amsterdam, The Netherlands
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| |
Collapse
|
32
|
Cheng Z, Wu J, Xu C, Yan X. Exploring the Causal Relationship Between Frailty and Chronic Obstructive Pulmonary Disease: Insights From Bidirectional Mendelian Randomization and Mediation Analysis. Int J Chron Obstruct Pulmon Dis 2025; 20:193-205. [PMID: 39881812 PMCID: PMC11776522 DOI: 10.2147/copd.s501635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Accepted: 01/19/2025] [Indexed: 01/31/2025] Open
Abstract
Background Observational studies have underscored a robust association between frailty and chronic obstructive pulmonary disease (COPD), yet the causality remains equivocal. Methods This study employed bidirectional two-sample Mendelian randomization (MR) analysis. Univariable MR investigated the causal relationship between frailty and COPD. Genetic correlation was assessed using linkage disequilibrium score (LDSC) regression. Multivariable MR and mediation analysis explored the influence of various confounders and their mediating effects. The primary analytic approach was inverse variance weighted (IVW). Results LDSC analysis revealed moderate genetic correlations between frailty and Global Biobank Meta-Analysis Initiative (GBMI) COPD (rg = 0.643, P = 6.66×10-62) as well as FinnGen COPD (rg = 0.457, P = 8.20×10-28). IVW analysis demonstrated that frailty was associated with increased risk of COPD in both the GBMI cohort (95% CI, 1.475 to 2.158; P = 2.40×10-9) and the FinnGen database (1.411 to 2.434; 9.02×10-6). Concurrently, COPD was identified as a susceptibility factor for frailty (P < 0.05). These consistent findings persisted after adjustment for potential confounders in MVMR. Additionally, mediation analysis revealed that walking pace mediated 19.11% and 15.40% of the impact of frailty on COPD risk, and 17.58% and 23.26% of the effect of COPD on frailty risk in the GBMI and FinnGen cohorts, respectively. Conclusion This study has strengthened the current evidence affirming a reciprocal causal relationship between frailty and COPD, highlighting walking pace as a pivotal mediator.
Collapse
Affiliation(s)
- Zewen Cheng
- Department of Thoracic Surgery, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, Jiangsu, 215000, People’s Republic of China
| | - Jian Wu
- Department of Thoracic Surgery, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, Jiangsu, 215000, People’s Republic of China
| | - Chun Xu
- Department of Cardio-Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215000, People’s Republic of China
| | - Xiaokun Yan
- Department of Thoracic Surgery, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, Jiangsu, 215000, People’s Republic of China
| |
Collapse
|
33
|
Li H, Zeng J, Snyder MP, Zhang S. Modeling gene interactions in polygenic prediction via geometric deep learning. Genome Res 2025; 35:178-187. [PMID: 39562137 PMCID: PMC11789630 DOI: 10.1101/gr.279694.124] [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: 06/14/2024] [Accepted: 11/14/2024] [Indexed: 11/21/2024]
Abstract
Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.
Collapse
Affiliation(s)
- Han Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, 310030, Zhejiang, China;
| | - Michael P Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, California 94304, USA;
| | - Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, Florida 32603, USA;
- Departments of Biostatistics & Biomedical Engineering, UF Genetics Institute, University of Florida, Gainesville, Florida 32603, USA
| |
Collapse
|
34
|
Chen Z, Tang M, Wang N, Liu J, Tan X, Ma H, Luo J, Xie K. Genetic variation reveals the therapeutic potential of BRSK2 in idiopathic pulmonary fibrosis. BMC Med 2025; 23:22. [PMID: 39838395 PMCID: PMC11752817 DOI: 10.1186/s12916-025-03848-y] [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: 08/07/2024] [Accepted: 01/07/2025] [Indexed: 01/23/2025] Open
Abstract
BACKGROUND Current research underscores the need to better understand the pathogenic mechanisms and treatment strategies for idiopathic pulmonary fibrosis (IPF). This study aimed to identify key targets involved in the progression of IPF. METHODS We employed Mendelian randomization (MR) with three genome-wide association studies and four quantitative trait loci datasets to identify key driver genes for IPF. Prioritized targets were evaluated for respiratory insufficiency and transplant-free survival. The therapeutic efficacy of the core gene was validated in cellular and animal models. Additionally, we conducted a comprehensive evaluation of therapeutic value, pathogenic mechanisms, and safety through phenome-wide association study (PheWAS), mediation analysis, transcriptomic analyses, shared causal variant exploration, DNA methylation MR, and protein interactions. RESULTS Multiple MR results revealed that BRSK2 has a significant pathogenic impact on IPF at both transcriptional and translational levels, with a lung tissue-specific association (OR = 1.596; CI, 1.300-1.961; Pval = 8.290 × 10 - 6). BRSK2 was associated with IPF progression driven by high-risk factors, with mediation effects ranging from 34.452 to 69.665%. Elevated BRSK2 expression in peripheral blood mononuclear cells correlated with reduced pulmonary function, while increased circulating BRSK2 levels suggested respiratory failure and shorter transplant-free survival in IPF patients. BRSK2 silencing attenuated lung fibrosis progression in cellular and animal models. Transcriptomic integration identified PSMB1, CTSD, and CTSH as significant downstream effectors of BRSK2, with PSMB1 showing robust shared causal variant support (PPH4 = 0.800). Colocalization analysis and phenotype scan deepened the pathogenic association of BRSK2 with IPF, while methylation MR analysis highlighted the critical role of epigenetic regulation in BRSK2-driven IPF pathogenesis. PheWAS revealed no significant drug-related toxicities for BRSK2, and its therapeutic potential was further underscored by protein interaction analyses. CONCLUSIONS BRSK2 is identified as a critical pathogenic factor in IPF, with strong potential as a therapeutic target. Future studies should focus on its translational implications and the development of targeted therapies to improve patient outcomes.
Collapse
Affiliation(s)
- Zhe Chen
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Mingyang Tang
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Nan Wang
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Jiangjiang Liu
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Xiaoyan Tan
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Haitao Ma
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China.
| | - Jing Luo
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Kai Xie
- Department of Cardiothoracic Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China.
| |
Collapse
|
35
|
De Walsche A, Vergne A, Rincent R, Roux F, Nicolas S, Welcker C, Mezmouk S, Charcosset A, Mary-Huard T. metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. PLoS Genet 2025; 21:e1011553. [PMID: 39792927 PMCID: PMC11756807 DOI: 10.1371/journal.pgen.1011553] [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: 08/07/2024] [Revised: 01/23/2025] [Accepted: 12/23/2024] [Indexed: 01/12/2025] Open
Abstract
Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.
Collapse
Affiliation(s)
- Annaïg De Walsche
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
- MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay, Palaiseau, France
| | | | - Renaud Rincent
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Fabrice Roux
- LIPME, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | - Stéphane Nicolas
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Claude Welcker
- LEPSE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | - Alain Charcosset
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
- MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay, Palaiseau, France
| |
Collapse
|
36
|
Guare LA, Das J, Caruth L, Setia-Verma S. Social Determinants of Health and Lifestyle Risk Factors Modulate Genetic Susceptibility for Women's Health Outcomes. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2025; 30:296-313. [PMID: 39670378 PMCID: PMC11658798 DOI: 10.1142/9789819807024_0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/16/2025]
Abstract
Women's health conditions are influenced by both genetic and environmental factors. Understanding these factors individually and their interactions is crucial for implementing preventative, personalized medicine. However, since genetics and environmental exposures, particularly social determinants of health (SDoH), are correlated with race and ancestry, risk models without careful consideration of these measures can exacerbate health disparities. We focused on seven women's health disorders in the All of Us Research Program: breast cancer, cervical cancer, endometriosis, ovarian cancer, preeclampsia, uterine cancer, and uterine fibroids. We computed polygenic risk scores (PRSs) from publicly available weights and tested the effect of the PRSs on their respective phenotypes as well as any effects of genetic risk on age at diagnosis. We next tested the effects of environmental risk factors (BMI, lifestyle measures, and SDoH) on age at diagnosis. Finally, we examined the impact of environmental exposures in modulating genetic risk by stratified logistic regressions for different tertiles of the environment variables, comparing the effect size of the PRS. Of the twelve sets of weights for the seven conditions, nine were significantly and positively associated with their respective phenotypes. None of the PRSs was associated with different ages at diagnoses in the time-to-event analyses. The highest environmental risk group tended to be diagnosed earlier than the low and medium-risk groups. For example, the cases of breast cancer, ovarian cancer, uterine cancer, and uterine fibroids in highest BMI tertile were diagnosed significantly earlier than the low and medium BMI groups, respectively). PRS regression coefficients were often the largest in the highest environment risk groups, showing increased susceptibility to genetic risk. This study's strengths include the diversity of the All of Us study cohort, the consideration of SDoH themes, and the examination of key risk factors and their interrelationships. These elements collectively underscore the importance of integrating genetic and environmental data to develop more precise risk models, enhance personalized medicine, and ultimately reduce health disparities.
Collapse
Affiliation(s)
- Lindsay A Guare
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia, PA 19104, USA,
| | - Jagyashila Das
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia, PA 19104, USA,
| | - Lannawill Caruth
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia, PA 19104, USA,
| | - Shefali Setia-Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia, PA 19104, USA,
| |
Collapse
|
37
|
Douville NJ, Bastarache L, He J, Wu KHH, Vanderwerff B, Bertucci-Richter E, Hornsby WE, Lewis A, Jewell ES, Kheterpal S, Shah N, Mathis M, Engoren MC, Douville CB, Surakka I, Willer C, Kertai MD. Polygenic Score for the Prediction of Postoperative Nausea and Vomiting: A Retrospective Derivation and Validation Cohort Study. Anesthesiology 2025; 142:52-71. [PMID: 39250560 PMCID: PMC11620327 DOI: 10.1097/aln.0000000000005214] [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: 07/21/2023] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
BACKGROUND Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction after surgery. Because traditional risk factors do not completely explain variability in risk, this study hypothesized that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV. METHODS Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine (Ann Arbor, Michigan) and Vanderbilt University Medical Center (Nashville, Tennessee) were studied. PONV was defined as nausea or emesis occurring and documented in the postanesthesia care unit. In the discovery phase, genome-wide association studies were performed on each genetic cohort, and the results were meta-analyzed. Next, the polygenic phase assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (c-statistic) and net reclassification index. RESULTS Of 64,523 total patients, 5,703 developed PONV (8.8%). The study identified 46 genetic variants exceeding the threshold of P < 1 × 10-5, occurring with minor allele frequency greater than 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex (adjusted odds ratio, 1.027 per SD increase in overall genetic risk; 95% CI, 1.001 to 1.053; P = 0.044), a model based on known clinical risks (adjusted odds ratio, 1.029; 95% CI, 1.003 to 1.055; P = 0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (adjusted odds ratio, 1.029; 95% CI, 1.002 to 1.056; P = 0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic, 0.616 compared to 0.613; P = 0.028) and improved net reclassification of 4.6% of cases. CONCLUSIONS Standardized polygenic risk was associated with PONV in all three of the study's models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score greater than 1 SD above the mean has 2 to 3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV or motion sickness (55%), having a history of migraines (17%), or being female (83%) and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful. EDITOR’S PERSPECTIVE
Collapse
Affiliation(s)
- Nicholas J. Douville
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan; and Institute of Healthcare Policy and Innovation and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | | | | | | | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Sachin Kheterpal
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | - Nirav Shah
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | - Michael Mathis
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan; and Institute of Healthcare Policy and Innovation and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Milo C. Engoren
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | | | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Miklos D. Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
| |
Collapse
|
38
|
Delabays B, De Paoli C, Miller-Nesbitt A, Mooser V. Genetically Enriched Clinical Trials for Precision Development of Noncancer Therapeutics: A Scoping Review. Annu Rev Pharmacol Toxicol 2025; 65:149-167. [PMID: 39348854 DOI: 10.1146/annurev-pharmtox-031524-021631] [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] [Indexed: 10/02/2024]
Abstract
Genetically driven clinical trial enrichment has been proposed to accelerate and reduce the cost of developing new therapeutics. Usage of this approach has not been comprehensively reviewed. We searched Ovid MEDLINE, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov, and WHO ICTRP for articles published between 2010 and 2023. Excluding absorption, distribution, metabolism, and elimination pharmacogenetic studies and anti-infectives, we found 95 completed, 4 terminated, and 22 ongoing prospective genetically enriched trials on 110 drugs for 48 nononcology, nonrare syndromic indications. Trial sizes ranged from 4 to 6,147 participants (median 72) and covered numerous disease areas, particularly neurology (30), metabolism (22), and psychiatry (17). Fifty-six completed studies (60%) met their primary end point. Overall, this scoping review demonstrates that genetically enriched trials are feasible and scalable across disease areas and provide critical information for further development, or attrition, of investigational drugs. Large, appropriately designed disease-, hospital-, or population-based biobanks will undoubtedly facilitate this type of precision drug development approach.
Collapse
Affiliation(s)
- Benoît Delabays
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montréal, Quebec, Canada;
| | - Chiara De Paoli
- School of Medicine, University of Eastern Piedmont, Novara, Italy
| | | | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montréal, Quebec, Canada;
| |
Collapse
|
39
|
Shao M, Chen K, Zhang S, Tian M, Shen Y, Cao C, Gu N. Multiome-wide Association Studies: Novel Approaches for Understanding Diseases. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae077. [PMID: 39471467 PMCID: PMC11630051 DOI: 10.1093/gpbjnl/qzae077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/06/2024] [Accepted: 10/23/2024] [Indexed: 11/01/2024]
Abstract
The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.
Collapse
Affiliation(s)
- Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Kaiyang Chen
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Shuting Zhang
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Min Tian
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Yan Shen
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Ning Gu
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
- Nanjing Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China
| |
Collapse
|
40
|
Thompson MD, Reiner-Link D, Berghella A, Rana BK, Rovati GE, Capra V, Gorvin CM, Hauser AS. G protein-coupled receptor (GPCR) pharmacogenomics. Crit Rev Clin Lab Sci 2024; 61:641-684. [PMID: 39119983 DOI: 10.1080/10408363.2024.2358304] [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: 06/15/2023] [Revised: 09/03/2023] [Accepted: 05/18/2024] [Indexed: 08/10/2024]
Abstract
The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.
Collapse
Affiliation(s)
- Miles D Thompson
- Krembil Brain Institute, Toronto Western Hospital, Toronto, Ontario, Canada
| | - David Reiner-Link
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alessandro Berghella
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brinda K Rana
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - G Enrico Rovati
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Valerie Capra
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Caroline M Gorvin
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, United Kingdom
| | - Alexander S Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
41
|
Shine BK, Choi JE, Park YJ, Hong KW. The Genetic Variants Influencing Hypertension Prevalence Based on the Risk of Insulin Resistance as Assessed Using the Metabolic Score for Insulin Resistance (METS-IR). Int J Mol Sci 2024; 25:12690. [PMID: 39684400 DOI: 10.3390/ijms252312690] [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: 10/21/2024] [Revised: 11/24/2024] [Accepted: 11/25/2024] [Indexed: 12/18/2024] Open
Abstract
Insulin resistance is a major indicator of cardiovascular diseases, including hypertension. The Metabolic Score for Insulin Resistance (METS-IR) offers a simplified and cost-effective way to evaluate insulin resistance. This study aimed to identify genetic variants associated with the prevalence of hypertension stratified by METS-IR score levels. Data from the Korean Genome and Epidemiology Study (KoGES) were analyzed. The METS-IR was calculated using the following formula: ln [(2 × fasting blood glucose (FBG) + triglycerides (TG)) × body mass index (BMI)]/ ln [high-density lipoprotein cholesterol (HDL-C)]. The participants were divided into tertiles 1 (T1) and 3 (T3) based on their METS-IR scores. Genome-wide association studies (GWAS) were performed for hypertensive cases and non-hypertensive controls within these tertile groups using logistic regression adjusted for age, sex, and lifestyle factors. Among the METS-IR tertile groups, 3517 of the 19,774 participants (17.8%) at T1 had hypertension, whereas 8653 of the 20,374 participants (42.5%) at T3 had hypertension. A total of 113 single-nucleotide polymorphisms (SNPs) reached the GWAS significance threshold (p < 5 × 10-8) in at least one tertile group, mapping to six distinct genetic loci. Notably, four loci, rs11899121 (chr2p24), rs7556898 (chr2q24.3), rs17249754 (ATP2B1), and rs1980854 (chr20p12.2), were significantly associated with hypertension in the high-METS-score group (T3). rs10857147 (FGF5) was significant in both the T1 and T3 groups, whereas rs671 (ALDH2) was significant only in the T1 group. The GWASs identified six genetic loci significantly associated with hypertension, with distinct patterns across METS-IR tertiles, highlighting the role of metabolic context in genetic susceptibility. These findings underscore critical genetic factors influencing hypertension prevalence and provide insights into the metabolic-genetic interplay underlying this condition.
Collapse
Affiliation(s)
- Bo-Kyung Shine
- Department of Family Medicine, Medical Center, Dong-A University, Busan 49201, Republic of Korea
| | - Ja-Eun Choi
- Institute of Advanced Technology, Theragen Health Co., Ltd., Seongnam 13493, Republic of Korea
| | - Young-Jin Park
- Department of Family Medicine, Medical Center, Dong-A University, Busan 49201, Republic of Korea
| | - Kyung-Won Hong
- Institute of Advanced Technology, Theragen Health Co., Ltd., Seongnam 13493, Republic of Korea
| |
Collapse
|
42
|
Chen G, Jin Y, Chu C, Zheng Y, Yang C, Chen Y, Zhu X. A cross-tissue transcriptome-wide association study reveals GRK4 as a novel susceptibility gene for COPD. Sci Rep 2024; 14:28438. [PMID: 39558015 PMCID: PMC11574126 DOI: 10.1038/s41598-024-80122-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: 08/20/2024] [Accepted: 11/15/2024] [Indexed: 11/20/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder with environmental factors being the primary risk determinants. However, genetic factors also substantially contribute to the susceptibility and progression of COPD. Although genome-wide association studies (GWAS) have identified several loci associated with COPD susceptibility, the specific pathogenic genes underlying these loci, along with their biological functions and roles within regulatory networks, remain unclear. This lack of clarity constrains our ability to achieve a deeper understanding of the genetic basis of COPD. This study leveraged the FinnGen R11 genetic dataset, comprising 21,617 cases and 372,627 controls, along with GTEx V8 eQTLs data to conduct a cross-tissue transcriptome-wide association study (TWAS). Initially, we performed a cross-tissue TWAS analysis using the Unified Test for Molecular Signatures (UTMOST), followed by validation of the UTMOST findings in single tissues using the Functional Summary-based Imputation (FUSION) method and conditional and joint (COJO) analyses of the identified genes. Subsequently, candidate susceptibility genes were screened using Multi-marker Analysis of Genomic Annotation (MAGMA). The causal relationship between these candidate genes and COPD was further evaluated through summary data-based Mendelian randomization (SMR), colocalization analysis, and Mendelian randomization (MR). Additionally, the identified results were validated against the COPD dataset in the GWAS Catalog (GCST90399694). GeneMANIA was employed to further explore the functional significance of these susceptibility genes. In the cross-tissue TWAS analysis (UTMOST), we identified 17 susceptibility genes associated with COPD. Among these, a novel susceptibility gene, G protein-coupled receptor kinase 4 (GRK4), was validated through single-tissue TWAS (FUSION) and MAGMA analyses, with further confirmation via SMR, MR, and colocalization analyses. Moreover, GRK4 was validated in an independent dataset. This study identifies GRK4 as a potential novel susceptibility gene for COPD, which may influence disease risk by exacerbating inflammatory responses. The findings address gaps in previous single-tissue GWAS studies, revealing consistent expression and potential function of GRK4 across different tissues. However, considering the study's limitations, further investigation and validation of GRK4's role in COPD are warranted.
Collapse
Affiliation(s)
- Guanglei Chen
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China
| | - Yaxian Jin
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550000, Guizhou, China
| | - Cancan Chu
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China
| | - Yuhao Zheng
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China
| | - Changfu Yang
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China
| | - Yunzhi Chen
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China
| | - Xing Zhu
- Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China.
| |
Collapse
|
43
|
Schuurmans IK, Dunn EC, Lussier AA. DNA methylation as a possible mechanism linking childhood adversity and health: results from a 2-sample mendelian randomization study. Am J Epidemiol 2024; 193:1541-1552. [PMID: 38754872 PMCID: PMC11538561 DOI: 10.1093/aje/kwae072] [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: 06/02/2023] [Revised: 03/07/2024] [Accepted: 05/14/2024] [Indexed: 05/18/2024] Open
Abstract
Childhood adversity is an important risk factor for adverse health across the life course. Epigenetic modifications, such as DNA methylation (DNAm), are a hypothesized mechanism linking adversity to disease susceptibility. Yet, few studies have determined whether adversity-related DNAm alterations are causally related to future health outcomes or if their developmental timing plays a role in these relationships. Here, we used 2-sample mendelian randomization to obtain stronger causal inferences about the association between adversity-associated DNAm loci across development (ie, birth, childhood, adolescence, and young adulthood) and 24 mental, physical, and behavioral health outcomes. We identified particularly strong associations between adversity-associated DNAm and attention-deficit/hyperactivity disorder, depression, obsessive-compulsive disorder, suicide attempts, asthma, coronary artery disease, and chronic kidney disease. More of these associations were identified for birth and childhood DNAm, whereas adolescent and young adulthood DNAm were more closely linked to mental health. Childhood DNAm loci also had primarily risk-suppressing relationships with health outcomes, suggesting that DNAm might reflect compensatory or buffering mechanisms against childhood adversity rather than acting solely as an indicator of disease risk. Together, our results suggest adversity-related DNAm alterations are linked to both physical and mental health outcomes, with particularly strong impacts of DNAm differences emerging earlier in development.
Collapse
Affiliation(s)
- Isabel K Schuurmans
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, 3000 CA Rotterdam, the Netherlands
| | - Erin C Dunn
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, United States
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, United States
| | - Alexandre A Lussier
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, United States
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, United States
| |
Collapse
|
44
|
Major TJ, Takei R, Matsuo H, Leask MP, Sumpter NA, Topless RK, Shirai Y, Wang W, Cadzow MJ, Phipps-Green AJ, Li Z, Ji A, Merriman ME, Morice E, Kelley EE, Wei WH, McCormick SPA, Bixley MJ, Reynolds RJ, Saag KG, Fadason T, Golovina E, O'Sullivan JM, Stamp LK, Dalbeth N, Abhishek A, Doherty M, Roddy E, Jacobsson LTH, Kapetanovic MC, Melander O, Andrés M, Pérez-Ruiz F, Torres RJ, Radstake T, Jansen TL, Janssen M, Joosten LAB, Liu R, Gaal OI, Crişan TO, Rednic S, Kurreeman F, Huizinga TWJ, Toes R, Lioté F, Richette P, Bardin T, Ea HK, Pascart T, McCarthy GM, Helbert L, Stibůrková B, Tausche AK, Uhlig T, Vitart V, Boutin TS, Hayward C, Riches PL, Ralston SH, Campbell A, MacDonald TM, Nakayama A, Takada T, Nakatochi M, Shimizu S, Kawamura Y, Toyoda Y, Nakaoka H, Yamamoto K, Matsuo K, Shinomiya N, Ichida K, Lee C, Bradbury LA, Brown MA, Robinson PC, Buchanan RRC, Hill CL, Lester S, Smith MD, Rischmueller M, Choi HK, Stahl EA, Miner JN, Solomon DH, Cui J, Giacomini KM, Brackman DJ, Jorgenson EM, Liu H, Susztak K, Shringarpure S, So A, Okada Y, Li C, Shi Y, Merriman TR. A genome-wide association analysis reveals new pathogenic pathways in gout. Nat Genet 2024; 56:2392-2406. [PMID: 39406924 DOI: 10.1038/s41588-024-01921-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/21/2024] [Indexed: 10/18/2024]
Abstract
Gout is a chronic disease that is caused by an innate immune response to deposited monosodium urate crystals in the setting of hyperuricemia. Here, we provide insights into the molecular mechanism of the poorly understood inflammatory component of gout from a genome-wide association study (GWAS) of 2.6 million people, including 120,295 people with prevalent gout. We detected 377 loci and 410 genetically independent signals (149 previously unreported loci in urate and gout). An additional 65 loci with signals in urate (from a GWAS of 630,117 individuals) but not gout were identified. A prioritization scheme identified candidate genes in the inflammatory process of gout, including genes involved in epigenetic remodeling, cell osmolarity and regulation of NOD-like receptor protein 3 (NLRP3) inflammasome activity. Mendelian randomization analysis provided evidence for a causal role of clonal hematopoiesis of indeterminate potential in gout. Our study identifies candidate genes and molecular processes in the inflammatory pathogenesis of gout suitable for follow-up studies.
Collapse
Affiliation(s)
- Tanya J Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Riku Takei
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
- Department of Biomedical Information Management, National Defense Medical College Research Institute, National Defense Medical College, Saitama, Japan
| | - Megan P Leask
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nicholas A Sumpter
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ruth K Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Wei Wang
- Genomics R&D, 23andMe, Inc, Sunnyvale, CA, USA
| | - Murray J Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Zhiqiang Li
- The Biomedical Sciences Institute and The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong, China
| | - Aichang Ji
- Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases and Gout, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- The Institute of Metabolic Diseases, Qingdao University, Qingdao, Shandong, China
| | - Marilyn E Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Emily Morice
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eric E Kelley
- Department of Physiology and Pharmacology, West Virginia University, Morgantown, WV, USA
| | - Wen-Hua Wei
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Department of Women's and Children's Health, University of Otago, Dunedin, New Zealand
| | | | - Matthew J Bixley
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Richard J Reynolds
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kenneth G Saag
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tayaza Fadason
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Evgenia Golovina
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Lisa K Stamp
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Abhishek Abhishek
- Academic Rheumatology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Michael Doherty
- Academic Rheumatology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Edward Roddy
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
- Haywood Academic Rheumatology Centre, Midlands Partnership University NHS Foundation Trust, Stoke-on-Trent, UK
| | - Lennart T H Jacobsson
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Meliha C Kapetanovic
- Department of Clinical Sciences Lund, Section of Rheumatology, Lund University and Skåne University Hospital, Lund, Sweden
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Mariano Andrés
- Rheumatology Department, Dr Balmis General University Hospital-ISABIAL, Alicante, Spain
- Department of Clinical Medicine, Miguel Hernandez University, Alicante, Spain
| | - Fernando Pérez-Ruiz
- Osakidetza, OSI-EE-Cruces, BIOBizkaia Health Research Institute and Medicine Department of Medicine and Nursery School, University of the Basque Country, Biskay, Spain
| | - Rosa J Torres
- Department of Biochemistry, Hospital La Paz Institute for Health Research (IdiPaz), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
| | - Timothy Radstake
- Department of Rheumatology and Clinical Immunology, University Medical Center, Utrecht, The Netherlands
| | - Timothy L Jansen
- Department of Rheumatology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Matthijs Janssen
- Department of Rheumatology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine and Radboud Institute of Molecular Life Science, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ruiqi Liu
- Department of Internal Medicine and Radboud Institute of Molecular Life Science, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Orsolya I Gaal
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Tania O Crişan
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Simona Rednic
- Department of Rheumatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Cluj, Romania
| | - Fina Kurreeman
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tom W J Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - René Toes
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Frédéric Lioté
- Rheumatology Department, Feel'Gout, GH Paris Saint Joseph, Paris, France
- Rheumatology Department, INSERM U1132, BIOSCAR, University Paris Cité, Lariboisière Hospital, Paris, France
| | - Pascal Richette
- Rheumatology Department, INSERM U1132, BIOSCAR, University Paris Cité, Lariboisière Hospital, Paris, France
| | - Thomas Bardin
- Rheumatology Department, INSERM U1132, BIOSCAR, University Paris Cité, Lariboisière Hospital, Paris, France
| | - Hang Korng Ea
- Rheumatology Department, INSERM U1132, BIOSCAR, University Paris Cité, Lariboisière Hospital, Paris, France
| | - Tristan Pascart
- Department of Rheumatology, Hopital Saint-Philibert, Lille Catholic University, Lille, France
| | - Geraldine M McCarthy
- Department of Rheumatology, Mater Misericordiae University Hospital and School of Medicine, University College, Dublin, Ireland
| | - Laura Helbert
- Department of Rheumatology, Mater Misericordiae University Hospital and School of Medicine, University College, Dublin, Ireland
| | - Blanka Stibůrková
- Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
- Institute of Rheumatology, Prague, Czech Republic
| | - Anne-K Tausche
- Department of Rheumatology, University Clinic 'Carl Gustav Carus' at the Technical University, Dresden, Germany
| | - Till Uhlig
- Center for Treatment of Rheumatic and Musculoskeletal Diseases, Diakonhjemmet Hospital, Oslo, Norway
| | - Véronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Philip L Riches
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Stuart H Ralston
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Thomas M MacDonald
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee Medical School, Ninewells Hospital, Dundee, United Kingdom
| | - Akiyoshi Nakayama
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Tappei Takada
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Seiko Shimizu
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Yusuke Kawamura
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
- Department of Cancer Genome Research, Sasaki Institute, Sasaki Foundation, Tokyo, Japan
| | - Yu Toyoda
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Hirofumi Nakaoka
- Department of Cancer Genome Research, Sasaki Institute, Sasaki Foundation, Tokyo, Japan
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Fukuoka, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology & Prevention, Aichi Cancer Center, Aichi, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Aichi, Japan
- The Japan Multi-Institutional Collaborative Cohort (J-MICC) Study, Tokyo, Japan
| | - Nariyoshi Shinomiya
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Kimiyoshi Ichida
- Department of Pathophysiology, Tokyo University of Pharmacy and Life Sciences, Tokyo, Japan
| | - Chaeyoung Lee
- Department of Bioinformatics and Life Science, Soongsil University, Seoul, South Korea
| | - Linda A Bradbury
- Institute of Health and Biomedical Innovation, Translational Research Institute, Queensland University of Technology, Brisbane, Australia
| | - Matthew A Brown
- Institute of Health and Biomedical Innovation, Translational Research Institute, Queensland University of Technology, Brisbane, Australia
| | - Philip C Robinson
- School of Clinical Medicine, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | | | - Catherine L Hill
- Rheumatology Department, The Queen Elizabeth Hospital, Woodville South, South Australia, Australia
- Discipline of Medicine, University of Adelaide, Adelaide, Australia
| | - Susan Lester
- Rheumatology Department, The Queen Elizabeth Hospital, Woodville South, South Australia, Australia
- Discipline of Medicine, University of Adelaide, Adelaide, Australia
| | | | - Maureen Rischmueller
- Rheumatology Department, The Queen Elizabeth Hospital, Woodville South, South Australia, Australia
- Discipline of Medicine, University of Adelaide, Adelaide, Australia
| | - Hyon K Choi
- Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eli A Stahl
- Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeff N Miner
- Viscient Biosciences, 5752 Oberlin Dr., Suite 111, San Diego, CA, 92121, USA
| | - Daniel H Solomon
- Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jing Cui
- Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Deanna J Brackman
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Eric M Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Hongbo Liu
- Penn / The Children's Hospital of Pennsylvania Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19101, USA
- Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19101, USA
| | - Katalin Susztak
- Penn / The Children's Hospital of Pennsylvania Kidney Innovation Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19101, USA
- Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19101, USA
| | | | - Alexander So
- Service of Rheumatology, Center Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Changgui Li
- Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases and Gout, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- The Institute of Metabolic Diseases, Qingdao University, Qingdao, Shandong, China
| | - Yongyong Shi
- Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Tony R Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
- The Institute of Metabolic Diseases, Qingdao University, Qingdao, Shandong, China.
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand.
| |
Collapse
|
45
|
Gerring ZF, Thorp JG, Treur JL, Verweij KJH, Derks EM. The genetic landscape of substance use disorders. Mol Psychiatry 2024; 29:3694-3705. [PMID: 38811691 PMCID: PMC11541208 DOI: 10.1038/s41380-024-02547-z] [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: 07/17/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 05/31/2024]
Abstract
Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.
Collapse
Affiliation(s)
- Zachary F Gerring
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
| |
Collapse
|
46
|
Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun 2024; 15:8891. [PMID: 39406732 PMCID: PMC11480483 DOI: 10.1038/s41467-024-53333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 10/09/2024] [Indexed: 10/19/2024] Open
Abstract
Identifying genetic drivers of chronic diseases is necessary for drug discovery. Here, we develop a machine learning-assisted genetic priority score, which we call ML-GPS, that incorporates genetic associations with predicted disease phenotypes to enhance target discovery. First, we construct gradient boosting models to predict 112 chronic disease phecodes in the UK Biobank and analyze associations of predicted and observed phenotypes with common, rare, and ultra-rare variants to model the allelic series. We integrate these associations with existing evidence using gradient boosting with continuous feature encoding to construct ML-GPS, training it to predict drug indications in Open Targets and externally testing it in SIDER. We then generate ML-GPS predictions for 2,362,636 gene-phecode pairs. We find that the use of predicted phenotypes, which identify substantially more genetic associations than observed phenotypes across the allele frequency spectrum, significantly improves the performance of ML-GPS. ML-GPS increases coverage of drug targets, with the top 1% of all scores providing support for 15,077 gene-phecode pairs that previously had no support. ML-GPS can also identify well-known target-disease relationships, promising targets without indicated drugs, and targets for several drugs in clinical trials, including LRRK2 inhibitors for Parkinson's disease and olpasiran for cardiovascular disease.
Collapse
Affiliation(s)
- Robert Chen
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ha My Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Stein
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
47
|
Zhao Z, Wan Y, Fu H, Ying S, Zhang P, Meng H, Song Y, Fu N. Lipid-lowering drugs and risk of rapid renal function decline: a mendelian randomization study. BMC Med Genomics 2024; 17:248. [PMID: 39379957 PMCID: PMC11463126 DOI: 10.1186/s12920-024-02020-4] [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: 10/17/2023] [Accepted: 09/25/2024] [Indexed: 10/10/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) patients face the risk of rapid kidney function decline leading to adverse outcomes like dialysis and mortality. Lipid metabolism might contribute to acute kidney function decline in CKD patients. Here, we utilized the Mendelian Randomization approach to investigate potential causal relationships between drug target-mediated lipid phenotypes and rapid renal function decline. METHODS In this study, we utilized two methodologies: summarized data-based Mendelian randomization (SMR) and inverse variance-weighted Mendelian randomization (IVW-MR), to approximate exposure to lipid-lowering drugs. This entailed leveraging expression quantitative trait loci (eQTL) for drug target genes and genetic variants proximal to drug target gene regions, which encode proteins associated with low-density lipoprotein (LDL) cholesterol, as identified in genome-wide association studies. The objective was to investigate causal associations with the progression of rapid kidney function decline. RESULTS The SMR analysis revealed a potential association between high expression of PCSK9 and rapid kidney function decline (OR = 1.11, 95% CI= [1.001-1.23]; p = 0.044). Similarly, IVW-MR analysis demonstrated a negative association between LDL cholesterol mediated by HMGCR and kidney function decline (OR = 0.74, 95% CI = 0.60-0.90; p = 0.003). CONCLUSION Genetically predicted inhibition of HMGCR is linked with the progression of kidney function decline, while genetically predicted PCSK9 inhibition is negatively associated with kidney function decline. Future research should incorporate clinical trials to validate the relevance of PCSK9 in preventing kidney function decline.
Collapse
Affiliation(s)
- Zhicheng Zhao
- Graduate school of Tianjin Medical University, Tianjin, 300070, China
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, 300222, China
| | - Yu Wan
- Graduate school of Tianjin Medical University, Tianjin, 300070, China
| | - Han Fu
- Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuo Ying
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, 300222, China
| | - Peng Zhang
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, 300222, China
| | - Haoyu Meng
- Graduate school of Tianjin Medical University, Tianjin, 300070, China
| | - Yu Song
- Graduate school of Tianjin Medical University, Tianjin, 300070, China
| | - Naikuan Fu
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, 300222, China.
| |
Collapse
|
48
|
Tang K, Wang W, Chang W, Wu X. Genetically Proxied Antidiabetic Drug Target and Primary Open-Angle Glaucoma: A Mendelian Randomization Study. Health Sci Rep 2024; 7:e70162. [PMID: 39449751 PMCID: PMC11499708 DOI: 10.1002/hsr2.70162] [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: 05/23/2024] [Revised: 09/30/2024] [Accepted: 10/08/2024] [Indexed: 10/26/2024] Open
Abstract
Background and Aims Observational studies suggest that antidiabetic drugs may lower POAG risk; while the causal relationship remains unclear. Naturally occurring variation in genes encoding antidiabetics drug targets can be used as proxies to investigate long-term therapeutic effect of these drugs on POAG risk. Methods We performed a two-sample Mendelian randomization study to evaluate the potential effect of antidiabetic drug targets on POAG in Europeans and East Asians. To proxy antidiabetic drugs (ABCC8, PPARG, GLP1R, SLC5A2), we leveraged genetic variants located near or within drug target genes that were associated with HbA1c. The validity of our ancestry-specific genetic instrument was checked with multipul positive control outcomes. Genetic summary statistics of POAG from the International Glaucoma Genetics Consortium, Global Biobank Meta-analysis Initiative, and FinnGen consortia were analyzed for Europeans (38,164 cases and 1,576,179 controls) and East Asians (16,650 cases and 288,833 controls) separately. Inverse-variance weighted random-effects models were used as primary method. Results MR results provided consistent evidence of a protective effect of ABCC8 inhibition on POAG using data sets from IGG, GBMI, and FinnGen. Genetically predicted one-standard deviation reduction in HbA1c from ABCC8 inhibition were significant associated with lower risk of POAG in Europeans (OR = 0.211, 95% CI: 0.133-0.333; p < 0.001) and East Asians (OR = 0.070, 95% CI: 0.011-0.459; p = 0.0056). The association between genetically predicted ABCC8 inhibition and risk of POAG was mainly mediated through intraocular pressure. No association was found for PPARG, SLC5A2, or GLP1R. Sensitivity analyses supported this observation. Conclusions We found a protective effect of genetically proxied ABCC8 inhibition on POAG risk in both Europeans and East Asians, highlighting ABCC8 as a promising candidate drug target for POAG, and mechanisms underlying the protective effect should also be investigated.
Collapse
Affiliation(s)
- Kefu Tang
- Department of Clinical Laboratory, Prenatal Diagnosis Center, Changning Maternity and Infant Health HospitalEast China Normal UniversityShanghaiChina
| | - Wenqiu Wang
- Department of Ophthalmology, Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Weiteng Chang
- Department of Ophthalmology, NHC Key Laboratory of Myopia, Shanghai Research Center of Ophthalmology and Optometry, Eye & ENT HospitalFudan UniversityShanghaiChina
| | - Xi Wu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio‐X InstitutesShanghai Jiao Tong UniversityShanghaiChina
| |
Collapse
|
49
|
Akamatsu K, Golzari S, Amariuta T. Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24314410. [PMID: 39399015 PMCID: PMC11469471 DOI: 10.1101/2024.09.25.24314410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.
Collapse
Affiliation(s)
- Kai Akamatsu
- School of Biological Sciences, UC San Diego, La Jolla, CA, USA
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
| | - Stephen Golzari
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Tiffany Amariuta
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
| |
Collapse
|
50
|
Ueland TE, Mosley JD, Neylan C, Shelley JP, Robinson J, Gamazon ER, Maguire L, Peek R, Hawkins AT. Multiancestry transferability of a polygenic risk score for diverticulitis. BMJ Open Gastroenterol 2024; 11:e001474. [PMID: 39313293 PMCID: PMC11418579 DOI: 10.1136/bmjgast-2024-001474] [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: 05/26/2024] [Accepted: 09/06/2024] [Indexed: 09/25/2024] Open
Abstract
OBJECTIVE Polygenic risk scores (PRS) for diverticular disease must be evaluated in diverse cohorts. We sought to explore shared genetic predisposition across the phenome and to assess risk stratification in individuals genetically similar to European, African and Admixed-American reference samples. METHODS A 44-variant PRS was applied to the All of Us Research Program. Phenome-wide association studies (PheWAS) identified conditions linked with heightened genetic susceptibility to diverticular disease. To evaluate the PRS in risk stratification, logistic regression models for symptomatic and for severe diverticulitis were compared with base models with covariates of age, sex, body mass index, smoking and principal components. Performance was assessed using area under the receiver operating characteristic curves (AUROC) and Nagelkerke's R2. RESULTS The cohort comprised 181 719 individuals for PheWAS and 50 037 for risk modelling. PheWAS identified associations with diverticular disease, connective tissue disease and hernias. Across ancestry groups, one SD PRS increase was consistently associated with greater odds of severe (range of ORs (95% CI) 1.60 (1.27 to 2.02) to 1.86 (1.42 to 2.42)) and of symptomatic diverticulitis ((95% CI) 1.27 (1.10 to 1.46) to 1.66 (1.55 to 1.79)) relative to controls. European models achieved the highest AUROC and Nagelkerke's R2 (AUROC (95% CI) 0.78 (0.75 to 0.81); R2 0.25). The PRS provided a maximum R2 increase of 0.034 and modest AUROC improvement. CONCLUSION Associations between a diverticular disease PRS and severe presentations persisted in diverse cohorts when controlling for known risk factors. Relative improvements in model performance were observed, but absolute change magnitudes were modest.
Collapse
Affiliation(s)
- Thomas E Ueland
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christopher Neylan
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John P Shelley
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jamie Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lillias Maguire
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Richard Peek
- Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alexander T Hawkins
- Division of General Surgery, Section of Colon & Rectal Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|