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Mauleekoonphairoj J, Tongsima S, Khongphatthanayothin A, Jurgens SJ, Zimmerman DS, Sutjaporn B, Wandee P, Bezzina CR, Nademanee K, Poovorawan Y. A diverse ancestrally-matched reference panel increases genotype imputation accuracy in a underrepresented population. Sci Rep 2023; 13:12360. [PMID: 37524845 PMCID: PMC10390539 DOI: 10.1038/s41598-023-39429-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/25/2023] [Indexed: 08/02/2023] Open
Abstract
Variant imputation, a common practice in genome-wide association studies, relies on reference panels to infer unobserved genotypes. Multiple public reference panels are currently available with variations in size, sequencing depth, and represented populations. Currently, limited data exist regarding the performance of public reference panels when used in an imputation of populations underrepresented in the reference panel. Here, we compare the performance of various public reference panels: 1000 Genomes Project, Haplotype Reference Consortium, GenomeAsia 100 K, and the recent Trans-Omics for Precision Medicine (TOPMed) program, when used in an imputation of samples from the Thai population. Genotype yields were assessed, and imputation accuracies were examined by comparison with high-depth whole genome sequencing data of the same sample. We found that imputation using the TOPMed panel yielded the largest number of variants (~ 271 million). Despite being the smallest in size, GenomeAsia 100 K achieved the best imputation accuracy with a median genotype concordance rate of 0.97. For rare variants, GenomeAsia 100 K also offered the best accuracy, although rare variants were less accurately imputable than common variants (30.3% reduction in concordance rates). The high accuracy observed when using GenomeAsia 100 K is likely attributable to the diverse representation of populations genetically similar to the study cohort emphasizing the benefits of sequencing populations classically underrepresented in human genomics.
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Affiliation(s)
- John Mauleekoonphairoj
- Center of Excellence in Arrhythmia Research, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Interdisciplinary Program of Biomedical Sciences, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Sissades Tongsima
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Apichai Khongphatthanayothin
- Center of Excellence in Arrhythmia Research, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Cardiology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Bangkok Hospital, Bangkok, Thailand
| | - Sean J Jurgens
- Heart Center, Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University, Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dominic S Zimmerman
- Heart Center, Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University, Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Boosamas Sutjaporn
- Center of Excellence in Arrhythmia Research, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Pharawee Wandee
- Center of Excellence in Arrhythmia Research, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Connie R Bezzina
- Heart Center, Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University, Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Koonlawee Nademanee
- Center of Excellence in Arrhythmia Research, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Pacific Rim Electrophysiology Research Institute, Bumrungrad International Hospital, Bangkok, Thailand
| | - Yong Poovorawan
- Center of Excellence in Clinical Virology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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Affiliation(s)
- Hanno L Tan
- Department of Cardiology, Heart Center, Academic Medical Center, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - Laura H van Dongen
- Department of Cardiology, Heart Center, Academic Medical Center, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Dominic S Zimmerman
- Department of Cardiology, Heart Center, Academic Medical Center, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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van Dongen LH, Harms PP, Hoogendoorn M, Zimmerman DS, Lodder EM, 't Hart LM, Herings R, van Weert HCPM, Nijpels G, Swart KMA, van der Heijden AA, Blom MT, Elders PJ, Tan HL. Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project. Open Heart 2021; 8:openhrt-2020-001554. [PMID: 33547224 PMCID: PMC7871346 DOI: 10.1136/openhrt-2020-001554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/19/2022] Open
Abstract
Introduction Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information. Aim To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA. Methods The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA. Conclusion The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.
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Affiliation(s)
- Laura H van Dongen
- Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Peter P Harms
- General Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Mark Hoogendoorn
- Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dominic S Zimmerman
- Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Elisabeth M Lodder
- Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Leen M 't Hart
- Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands.,Biomedical Data Sciences, section Molecular Epidemiology, Leiden University Medical Centre, Leiden, Netherlands.,Epidemiology and Data Science, Amsterdam UMC, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Ron Herings
- PHARMO Institute, Utrecht, Utrecht, Netherlands
| | - Henk C P M van Weert
- Department of General Practice, Amsterdam Public Health, Amsterdam UMC Locatie AMC, Amsterdam, Netherlands
| | - Giel Nijpels
- General Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Karin M A Swart
- General Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands.,PHARMO Institute, Utrecht, Utrecht, Netherlands
| | - Amber A van der Heijden
- General Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Marieke T Blom
- Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Petra J Elders
- General Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Hanno L Tan
- Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands .,Netherlands Heart Institute, Utrecht, Netherlands
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