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Mayanja R, Machipisa T, Soremekun O, Kamiza AB, Kintu C, Kalungi A, Kalyesubula R, Sande OJ, Jjingo D, Fabian J, Robinson-Cohen C, Franceschini N, Nitsch D, Nyirenda M, Zeggini E, Morris AP, Chikowore T, Fatumo S. Genome-wide association analysis of cystatin-C kidney function in continental Africa. EBioMedicine 2023; 95:104775. [PMID: 37639939 PMCID: PMC10474146 DOI: 10.1016/j.ebiom.2023.104775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Chronic kidney disease is becoming more prevalent in Africa, and its genetic determinants are poorly understood. Creatinine-based estimated glomerular filtration rate (eGFR) is commonly used to estimate kidney function, modelling the excretion of the endogenous biomarker (creatinine). However, eGFR based on creatinine has been shown to inadequately detect individuals with low kidney function in Sub-Saharan Africa, with eGFR based on cystatin-C (eGFRcys) exhibiting significantly superior performance. Therefore, we opted to conduct a GWAS for eGFRcys. METHODS Using the Uganda Genomic Resource, we performed a genome-wide association study (GWAS) of eGFRcys in 5877 Ugandans and evaluated replication in independent studies. Subsequently, putative causal variants were screened through Bayesian fine-mapping. Functional annotation of the GWAS loci was performed using Functional Mapping and Annotation (FUMA). FINDINGS Three independent lead single nucleotide polymorphisms (SNPs) (P-value <5 × 10-8 (based on likelihood ratio test (LRT))) were identified; rs59288815 (ANK3), rs4277141 (OR51B5) and rs911119 (CST3). From fine-mapping, rs59288815 and rs911119 each had a posterior probability of causality of >99%. The rs911119 SNP maps to the cystatin C gene and has been previously associated with eGFRcys among Europeans. With gene-set enrichment analyses of the olfactory receptor family 51 overlapping genes, we identified an association with the G-alpha-S signalling events. INTERPRETATION Our study found two previously unreported associated SNPs for eGFRcys in continental Africans (rs59288815 and rs4277141) and validated a previously well-established SNP (rs911119) for eGFRcys. The identified gene-set enrichment for the G-protein signalling pathways relates to the capacity of the kidney to readily adapt to an ever-changing environment. Additional GWASs are required to represent the diverse regions in Africa. FUNDING Wellcome (220740/Z/20/Z).
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Affiliation(s)
- Richard Mayanja
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University, College of Health Sciences, Kampala, Uganda
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa; Clinical Research Laboratory-Genetic and Molecular Epidemiology Laboratory (CRLB-GMEL), Population Health Research Institute (PHRI) & McMaster University, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, Ontario, L8L 2X2, Canada
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Abram B Kamiza
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - Christopher Kintu
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University, College of Health Sciences, Kampala, Uganda
| | - Allan Kalungi
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Robert Kalyesubula
- Medical Research Council/ Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe, Uganda
| | - Obondo J Sande
- Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University, College of Health Sciences, Kampala, Uganda
| | - Daudi Jjingo
- African Center of Excellence in Bioinformatics (ACE-B), Makerere University, Kampala, Uganda
| | - June Fabian
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cassianne Robinson-Cohen
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nora Franceschini
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - Moffat Nyirenda
- Clinical Research Laboratory-Genetic and Molecular Epidemiology Laboratory (CRLB-GMEL), Population Health Research Institute (PHRI) & McMaster University, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, Ontario, L8L 2X2, Canada; London School of Hygiene and Tropical Medicine London, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; TUM School of Medicine, Translational Genomics, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Medical Research Council/ Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe, Uganda; London School of Hygiene and Tropical Medicine London, UK; Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
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Kintu C, Soremekun O, Kamiza AB, Kalungi A, Mayanja R, Kalyesubula R, Bagaya S B, Jjingo D, Fabian J, Gill D, Nyirenda M, Nitsch D, Chikowore T, Fatumo S. The causal effects of lipid traits on kidney function in Africans: bidirectional and multivariable Mendelian-randomization study. EBioMedicine 2023; 90:104537. [PMID: 37001235 PMCID: PMC10070509 DOI: 10.1016/j.ebiom.2023.104537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Observational studies have investigated the effect of serum lipids on kidney function, but these findings are limited by confounding, reverse causation and have reported conflicting results. Mendelian randomization (MR) studies address this confounding problem. However, they have been conducted mostly in European ancestry individuals. We, therefore, set out to investigate the effect of lipid traits on the estimated glomerular filtration rate (eGFR) based on serum creatinine in individuals of African ancestry. METHODS We used the two-sample and multivariable Mendelian randomization (MVMR) approaches; in which instrument variables (IV's) for the predictor (lipid traits) were derived from summary-level data of a meta-analyzed African lipid GWAS (MALG, n = 24,215) from the African Partnership for Chronic Disease Research (APCDR) (n = 13,612) & the Africa Wits-IN-DEPTH partnership for Genomics studies (AWI-Gen) dataset (n = 10,603). The outcome IV's were computed from the eGFR summary-level data of African-ancestry individuals within the Million Veteran Program (n = 57,336). A random-effects inverse variance method was used in our primary analysis, and pleiotropy was adjusted for using robust and penalized sensitivity testing. The lipid predictors for the MVMR were high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG). FINDINGS We found a significant causal association between genetically predicted low-density lipoprotein (LDL) cholesterol and eGFR in African ancestry individuals β = 1.1 (95% CI [0.411-1.788]; p = 0.002). Similarly, total cholesterol (TC) showed a significant causal effect on eGFR β = 1.619 (95% CI [0.412-2.826]; p = 0.009). However, the IVW estimate showed that genetically predicted HDL-C β = -0.164, (95% CI = [-1.329 to 1.00]; p = 0.782), and TG β = -0.934 (CI = [-2.815 to 0.947]; p = 0.33) were not significantly causally associated with the risk of eGFR. In the multivariable analysis inverse-variance weighted (MVIVW) method, there was evidence for a causal association between LDL and eGFR β = 1.228 (CI = [0.477-1.979]; p = 0.001). A significant causal effect of Triglycerides (TG) on eGFR in the MVIVW analysis β = -1.3 ([-2.533 to -0.067]; p = 0.039) was observed as well. All the causal estimates reported reflect a unit change in the outcome per a 1 SD increase in the exposure. HDL showed no evidence of a significant causal association with eGFR in the MVIVW method (β = -0.117 (95% CI [-1.252 to 0.018]; p = 0.840)). We found no evidence of a reverse causal impact of eGFR on serum lipids. All our sensitivity analyses indicated no strong evidence of pleiotropy or heterogeneity between our instrumental variables for both the forward and reverse MR analysis. INTERPRETATION In this African ancestry population, genetically predicted higher LDL-C and TC are causally associated with higher eGFR levels, which may suggest that the relationship between LDL, TC and kidney function may be U-shaped. And as such, lowering LDL_C does not necessarily improve risk of kidney disease. This may also imply the reason why LDL_C is seen to be a poorer predictor of kidney function compared to HDL. In addition, this further supports that more work is warranted to confirm the potential association between lipid traits and risk of kidney disease in individuals of African Ancestry. FUNDING Wellcome (220740/Z/20/Z).
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Affiliation(s)
- Christopher Kintu
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University College of Health Sciences, Kampala, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Abram B Kamiza
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Allan Kalungi
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Richard Mayanja
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Robert Kalyesubula
- Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University College of Health Sciences, Kampala, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
| | - Bernard Bagaya S
- Department of Immunology and Molecular Biology, School of Biomedical Sciences, Makerere University College of Health Sciences, Kampala, Uganda
| | - Daudi Jjingo
- African Center of Excellence in Bioinformatics (ACE-B), Makerere University, Kampala 10101, Uganda
| | - June Fabian
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
| | - Moffat Nyirenda
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
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Kamiza AB, Toure SM, Vujkovic M, Machipisa T, Soremekun OS, Kintu C, Corpas M, Pirie F, Young E, Gill D, Sandhu MS, Kaleebu P, Nyirenda M, Motala AA, Chikowore T, Fatumo S. Transferability of genetic risk scores in African populations. Nat Med 2022; 28:1163-1166. [PMID: 35654908 PMCID: PMC9205766 DOI: 10.1038/s41591-022-01835-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/20/2022] [Indexed: 01/02/2023]
Abstract
The poor transferability of genetic risk scores (GRSs) derived from European ancestry data in diverse populations is a cause of concern. We set out to evaluate whether GRSs derived from data of African American individuals and multiancestry data perform better in sub-Saharan Africa (SSA) compared to European ancestry-derived scores. Using summary statistics from the Million Veteran Program (MVP), we showed that GRSs derived from data of African American individuals enhance polygenic prediction of lipid traits in SSA compared to European and multiancestry scores. However, our GRS prediction varied greatly within SSA between the South African Zulu (low-density lipoprotein cholesterol (LDL-C), R2 = 8.14%) and Ugandan cohorts (LDL-C, R2 = 0.026%). We postulate that differences in the genetic and environmental factors between these population groups might lead to the poor transferability of GRSs within SSA. More effort is required to optimize polygenic prediction in Africa.
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Affiliation(s)
- Abram B Kamiza
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Karonga, Malawi
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sounkou M Toure
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- African Centre of Excellence in Bioinformatics, University of Science and Technologies of Bamako, Bamako, Mali
| | - Marijana Vujkovic
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Tafadzwa Machipisa
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA), Department of Medicine, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
| | - Opeyemi S Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Christopher Kintu
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Manuel Corpas
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, United Kingdom
- Institute of Continuing Education, Madingley Hall, University of Cambridge, Cambridge, UK
- Facultad de Ciencias de la Salud, Universidade Internacional de La Rioja, Madrid, Spain
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | | | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK
| | - Manjinder S Sandhu
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | | | | | - Ayesha A Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- MRC/UVRI and LSHTM, Entebbe, Uganda.
- London School of Hygiene and Tropical Medicine, London, UK.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
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Kamiza AB, Hsieh L, Tang R, Chien H, Lai C, Chiu L, Lo T, Hung K, You J, Wang W, Hsiung CA, Yeh C. Polymorphisms of DNA repair genes are associated with colorectal cancer in patients with Lynch syndrome. Mol Genet Genomic Med 2018; 6:533-540. [PMID: 29664240 PMCID: PMC6081223 DOI: 10.1002/mgg3.402] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 02/04/2018] [Accepted: 03/12/2018] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND DNA repair genes are crucial for maintaining genomic stability by preventing mutagenesis and carcinogenesis. The present retrospective cohort study aimed at investigating whether MLH1, APEX1, MUTYH, OGG1, NUDT1, XRCC5, XPA, and ERCC2 single nucleotide polymorphisms (SNPs) are associated with colorectal cancer (CRC) in Chinese population with Lynch syndrome. METHODS From Amsterdam criteria family registry, we identified 270 patients with Lynch syndrome. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between DNA repair SNPs and CRC were calculated using a weighted Cox proportional hazard regression model. RESULTS Heterozygous variants of rs1799832 in NUDT1 (HR = 2.97, 95% CI = 1.51-5.83) and rs13181 in ERCC2 (HR = 2.69, 95% CI = 1.10-6.55) were significantly associated with an increased risk of CRC compared with wild-type homozygous CC and TT genotypes, respectively. However, the variant CG+GG genotype of MUTYH rs3219489 was associated with a decreased risk of CRC (HR = 0.49, 95% CI = 0.26-0.91) compared with the homozygous CC wild-type counterparts. CONCLUSION Our findings revealed that polymorphisms of DNA repair genes that include NUDT1, ERCC2, and MUTYH are associated with CRC in patients with Lynch syndrome in Chinese population. Further studies with large sample size are needed to confirm our findings.
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Affiliation(s)
- Abram B. Kamiza
- School of Public HealthCollege of Public HealthTaipei Medical UniversityTaipeiTaiwan
| | - Ling‐Ling Hsieh
- Department of Public HealthCollege of MedicineChang Gung UniversityTaoyuanTaiwan
| | - Reiping Tang
- Colorectal SectionDepartment of SurgeryChang Gung Memorial HospitalTaoyuanTaiwan
- School of MedicineChang Gung UniversityTaoyuanTaiwan
| | - Huei‐Tzu Chien
- Department of Public HealthCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nutrition and Health SciencesChang Gung University of Science and TechnologyTaoyuanTaiwan
| | - Chih‐Hsiung Lai
- Department of Public HealthCollege of MedicineChang Gung UniversityTaoyuanTaiwan
| | - Li‐Ling Chiu
- Department of Public HealthCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nutrition and Health SciencesChang Gung University of Science and TechnologyTaoyuanTaiwan
| | - Tsai‐Ping Lo
- Institute of Population Health SciencesNational Health Research InstitutesMiaoliTaiwan
| | - Kuan‐Yi Hung
- Institute of Population Health SciencesNational Health Research InstitutesMiaoliTaiwan
| | - Jeng‐Fu You
- Colorectal SectionDepartment of SurgeryChang Gung Memorial HospitalTaoyuanTaiwan
- School of MedicineChang Gung UniversityTaoyuanTaiwan
| | - Wen‐Chang Wang
- Ph.D. Program for Translational MedicineCollege of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
| | - Chao A. Hsiung
- Institute of Population Health SciencesNational Health Research InstitutesMiaoliTaiwan
| | - Chih‐Ching Yeh
- School of Public HealthCollege of Public HealthTaipei Medical UniversityTaipeiTaiwan
- Department of Public HealthChina Medical UniversityTaichungTaiwan
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