1
|
Matosinho CGR, Silva CGR, Martins ML, Silva-Malta MCF. Next Generation Sequencing of Red Blood Cell Antigens in Transfusion Medicine: Systematic Review and Meta-Analysis. Transfus Med Rev 2024; 38:150776. [PMID: 37914611 DOI: 10.1016/j.tmrv.2023.150776] [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: 05/29/2023] [Revised: 08/11/2023] [Accepted: 09/01/2023] [Indexed: 11/03/2023]
Abstract
Molecular analysis of blood groups is important in transfusion medicine, allowing the prediction of red blood cell (RBC) antigens. Many blood banks use single nucleotide variant (SNV) based methods for blood group analysis. While this is a well-established approach, it is limited to the polymorphisms included in genotyping panels. Thus, variants that alter antigenic expression may be ignored, resulting in incorrect prediction of phenotypes. The popularization of next-generation sequencing (NGS) has led to its application in transfusion medicine, including for RBC antigens determination. The present review/meta-analysis aimed to evaluate the applicability of the NGS for the prediction of RBC antigens. A systematic review was conducted following a comprehensive literature search in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Studies were selected based on predefined criteria and evaluated using Strengthening the Reporting of Observational studies in Epidemiology guidelines. The characteristics and results of the studies were extracted and meta-analysis was performed to verify the agreement between results from standard molecular methods and NGS. Kell (rs8176058), Duffy (rs2814778, rs12078), or Kidd (rs1085396) alleles were selected as a model for comparisons. Additionally, results are presented for other blood group systems. Of the 864 eligible studies identified, 10 met the inclusion criteria and were selected for meta-analysis. The pooled concordance proportion for NGS compared to other methods ranged from 0.982 to 0.994. The sequencing depth coverage was identified as crucial parameters for the reliability of the results. Some studies reported difficulty in analyzing more complex systems, such as Rh and MNS, requiring the adoption of specific strategies. NGS is a technology capable of predicting blood group phenotypes and has many strengths such as the possibility of simultaneously analyzing hundred individuals and gene regions, and the ability to provide comprehensive genetic analysis, which is useful in the description of new alleles and a better understanding of the genetic basis of blood groups. The implementation of NGS in the routine of blood banks depends on several factors such as cost reduction, the availability of widely validated panels, the establishment of clear quality parameters and access to bioinformatics analysis tools that are easy to access and operate.
Collapse
|
2
|
Frangione E, Chung M, Casalino S, MacDonald G, Chowdhary S, Mighton C, Faghfoury H, Bombard Y, Strug L, Pugh T, Simpson J, Hao L, Lebo M, Lane WJ, Taher J, Lerner‐Ellis J. Genome Reporting for Healthy Populations-Pipeline for Genomic Screening from the GENCOV COVID-19 Study. Curr Protoc 2022; 2:e534. [PMID: 36205462 PMCID: PMC9874607 DOI: 10.1002/cpz1.534] [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] [Indexed: 11/06/2022]
Abstract
Genome sequencing holds the promise for great public health benefits. It is currently being used in the context of rare disease diagnosis and novel gene identification, but also has the potential to identify genetic disease risk factors in healthy individuals. Genome sequencing technologies are currently being used to identify genetic factors that may influence variability in symptom severity and immune response among patients infected by SARS-CoV-2. The GENCOV study aims to look at the relationship between genetic, serological, and biochemical factors and variability of SARS-CoV-2 symptom severity, and to evaluate the utility of returning genome screening results to study participants. Study participants select which results they wish to receive with a decision aid. Medically actionable information for diagnosis, disease risk estimation, disease prevention, and patient management are provided in a comprehensive genome report. Using a combination of bioinformatics software and custom tools, this article describes a pipeline for the analysis and reporting of genetic results to individuals with COVID-19, including HLA genotyping, large-scale continental ancestry estimation, and pharmacogenomic analysis to determine metabolizer status and drug response. In addition, this pipeline includes reporting of medically actionable conditions from comprehensive gene panels for Cardiology, Neurology, Metabolism, Hereditary Cancer, and Hereditary Kidney, and carrier screening for reproductive planning. Incorporated into the genome report are polygenic risk scores for six diseases-coronary artery disease; atrial fibrillation; type-2 diabetes; and breast, prostate, and colon cancer-as well as blood group genotyping analysis for ABO and Rh blood types and genotyping for other antigens of clinical relevance. The genome report summarizes the findings of these analyses in a way that extensively communicates clinically relevant results to patients and their physicians. © 2022 Wiley Periodicals LLC. Basic Protocol 1: HLA genotyping and disease association Basic Protocol 2: Large-scale continental ancestry estimation Basic Protocol 3: Dosage recommendations for pharmacogenomic gene variants associated with drug response Support Protocol: System setup.
Collapse
Affiliation(s)
- Erika Frangione
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada
| | - Monica Chung
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada
| | - Selina Casalino
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada
| | - Georgia MacDonald
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada
| | - Sunakshi Chowdhary
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada
| | - Chloe Mighton
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada,University of TorontoTorontoOntarioCanada,Unity Health TorontoTorontoOntarioCanada
| | | | - Yvonne Bombard
- University of TorontoTorontoOntarioCanada,Unity Health TorontoTorontoOntarioCanada
| | - Lisa Strug
- The Hospital for Sick ChildrenTorontoOntarioCanada
| | - Trevor Pugh
- University Health NetworkTorontoOntarioCanada,Ontario Institute for Cancer ResearchTorontoOntarioCanada
| | - Jared Simpson
- Ontario Institute for Cancer ResearchTorontoOntarioCanada
| | - Limin Hao
- Laboratory of Molecular MedicinePartners Personalized MedicineBostonMassachusetts
| | - Matthew Lebo
- Laboratory of Molecular MedicinePartners Personalized MedicineBostonMassachusetts,Harvard Medical School & Brigham and Women's HospitalBostonMassachusetts
| | - William J. Lane
- Harvard Medical School & Brigham and Women's HospitalBostonMassachusetts
| | - Jennifer Taher
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,University of TorontoTorontoOntarioCanada
| | - Jordan Lerner‐Ellis
- Mount Sinai HospitalSinai HealthTorontoOntarioCanada,Lunenfeld‐Tanenbaum Research InstituteSinai HealthTorontoOntarioCanada,University of TorontoTorontoOntarioCanada
| | | |
Collapse
|
3
|
Zhang Z, An HH, Vege S, Hu T, Zhang S, Mosbruger T, Jayaraman P, Monos D, Westhoff CM, Chou ST. Accurate long-read sequencing allows assembly of the duplicated RHD and RHCE genes harboring variants relevant to blood transfusion. Am J Hum Genet 2022; 109:180-191. [PMID: 34968422 DOI: 10.1016/j.ajhg.2021.12.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 12/07/2021] [Indexed: 12/18/2022] Open
Abstract
Next-generation sequencing (NGS) technologies have transformed medical genetics. However, short-read lengths pose a limitation on identification of structural variants, sequencing repetitive regions, phasing of distant nucleotide changes, and distinguishing highly homologous genomic regions. Long-read sequencing technologies may offer improvements in the characterization of genes that are currently difficult to assess. We used a combination of targeted DNA capture, long-read sequencing, and a customized bioinformatics pipeline to fully assemble the RH region, which harbors variation relevant to red cell donor-recipient mismatch, particularly among patients with sickle cell disease. RHD and RHCE are a pair of duplicated genes located within an ∼175 kb region on human chromosome 1 that have high sequence similarity and frequent structural variations. To achieve the assembly, we utilized palindrome repeats in PacBio SMRT reads to obtain consensus sequences of 2.1 to 2.9 kb average length with over 99% accuracy. We used these long consensus sequences to identify 771 assembly markers and to phase the RHD-RHCE region with high confidence. The dataset enabled direct linkage between coding and intronic variants, phasing of distant SNPs to determine RHD-RHCE haplotypes, and identification of known and novel structural variations along with the breakpoints. A limiting factor in phasing is the frequency of heterozygous assembly markers and therefore was most successful in samples from African Black individuals with increased heterogeneity at the RH locus. Overall, this approach allows RH genotyping and de novo assembly in an unbiased and comprehensive manner that is necessary to expand application of NGS technology to high-resolution RH typing.
Collapse
Affiliation(s)
- Zhe Zhang
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Hyun Hyung An
- Division of Hematology, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Sunitha Vege
- Immunohematology and Genomics, New York Blood Center, New York, NY 11101, USA
| | - Taishan Hu
- Immunogenetics Laboratory, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Shiping Zhang
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Timothy Mosbruger
- Immunogenetics Laboratory, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Pushkala Jayaraman
- Immunogenetics Laboratory, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Dimitri Monos
- Immunogenetics Laboratory, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman Schools of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Connie M Westhoff
- Immunohematology and Genomics, New York Blood Center, New York, NY 11101, USA
| | - Stella T Chou
- Division of Hematology, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Transfusion Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
| |
Collapse
|
4
|
Srivastava K, Fratzscher AS, Lan B, Flegel WA. Cataloguing experimentally confirmed 80.7 kb-long ACKR1 haplotypes from the 1000 Genomes Project database. BMC Bioinformatics 2021; 22:273. [PMID: 34039276 PMCID: PMC8150616 DOI: 10.1186/s12859-021-04169-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 05/04/2021] [Indexed: 12/18/2022] Open
Abstract
Background Clinically effective and safe genotyping relies on correct reference sequences, often represented by haplotypes. The 1000 Genomes Project recorded individual genotypes across 26 different populations and, using computerized genotype phasing, reported haplotype data. In contrast, we identified long reference sequences by analyzing the homozygous genomic regions in this online database, a concept that has rarely been reported since next generation sequencing data became available. Study design and methods Phased genotype data for a 80.6 kb region of chromosome 1 was downloaded for all 2,504 unrelated individuals of the 1000 Genome Project Phase 3 cohort. The data was centered on the ACKR1 gene and bordered by the CADM3 and FCER1A genes. Individuals with heterozygosity at a single site or with complete homozygosity allowed unambiguous assignment of an ACKR1 haplotype. A computer algorithm was developed for extracting these haplotypes from the 1000 Genome Project in an automated fashion. A manual analysis validated the data extracted by the algorithm. Results We confirmed 902 ACKR1 haplotypes of varying lengths, the longest at 80,584 nucleotides and shortest at 1,901 nucleotides. The combined length of haplotype sequences comprised 19,895,388 nucleotides with a median of 16,014 nucleotides. Based on our approach, all haplotypes can be considered experimentally confirmed and not affected by the known errors of computerized genotype phasing. Conclusions Tracts of homozygosity can provide definitive reference sequences for any gene. They are particularly useful when observed in unrelated individuals of large scale sequence databases. As a proof of principle, we explored the 1000 Genomes Project database for ACKR1 gene data and mined long haplotypes. These haplotypes are useful for high throughput analysis with next generation sequencing. Our approach is scalable, using automated bioinformatics tools, and can be applied to any gene. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04169-6.
Collapse
Affiliation(s)
- Kshitij Srivastava
- Laboratory Services Section, Department of Transfusion Medicine, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anne-Sophie Fratzscher
- Laboratory Services Section, Department of Transfusion Medicine, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Bo Lan
- Laboratory Services Section, Department of Transfusion Medicine, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Willy Albert Flegel
- Laboratory Services Section, Department of Transfusion Medicine, NIH Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
| |
Collapse
|