Genomic coordinates and continental distribution of 120 blood group variants reported by the 1000 Genomes Project

Characterization of Blood Group Variants in an Omani Population by Comparison of Whole Genome Sequencing and Serology

Although blood group variation was first described over a century ago, our understanding of the genetic variation affecting antigenic expression on the red blood cell surface in many populations is lacking. This deficit limits the ability to accurately type patients, especially as serological testing is not available for all described blood groups, and targeted genotyping panels may lack rare or population-specific variants. Here, we perform serological assays across 24 antigens and whole genome sequencing on 100 Omanis, a population underrepresented in genomic databases. We inferred blood group phenotypes using the most commonly typed genetic variants. The comparison of serological to inferred phenotypes resulted in an average concordance of 96.9%. Among the 22 discordances, we identify seven known variants in four blood groups that, to our knowledge, have not been previously reported in Omanis. Incorporating these variants for phenotype inference, concordance increases to 98.8%. Additionally, we describe five candidate variants in the Lewis, Lutheran, MNS, and P1 blood groups that may affect antigenic expression, although further functional confirmation is required. Notably, we identify several blood group alleles most common in African populations, likely introduced to Oman by gene flow over the last thousand years. These findings highlight the need to evaluate individual populations and their population history when considering variants to include in genotype panels for blood group typing. This research will inform future work in blood banks and transfusion services.

When and why is red blood cell genotyping applicable in transfusion medicine: a systematic review of the literature

This review aims to provide a better understanding of when and why red blood cell (RBC) genotyping is applicable in transfusion medicine. Articles published within the last 8 years in peer-reviewed journals were reviewed in a systematic manner. RBC genotyping has many applications in transfusion medicine including predicting a patient's antigen profile when serologic methods cannot be used, such as in a recently transfused patient, in the presence of autoantibody, or when serologic reagents are not available. RBC genotyping is used in prenatal care to determine zygosity and guide the administration of Rh immune globulin in pregnant women to prevent hemolytic disease of the fetus and newborn. In donor testing, RBC genotyping is used for resolving ABO/D discrepancies for better donor retention or for identifying donors negative for high-prevalence antigens to increase blood availability and compatibility for patients requiring rare blood. RBC genotyping is helpful to immunohematology reference laboratory staff performing complex antibody workups and is recommended for determining the antigen profiles of patients and prospective donors for accurate matching for C, E, and K in multiply transfused patients. Such testing is also used to determine patients or donors with variant alleles in the Rh blood group system. Information from this testing aides in complex antibody identification as well as sourcing rare allele-matched RBC units. While RBC genotyping is useful in transfusion medicine, there are limitations to its implementation in transfusion services, including test availability, turn-around time, and cost.

Cataloguing experimentally confirmed 80.7 kb-long ACKR1 haplotypes from the 1000 Genomes Project database

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.

An open-source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing

BACKGROUND AND OBJECTIVES

Next generation sequencing (NGS) has promising applications in transfusion medicine. Exome sequencing (ES) is increasingly used in the clinical setting, and blood group interpretation is an additional value that could be extracted from existing data sets. We provide the first release of an open-source software tailored for this purpose and describe its validation with three blood group systems.

MATERIALS AND METHODS

The DTM-Tools algorithm was designed and used to analyse 1018 ES NGS files from the ClinSeq® cohort. Predictions were correlated with serology for 5 antigens in a subset of 108 blood samples. Discrepancies were investigated with alternative phenotyping and genotyping methods, including a long-read NGS platform.

RESULTS

Of 116 genomic variants queried, those corresponding to 18 known KEL, FY and JK alleles were identified in this cohort. 596 additional exonic variants were identified KEL, ACKR1 and SLC14A1, including 58 predicted frameshifts. Software predictions were validated by serology in 108 participants; one case in the FY blood group and three cases in the JK blood group were discrepant. Investigation revealed that these discrepancies resulted from (1) clerical error, (2) serologic failure to detect weak antigenic expression and (3) a frameshift variant absent in blood group databases.

CONCLUSION

DTM-Tools can be employed for rapid Kell, Duffy and Kidd blood group antigen prediction from existing ES data sets; for discrepancies detected in the validation data set, software predictions proved accurate. DTM-Tools is open-source and in continuous development.

An outcome-based review of an accredited Specialist in Blood Banking (SBB) program: 25 years and counting

CONCLUSIONS

Specialist in Blood Banking (SBB) programs play an important role in preparing technologists to become leaders and contributors to the field of transfusion medicine through dedicated education and training. The SBB program at the National Institutes of Health (NIH) Clinical Center has graduated 55 students since 1994 with an overall pass rate of 96 percent for the American Society for Clinical Pathology (ASCP) SBB examination. Graduates hold positions in a variety of transfusion medicine-related fields, with hospitals, blood centers, and Immunohematology Reference Laboratories being the most common categories of employer. Projects completed as part of the program added to transfusion medicine knowledge as evidenced by publications and awards. Almost half of all projects completed led to publications (49%), and greater than 50 percent of submissions have been selected for the AABB Future Leaders Scholarship (previously known as AABB Fenwal Scholarship Award). The students have completed over 40 program value-added opportunities. This information was available for retrieval and review. In this review, we analyzed data for the last 25 years from the SBB program at the NIH Clinical Center on program statistics, student accomplishments (such as publications in peer-reviewed journals), program value-added opportunities (such as other publications and audits performed with our Quality Assurance office), and job procurement. The collected, reviewed, and organized data provided a useful internal self-assessment to review the history of our program and head into the future.

Banking with precision: transfusion medicine as a potential universal application in clinical genomics

PURPOSE OF REVIEW

To summarize the most recent scientific progress in transfusion medicine genomics and discuss its role within the broad genomic precision medicine model, with a focus on the unique computational and bioinformatic aspects of this emergent field.

RECENT FINDINGS

Recent publications continue to validate the feasibility of using next-generation sequencing (NGS) for blood group prediction with three distinct approaches: exome sequencing, whole genome sequencing, and PCR-based targeted NGS methods. The reported correlation of NGS with serologic and alternative genotyping methods ranges from 92 to 99%. NGS has demonstrated improved detection of weak antigens, structural changes, copy number variations, novel genomic variants, and microchimerism. Addition of a transfusion medicine interpretation to any clinically sequenced genome is proposed as a strategy to enhance the cost-effectiveness of precision genomic medicine. Interpretation of NGS in the blood group antigen context requires not only advanced immunohematology knowledge, but also specialized software and hardware resources, and a bioinformatics-trained workforce.

SUMMARY

Blood transfusions are a common inpatient procedure, making blood group genomics a promising facet of precision medicine research. Further efforts are needed to embrace transfusion bioinformatic challenges and evaluate its clinical utility.

Automated typing of red blood cell and platelet antigens from whole exome sequences

BACKGROUND

Genotyping has expanded the number red blood cell (RBC) and platelet (PLT) antigens that can readily be typed, but often represents an additional testing cost. The analysis of existing genomic data offers a cost-effective approach. We recently developed automated software (bloodTyper) for determination of RBC and PLT antigens from whole genome sequencing. Here we extend the algorithm to whole exome sequencing (WES).

STUDY DESIGN AND METHODS

Whole exome sequencing was performed on samples from 75 individuals. WES-based bloodTyper RBC and PLT typing was compared to conventional polymerase chain reaction (PCR) RHD zygosity testing and serologic and single-nucleotide polymorphism (SNP) typing for 38 RBC antigens in 12 systems (17 serologic and 35 SNPs) and 22 PLT antigens (22 SNPs). Samples from the first 20 individuals were used to modify bloodTyper to interpret WES followed by blinded typing of 55 samples.

RESULTS

Over the first 20 samples, discordances were noted for C, M, and N antigens, which were due to WES-specific biases. After modification, bloodTyper was 100% accurate on blinded evaluation of the last 55 samples and outperformed both serologic (99.67% accurate) and SNP typing (99.97% accurate) reflected by two Fy^b and one N serologic typing errors and one undetected SNP encoding a Jk_null phenotype. RHD zygosity testing by bloodTyper was 100% concordant with a combination of hybrid Rhesus box PCR and PCR-restriction fragment length polymorphism for all samples.

CONCLUSION

The automated bloodTyper software was modified for WES biases to allow for accurate RBC and PLT antigen typing. Such analysis could become a routing part of future WES efforts.

An update on the Scianna blood group system

This update of the Scianna blood group system (Brunker PA, Flegel WA. Scianna: the lucky 13th blood group system. Immunohematology 2011;27:41-57) provides the recent work on the genetic variation of ERMAP across more world populations, the elucidation of the molecular basis of an historical serologic case, new cases of antibodies in the system, the development of new serologic reagents, and new discoveries in the biology of the erythroid membrane associated protein (ERMAP). Although genetic variation in ERMAP has been extensively cataloged, nonsynonymous variants associated with alloantigens have remained limited, and no new antigens have been identified. The first case of a severe hemolytic transfusion reaction to anti-Sc2 has recently been reported, highlighting the importance of pursuing the possibility of antibodies to low-prevalence antigens via indirect antiglobulin testing as a routine component of all transfusion reaction investigations. The expanding use of molecular testing in blood centers and transfusion services has uncovered a wider population distribution of Scianna antigens and heightened the awareness of this blood group system. The International Society of Blood Transfusion recognizes seven antigens in the Scianna blood group system 13.

Developments beyond blood group serology in the genomics era

Blood group serology and single nucleotide polymorphism-based genotyping platforms are accurate but do not provide a comprehensive cover for all 36 blood group systems and do not cover the antigen diversity observed among population groups. This review examines the extent to which genomics is shaping blood group serology. Resources for genomics include the Human Reference Genome Sequence assembly; curated blood group tables listing variants; public databases providing information on genetic variants from world-wide studies; and massively parallel sequencing technologies. Blood group genomic studies span the spectrum, from bioinformatic data mining of huge data sets containing whole genome and whole exome information to laboratory investigations utilising targeted sequencing approaches. Blood group predictions based on genome sequencing and genomic studies are proving accurate, and have shown utility in both research and reference settings. Overall, studies confirm the potential for blood group genomics to reshape donor and patient transfusion management strategies to provide more compatible blood transfusions.