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Ardiansyah E, Riza AL, Dian S, Ganiem AR, Alisjahbana B, Setiabudiawan TP, van Laarhoven A, van Crevel R, Kumar V. Sequencing whole genomes of the West Javanese population in Indonesia reveals novel variants and improves imputation accuracy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598981. [PMID: 38915501 PMCID: PMC11195206 DOI: 10.1101/2024.06.14.598981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Existing genotype imputation reference panels are mainly derived from European populations, limiting their accuracy in non-European populations. To improve imputation accuracy for Indonesians, the world's fourth most populous country, we combined Whole Genome Sequencing (WGS) data from 227 West Javanese individuals with East Asian data from the 1000 Genomes Project. This created three reference panels: EAS 1KGP3 (EASp), Indonesian (INDp), and a combined panel (EASp+INDp). We also used ten West-Javanese samples with WGS and SNP-typing data for benchmarking. We identified 1.8 million novel single nucleotide variants (SNVs) in the West Javanese population, which, while similar to the East Asians, are distinct from the Central Indonesian Flores population. Adding INDp to the EASp reference panel improved imputation accuracy (R2) from 0.85 to 0.90, and concordance from 87.88% to 91.13%. These findings underscore the importance of including Indonesian genetic data in reference panels, advocating for broader WGS of diverse Indonesian populations to enhance genomic studies.
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Affiliation(s)
- Edwin Ardiansyah
- Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
| | - Anca-Lelia Riza
- Laboratory of Human Genomics, University of Medicine and Pharmacy of Craiova, 200638 Craiova, Romania
| | - Sofiati Dian
- Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
- Department of Neurology, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Ahmad Rizal Ganiem
- Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
- Department of Neurology, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Bachti Alisjahbana
- Research Center for Care and Control of Infectious Diseases, Universitas Padjadjaran, Bandung, Indonesia
- Department of Internal Medicine, Hasan Sadikin Hospital, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Todia P Setiabudiawan
- Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Arjan van Laarhoven
- Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Reinout van Crevel
- Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Vinod Kumar
- Department of Internal Medicine and Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
- University of Groningen, University Medical Center Groningen, department of Genetics, Groningen, the Netherlands
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Croock D, Swart Y, Schurz H, Petersen DC, Möller M, Uren C. Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies. Curr Protoc 2024; 4:e1055. [PMID: 38837690 DOI: 10.1002/cpz1.1055] [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: 06/07/2024]
Abstract
Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.
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Affiliation(s)
- Dayna Croock
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Yolandi Swart
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Haiko Schurz
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Desiree C Petersen
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
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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] [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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Preudhomme LK, Gellman MD, Franceschini N, Perreira KM, Fernández-Rhodes LE, Gallo LC, Isasi CR, Smoller S, Castañeda SF, Daviglus M, Hutten C, Cooper RS, Cai J, Schneiderman N, Llabre MM. Genetic and stress influences on the prevalence of hypertension among hispanics/latinos in the hispanic community health study/study of latinos (HCHS/SOL). Blood Press 2022; 31:155-163. [PMID: 35762607 DOI: 10.1080/08037051.2022.2091977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
PURPOSE The current study examined the effects of chronic stress and a genetic risk score on the presence of hypertension and elevated systolic blood pressure and diastolic blood pressure among Hispanics/Latinos in the target population of Hispanic Community Health Study/Study of Latinos. MATERIALS AND METHODS Of the participants (N = 11,623) assessed during two clinic visits (Visit 1 2008-2013 & Visit 2 2014-2018), we analysed data from 7,429 adults (50.4% female), aged 18-74, who were genotyped and responded to chronic stress questionnaires. We calculated an unweighted genetic risk score using blood pressure increasing single nucleotide polymorphisms (SNPs) found to be generalisable to Hispanics/Latinos (10 SNPs). Linear and logistic regression models were used to estimate associations between chronic stress and genetic risk score and their interaction, with prevalent Visit 2 SBP or DBP, and hypertension, respectively. Models accounted for sampling weights, stratification, and cluster design. RESULTS Chronic stress (adjusted OR = 1.18, 95%CI:1.15,1.22) and hypertension genetic risk score (adjusted OR = 1.04, 95%CI:1.01,1.07) were significantly associated with prevalent hypertension, but there was no significant interaction between the chronic stress and genetic risk score on hypertension (p = .49). genetic risk score (b = .32, 95%CI:.08, .55, R2 = .02) and chronic stress (b = .45, 95%CI:.19, .72, R2 = .11) were related to DBP, with no significant interaction (p = .62). Genetic risk score (b = .42, 95%CI:.08, .76, R2 = .01) and chronic stress (b = .80, 95%CI:.34,1.26, R2 = .11) were also related to SBP, with no significant interaction (p = .51). CONCLUSION Results demonstrate the utility of a genetic risk score for blood pressure and are consistent with literature suggesting chronic stress has a strong, direct association with elevated blood pressure among U.S. Hispanics/Latinos.
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Affiliation(s)
| | - Marc D Gellman
- University of Miami, Department of Psychology, Coral Gables, USA
| | | | | | - Lindsay E Fernández-Rhodes
- Pennsylvania State University, Biobehavioral Health - BBH Epidemiology and Genetics across Populations & Societies, University Park, USA
| | - Linda C Gallo
- San Diego State University, Department of Psychology, San Diego, USA
| | - Carmen R Isasi
- Albert Einstein College of Medicine, Department of Epidemiology & Population Health, Bronx, USA
| | - Sylvia Smoller
- Albert Einstein College of Medicine, Department of Epidemiology & Population Health, Bronx, USA
| | | | - Martha Daviglus
- University of Illinois at Chicago, Department of Medicine, Chicago, USA
| | - Christina Hutten
- University of Illinois at Chicago, Department of Epidemiology and Biostatistics, Chicago, USA
| | - Richard S Cooper
- Loyola University-Chicago, Department of Public Health Sciences, Chicago, USA
| | - Jianwen Cai
- UNC-Chapel Hill, Department of Epidemiology, Chapel Hill, USA
| | | | - Maria M Llabre
- University of Miami, Department of Psychology, Coral Gables, USA
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Mdyogolo S, MacNeil MD, Neser FWC, Scholtz MM, Makgahlela ML. Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa. Trop Anim Health Prod 2022; 54:90. [PMID: 35133512 DOI: 10.1007/s11250-022-03102-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/01/2022] [Indexed: 11/26/2022]
Abstract
Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.
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Affiliation(s)
- S Mdyogolo
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa.
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa.
| | - M D MacNeil
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
- Delta G, Miles City, MT, USA
| | - F W C Neser
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M M Scholtz
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M L Makgahlela
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
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McInerney TW, Fulton-Howard B, Patterson C, Paliwal D, Jermiin LS, Patel HR, Pa J, Swerdlow RH, Goate A, Easteal S, Andrews SJ. A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants. BMC Bioinformatics 2021; 22:417. [PMID: 34470617 PMCID: PMC8409003 DOI: 10.1186/s12859-021-04337-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 08/16/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Variation in mitochondrial DNA (mtDNA) identified by genotyping microarrays or by sequencing only the hypervariable regions of the genome may be insufficient to reliably assign mitochondrial genomes to phylogenetic lineages or haplogroups. This lack of resolution can limit functional and clinical interpretation of a substantial body of existing mtDNA data. To address this limitation, we developed and evaluated a large, curated reference alignment of complete mtDNA sequences as part of a pipeline for imputing missing mtDNA single nucleotide variants (mtSNVs). We call our reference alignment and pipeline MitoImpute. RESULTS We aligned the sequences of 36,960 complete human mitochondrial genomes downloaded from GenBank, filtered and controlled for quality. These sequences were reformatted for use in imputation software, IMPUTE2. We assessed the imputation accuracy of MitoImpute by measuring haplogroup and genotype concordance in data from the 1000 Genomes Project and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The mean improvement of haplogroup assignment in the 1000 Genomes samples was 42.7% (Matthew's correlation coefficient = 0.64). In the ADNI cohort, we imputed missing single nucleotide variants. CONCLUSION These results show that our reference alignment and panel can be used to impute missing mtSNVs in existing data obtained from using microarrays, thereby broadening the scope of functional and clinical investigation of mtDNA. This improvement may be particularly useful in studies where participants have been recruited over time and mtDNA data obtained using different methods, enabling better integration of early data collected using less accurate methods with more recent sequence data.
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Affiliation(s)
- Tim W McInerney
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Brian Fulton-Howard
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - Christopher Patterson
- Keck School of Medicine, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Devashi Paliwal
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Lars S Jermiin
- CSIRO Land and Water, Commonwealth Scientific Industrial and Research Organization, Acton, ACT, 2601, Australia
- Research School of Biology, Australian National University, Canberra, ACT, 2601, Australia
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
- Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Hardip R Patel
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Judy Pa
- Keck School of Medicine, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Russell H Swerdlow
- Department of Neurology, Alzheimer's Disease Center, University of Kansas, Fairway, KS, USA
| | - Alison Goate
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - Simon Easteal
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Shea J Andrews
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.
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Page GP, Kanias T, Guo YJ, Lanteri MC, Zhang X, Mast AE, Cable RG, Spencer BR, Kiss JE, Fang F, Endres-Dighe SM, Brambilla D, Nouraie M, Gordeuk VR, Kleinman S, Busch MP, Gladwin MT. Multiple-ancestry genome-wide association study identifies 27 loci associated with measures of hemolysis following blood storage. J Clin Invest 2021; 131:146077. [PMID: 34014839 DOI: 10.1172/jci146077] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 05/13/2021] [Indexed: 12/17/2022] Open
Abstract
BackgroundThe evolutionary pressure of endemic malaria and other erythrocytic pathogens has shaped variation in genes encoding erythrocyte structural and functional proteins, influencing responses to hemolytic stress during transfusion and disease.MethodsWe sought to identify such genetic variants in blood donors by conducting a genome-wide association study (GWAS) of 12,353 volunteer donors, including 1,406 African Americans, 1,306 Asians, and 945 Hispanics, whose stored erythrocytes were characterized by quantitative assays of in vitro osmotic, oxidative, and cold-storage hemolysis.ResultsGWAS revealed 27 significant loci (P < 5 × 10-8), many in candidate genes known to modulate erythrocyte structure, metabolism, and ion channels, including SPTA1, ALDH2, ANK1, HK1, MAPKAPK5, AQP1, PIEZO1, and SLC4A1/band 3. GWAS of oxidative hemolysis identified variants in genes encoding antioxidant enzymes, including GLRX, GPX4, G6PD, and SEC14L4 (Golgi-transport protein). Genome-wide significant loci were also tested for association with the severity of steady-state (baseline) in vivo hemolytic anemia in patients with sickle cell disease, with confirmation of identified SNPs in HBA2, G6PD, PIEZO1, AQP1, and SEC14L4.ConclusionsMany of the identified variants, such as those in G6PD, have previously been shown to impair erythrocyte recovery after transfusion, associate with anemia, or cause rare Mendelian human hemolytic diseases. Candidate SNPs in these genes, especially in polygenic combinations, may affect RBC recovery after transfusion and modulate disease severity in hemolytic diseases, such as sickle cell disease and malaria.
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Affiliation(s)
- Grier P Page
- Division of Biostatistics and Epidemiology, RTI International, Atlanta, Georgia, USA
| | - Tamir Kanias
- Vitalant Research Institute, Denver, Colorado, USA
| | - Yuelong J Guo
- Division of Biostatistics and Epidemiology, RTI International, Durham, North Carolina, USA
| | - Marion C Lanteri
- Vitalant Research Institute and the Department of Laboratory Medicine, UCSF, San Francisco, California, USA
| | - Xu Zhang
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Alan E Mast
- Blood Research Institute, Blood Center of Wisconsin, and Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | | | | | - Joseph E Kiss
- Vitalant Northeast Division, Pittsburgh, Pennsylvania, USA
| | - Fang Fang
- Division of Biostatistics and Epidemiology, RTI International, Durham, North Carolina, USA
| | - Stacy M Endres-Dighe
- Division of Biostatistics and Epidemiology, RTI International, Rockville, Maryland, USA
| | - Donald Brambilla
- Division of Biostatistics and Epidemiology, RTI International, Rockville, Maryland, USA
| | - Mehdi Nouraie
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pennsylvania, USA
| | - Victor R Gordeuk
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Steve Kleinman
- University of British Columbia, Victoria, British Columbia, Canada
| | - Michael P Busch
- Vitalant Research Institute and the Department of Laboratory Medicine, UCSF, San Francisco, California, USA
| | - Mark T Gladwin
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
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8
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Jian X, Sofer T, Tarraf W, Bressler J, Faul JD, Zhao W, Ratliff SM, Lamar M, Launer LJ, Laurie CC, Schneiderman N, Weir DR, Wright CB, Yaffe K, Zeng D, DeCarli C, Mosley TH, Smith JA, González HM, Fornage M. Genome-wide association study of cognitive function in diverse Hispanics/Latinos: results from the Hispanic Community Health Study/Study of Latinos. Transl Psychiatry 2020; 10:245. [PMID: 32699239 PMCID: PMC7376098 DOI: 10.1038/s41398-020-00930-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/19/2020] [Accepted: 07/03/2020] [Indexed: 12/13/2022] Open
Abstract
Cognitive function such as reasoning, attention, memory, and language is strongly correlated with brain aging. Compared to non-Hispanic whites, Hispanics/Latinos have a higher risk of cognitive impairment and dementia. The genetic determinants of cognitive function have not been widely explored in this diverse and admixed population. We conducted a genome-wide association analysis of cognitive function in up to 7600 middle aged and older Hispanics/Latinos (mean = 55 years) from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Four cognitive measures were examined: the Brief Spanish English Verbal Learning Test (B-SEVLT), the Word Fluency Test (WFT), the Digit Symbol Substitution Test (DSST), the Six-Item Screener (SIS). Four novel loci were identified: one for B-SEVLT at 4p14, two for WFT at 3p14.1 and 6p21.32, and one for DSST at 10p13. These loci implicate genes highly expressed in brain and previously connected to neurological diseases (UBE2K, FRMD4B, the HLA gene complex). By applying tissue-specific gene expression prediction models to our genotype data, additional genes highly expressed in brain showed suggestive associations with cognitive measures possibly indicating novel biological mechanisms, including IFT122 in the hippocampus for SIS, SNX31 in the basal ganglia for B-SEVLT, RPS6KB2 in the frontal cortex for WFT, and CSPG5 in the hypothalamus for DSST. These findings provide new information about the genetic determinants of cognitive function in this unique population. In addition, we derived a measure of general cognitive function based on these cognitive tests and generated genome-wide association summary results, providing a resource to the research community for comparison, replication, and meta-analysis in future genetic studies in Hispanics/Latinos.
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Affiliation(s)
- Xueqiu Jian
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tamar Sofer
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology and Department of Health Care Sciences, Wayne State University, Detroit, MI, USA
| | - Jan Bressler
- Department of Epidemiology, Human Genetics and Environmental Sciences and Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Scott M Ratliff
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Melissa Lamar
- Department of Behavioral Sciences, Rush Medical College, Chicago, IL, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, MD, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Neil Schneiderman
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Clinton B Wright
- Division of Clinical Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Kristine Yaffe
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Charles DeCarli
- Department of Neurology, School of Medicine and Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California, Davis, Sacramento, CA, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center and Department of Medicine, The University of Mississippi Medical Center, Jackson, MS, USA
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Hector M González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Epidemiology, Human Genetics and Environmental Sciences and Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA.
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9
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Bai WY, Zhu XW, Cong PK, Zhang XJ, Richards JB, Zheng HF. Genotype imputation and reference panel: a systematic evaluation on haplotype size and diversity. Brief Bioinform 2019; 21:bbz108. [PMID: 32002535 DOI: 10.1093/bib/bbz108] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/12/2019] [Accepted: 07/31/2019] [Indexed: 12/12/2022] Open
Abstract
Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF < 0.5%), a small fraction of diversity in panel could improve imputation accuracy. Second, when the haplotype size of the reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF > 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8-21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).
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Affiliation(s)
- Wei-Yang Bai
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Xiao-Wei Zhu
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Pei-Kuan Cong
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Xue-Jun Zhang
- Institute of Dermatology and Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - J Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Hou-Feng Zheng
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
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10
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Saccone NL, Emery LS, Sofer T, Gogarten SM, Becker DM, Bottinger EP, Chen LS, Culverhouse RC, Duan W, Hancock DB, Hosgood HD, Johnson EO, Loos RJF, Louie T, Papanicolaou G, Perreira KM, Rodriquez EJ, Schurmann C, Stilp AM, Szpiro AA, Talavera GA, Taylor KD, Thrasher JF, Yanek LR, Laurie CC, Pérez-Stable EJ, Bierut LJ, Kaplan RC. Genome-Wide Association Study of Heavy Smoking and Daily/Nondaily Smoking in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Nicotine Tob Res 2019; 20:448-457. [PMID: 28520984 DOI: 10.1093/ntr/ntx107] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 05/11/2017] [Indexed: 02/07/2023]
Abstract
Introduction Genetic variants associated with nicotine dependence have previously been identified, primarily in European-ancestry populations. No genome-wide association studies (GWAS) have been reported for smoking behaviors in Hispanics/Latinos in the United States and Latin America, who are of mixed ancestry with European, African, and American Indigenous components. Methods We examined genetic associations with smoking behaviors in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (N = 12 741 with smoking data, 5119 ever-smokers), using ~2.3 million genotyped variants imputed to the 1000 Genomes Project phase 3. Mixed logistic regression models accounted for population structure, sampling, relatedness, sex, and age. Results The known region of CHRNA5, which encodes the α5 cholinergic nicotinic receptor subunit, was associated with heavy smoking at genome-wide significance (p ≤ 5 × 10-8) in a comparison of 1929 ever-smokers reporting cigarettes per day (CPD) > 10 versus 3156 reporting CPD ≤ 10. The functional variant rs16969968 in CHRNA5 had a p value of 2.20 × 10-7 and odds ratio (OR) of 1.32 for the minor allele (A); its minor allele frequency was 0.22 overall and similar across Hispanic/Latino background groups (Central American = 0.17; South American = 0.19; Mexican = 0.18; Puerto Rican = 0.22; Cuban = 0.29; Dominican = 0.19). CHRNA4 on chromosome 20 attained p < 10-4, supporting prior findings in non-Hispanics. For nondaily smoking, which is prevalent in Hispanic/Latino smokers, compared to daily smoking, loci on chromosomes 2 and 4 achieved genome-wide significance; replication attempts were limited by small Hispanic/Latino sample sizes. Conclusions Associations of nicotinic receptor gene variants with smoking, first reported in non-Hispanic European-ancestry populations, generalized to Hispanics/Latinos despite different patterns of smoking behavior. Implications We conducted the first large-scale genome-wide association study (GWAS) of smoking behavior in a US Hispanic/Latino cohort, and the first GWAS of daily/nondaily smoking in any population. Results show that the region of the nicotinic receptor subunit gene CHRNA5, which in non-Hispanic European-ancestry smokers has been associated with heavy smoking as well as cessation and treatment efficacy, is also significantly associated with heavy smoking in this Hispanic/Latino cohort. The results are an important addition to understanding the impact of genetic variants in understudied Hispanic/Latino smokers.
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Affiliation(s)
- Nancy L Saccone
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Tamar Sofer
- Department of Biostatistics, University of Washington, Seattle, WA
| | | | - Diane M Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Li-Shiun Chen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | | | - Weimin Duan
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC
| | - H Dean Hosgood
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Eric O Johnson
- Fellow Program and Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA
| | - George Papanicolaou
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Krista M Perreira
- Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Erik J Rodriquez
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD.,Division of General Internal Medicine, University of California, San Francisco, San Francisco, CA
| | - Claudia Schurmann
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Gregory A Talavera
- Graduate School of Public Health, San Diego State University, San Diego, CA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - James F Thrasher
- Department of Health Promotion, Education and Behavior, University of South Carolina, Columbia, SC
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Eliseo J Pérez-Stable
- National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
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11
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Sariya S, Lee JH, Mayeux R, Vardarajan BN, Reyes-Dumeyer D, Manly JJ, Brickman AM, Lantigua R, Medrano M, Jimenez-Velazquez IZ, Tosto G. Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools. Front Genet 2019; 10:239. [PMID: 31001313 PMCID: PMC6456789 DOI: 10.3389/fgene.2019.00239] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 03/04/2019] [Indexed: 11/13/2022] Open
Abstract
Background Imputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants’ imputation in an admixed Caribbean Hispanic population (CH). Methods We evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools’ internal indexes (e.g., IMPUTE2 “Info” ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen’s kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion. Results SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05). Conclusion Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.
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Affiliation(s)
- Sanjeev Sariya
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Joseph H Lee
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Jennifer J Manly
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Adam M Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Rafael Lantigua
- Medicine College of Physicians and Surgeons, and The Department of Epidemiology, School of Public Health, Columbia University, New York, NY, United States
| | - Martin Medrano
- School of Medicine, Pontificia Universidad Catolica Madre y Maestra, Santiago, Dominican Republic
| | - Ivonne Z Jimenez-Velazquez
- Department of Medicine, Geriatrics Program, University of Puerto Rico School of Medicine, San Juan, Puerto Rico
| | - Giuseppe Tosto
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
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12
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Schurz H, Müller SJ, van Helden PD, Tromp G, Hoal EG, Kinnear CJ, Möller M. Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population. Front Genet 2019; 10:34. [PMID: 30804980 PMCID: PMC6370942 DOI: 10.3389/fgene.2019.00034] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 01/17/2019] [Indexed: 12/30/2022] Open
Abstract
Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.
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Affiliation(s)
- Haiko Schurz
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stephanie J Müller
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Paul David van Helden
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gerard Tromp
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Eileen G Hoal
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Craig J Kinnear
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Marlo Möller
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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