Next generation sequencing and blood group genotyping: a narrative review

Introduction

The introduction of genotyping technologies for blood group antigen (BGA) typing has changed the practice of transfusion medicine (1-3). Molecular methods were at first a complement to serological tests, then became an alternative or the only solution. They have a great many useful applications which include the blood grouping of recently transfused blood recipients, autoimmune hemolytic anemia patients or during some monoclonal therapies, individuals with rare blood antigens, inconclusive serological results or multi-specific complex antibodies and finally, non-invasive fetal antigen genotyping and paternal RHD zygosity (4-9). Also, genotyping facilitates the identification of antigen-negative blood donors compatible with immunized patients against common antigens and extended BGA matching for transfusion dependent patients (10,11). But the currently routinely-used genotyping workflows determine a limited number of the most probable single nucleotide variations (SNV) using predefined primers/probes and frequently not solving complex cases which then require further laborious and time-consuming investigations with the classical sequencing of many exons (11-15). Thus, BGA genotyping workflow is evolving from SNV typing for a single patient to fully automatic high-throughput massive parallel sequencing of a whole genome or several exomes or targeted regions for patients and donor screening (11,16). Among the techniques from the genetic toolbox, sequencing has been an expensive and time-consuming solution used as a last resort but it has proved to be highly helpful in investigating the causes of the most atypical serological reactions and learning about underlying phenomena (17-19). Undoubtedly, there are several advantages of next generation sequencing (NGS) that overcome the limitations of current BGA genotyping platforms (20,21). The crucial issue is the revealing of the direct sequence of the entire gene or genes, exons, the promotors or intronic regions as well as the associated genes in one test in a relatively short time for a different number of individuals. Also, NGS technology determines any nucleotide variants in the tested sequences, quantifies sequences for estimation of copy number variation (CNV) or chimerism and maps of haplotype and cis/trans location (22,23). Moreover, the capacity of NGS is cost-effective and useful for high throughput screening (24). In the field of transfusion medicine there are nearly 50 genes known to be involved in expressing BGAs that are an ideal goal for the high throughput and capacity of massive parallel sequencing.

Sequencing of the genotype of BGAs has evolved in the last 30 years, since the discovery and sequencing of genes encoding the basic blood group systems (25). Over these years Sanger sequencing has been applied to describe single fragment/exon or gene encoding BGAs in single cases with discrepant serological and genetic results (1-3,17,18). The massive parallel sequencing has opened up new possibilities in the immunohematological field giving the opportunity to screen numbers of donors and alleles. The high through-put and huge capacity was firstly applied to HLA-typing and now it is a routine technique in these diagnostics (24). A characteristic feature of this method is the sequencing of millions of relatively short reads in one run. Researchers decide for themselves how deeply to sequence and how many fragments to read from how many people at the same time, which allows for a balance between multiplexing the desired features in research with BGA panels for hundreds of individuals (e.g., donor screening) or testing millions of reads with extremely high sensitivity for several patients (e.g., in non-invasive fetal testing from the maternal plasma of immunized pregnant women) or extracting BGA alleles from existing WES/WGS databases (9,16,26,27). Different sequencing kits offer a high flexibility fitting the workflow to the aims and budgets of studies. This study discusses the progress made in BGA typing using NGS technology. The review presents data from the articles published as a full-length papers in PubMed and reported in conference abstracts. We present the following article in accordance with the Narrative Review reporting checklist (available at https://aob.amegroups.com/article/view/10.21037/aob-21-39/rc).

NGS platforms applied for BGA genotyping

Currently, two NGS platforms are used for BGA typing known as the second generation: Sequencing-by-synthesis offered by Illumina and semiconductor sequencing on Ion Torrent from Thermofisher (28,29). The first technology is similar to the classical solution with the detection of fluorescence of each incorporated nucleotide. A flowcell coated with clusters of oligo-adaptors is flushed by a sequenced library. After capture by the adapters, cluster synthesis is generated and sequencing is followed by the detection of dye-labelled nucleotides. The solution makes sequencing from both ends possible (paired-end sequencing). The capacity of the system depends on the instrument used for testing (e.g., for the MiSeq it is about 45 milion paired reads per run). The time of each run is reported to be the main limitation of the system. Also, the error rate is estimated to be about 0.1% mainly due to non-specific substitutions. But the Illumina system is reported as being the main technology applied in BGA sequencing by various researchers.

The second semiconductor Ion Torrent method detects changes in electric potentials generated during the incorporation of complementary nucleotides. Sequencing beads coated with clonal templates are loaded into microwells covering a chip. The synthesis of each nucleotide releases protons changing the potential that is measured during each cycle. The main limitation is associated with about 1% error rate in the detection of changes during the sequencing of homopolymer fragments. But, on the other hand, such manners of detection take only a few hours per run. The capacity of the system is about 200–400 bp reads with a depth of 0.4–5.5 million reads. The solution is dedicated for panel sequencing and it was the first to be used for massive screening of regions of interest (ROI) encoding clinically relevant BGAs for blood donors.

BGA NGS strategies

Since 2011, when the first report of applying NGS for RHD typing was published, many NGS-based BGA typings have been described presenting a wide range of efforts to adjust and implement this new technology into routine immunohematological diagnostics (see Table 1) (69). Currently, four NGS strategies are applied for BGA genotyping often combined with platelet antigen (HPA) and even neutrophil antigen (HNA) genotyping, also useful in transfusion medicine.

The most common is targeted NGS focused on a single locus or employing a designed panel for several regions/genes/exons of interest encoding BGAs/HPAs. Fichou et al. were the first to prove that NGS using a customized panel for the Ion Torrent platform makes genotyping of 18 regions encoding BGAs for four individuals possible in a single run (33). Similarly, in our work we applied a panel for sequencing ROIs identifying six blood groups and five platelet antigen SNVs in 55 cases and due to an extremely high depth of reads determining natural chimerism in one donor (35). Also Jakobsen et al. used a panel designed for 15 alleles encoding rare BGAs not included in routine commercial assays (44). Dezan et al. who applied the separate RHD/RHCE libraries identified RH variants in 35 SCD patients clarifying their Rh phenotypes and improving their transfusion support since in 62% of the cases Rh typings were classified inaccurately by serology (38). Also Tounsi et al. focused on RHD and RHAG sequencing but using long-product PCR reconstructed haplotypes by identifying an intronic SNV pattern linked to the DcE haplotype but not to DCe, DCE or Dce (40). They described two reference sequences of the whole RHD. El Wafi et al., also focused on the RHD gene, revealing new intronic SNVs affecting mRNA splicing in 9 RhD negative patients (39). Wheeler et al. presented a report on the identification of RH structural variants using the NGS panel on the Illumina platform (41). Wu et al. identified novel ABO variants as well as ABO structural variants and microchimerism using an ABO targeted panel for 24 patients with discrepant serological results (32). Boccoz et al. demonstrated fast and complete antigen screening using a customized panel for 9 BGs in order to identify rare blood units by testing 95 donors in a single run of the Illumina platform (45). Recently Roulis et al. have presented a comprehensive panel for targeted exome sequencing of BGA, HPA and HNA genes correctly predicting genotypes for 99.5% of BGA and 100% HPA and HNA alleles (37). Also Vorholt et al. have described a HPA panel for high-throughput screening to build up HPA donor registry and provide matched blood products (46).

The above studies show the wide range of applications of this panel method in the field of blood grouping and how NGS technology has complemented basic knowledge in the context of immunohematology (see Table 2). The panels were precisely designed to SNV regions, exons and whole genes and also detected non-coding or flanking fragments known to interfere with the expression of some BGAs. Targeted NGS was focused on clinically significant immunogenic antigens such as: ABO, Rh but also on the rare antigens Xg(a-), P1. Various groups were tested: donors with defined phenotypes/genotypes, individuals with discrepant serological results and transfusion-dependent patients. The Ion Torrent platform with ion chemistry was the one most often used in this NGS strategy but sequencing-by-synthesis on the Illumina system was also employed successfully. Depending on the aim of the study, modified initial PCR protocols were used with long-product amplification, separating libraries or sequence specific primers for highly homological genes as well as different amplicon sizes, read depths and numbers of tested samples per NGS run.

The main advantage of the panel strategy is simplifying the bioinformatic analysis of limited NGS data focused only on SNVs and regions associated with the expressing BGAs. The researchers applied different bioinformatic tools for manual data processing, alignment, variant calling or copy number analysis and additional optimizing calculations for the interpretation of the genotype.

The BGA genotypes predicted using NGS panels had high correlation to serology and conventional genetic tests. However, often antigens encoded by homologous genes (RHD/CE, GYPA/B, Rg-Ch and ABO) were excluded from the final analysis. Targeted NGS of even a small number of cases revealed the presence of unknown SNVs and structural variants in coding or noncoding ROIs (see Table 2). Also, due to its high read depth, targeted NGS circumvented some misalignments in the structural variation analysis of highly homological regions, facilitated CNV analysis and was reported to determine a natural microchimerism. On the other hand, the strategy proved to have a huge capacity for high-throughput screening of a number of regions encoding clinically significant antigens in a number of individuals. The best example is Lang et al.’s report describing NGS of ABO exons 6 and 7 together with HLA that revealed novel 287 ABO variants in 1,693,287 bone marrow donors (30).

The targeted NGS strategy is also applied in non-invasive prenatal testing (NIPT) of fetal antigen genotype (see Table 3). A deep quantitative analysis of both maternal and fetal—paternally inherited sequences from cell-free plasma DNA of pregnant women enables an estimation of feto-maternal chimerism and the risk of fetal/neonatal disease in alloimmunized cases. There are two NIPT protocols—the first involves sequencing only ROIs encoding the incompatible antigen for testing several pregnant women with antibodies of the same specificity; the second—uses a wide panel of ROIs encoding the most clinically significant antigens together with additional fetal markers for testing pregnant women with different antibodies and confirming target-negative results in one experiment.

The second NGS strategy is based on previously generated whole genome sequencing (WGS) data filtered in the context of BGAs/HPAs. Möller et al. showed the possibilities of retrospective analysis of 36 blood group systems from a 1000 Genome database of over 2,500 individuals, obtained from classical sequencing, revealing 1,241 non-synonymous variants and creating an Erythrogene database of the complete coding regions of 43 blood group related genes and finding a novel ABO variant frequent in individuals of African origin (79,80). Lane et al. presented firstly an analysis of 45 blood group and 6 HPA related genes for one individual from the MedSeqProject WGS database proving the NGS utility in the field of transfusion medicine (60). Then, using automated WGS-based antigen typing software (bloodTyper), they filtered 38 BGAs and 22 HPA antigens for a few hundred cases from the INTERVAL database with 99% accuracy and later revealed SNVs correlating with Xg(a-) and P1 negative phenotypes in over 100 individuals from the MedSeqProject database (61,63). All the above published reports proved the utility of WGS-based data for BGA prediction. Untargeted sequencing of the whole genome produces the largest data, but short reads with low read depth may lead to misalignment of homologous fragments. On the other hand, WGS data gives a full landscape of coding, noncoding and regulatory regions that may have an impact on the final BGA phenotype and is useful for solving complex cases (62,64-66). In this NGS strategy an automated tools for WGS analysis is necessary for extracting a number of BGA genotypes in a number of individuals. And a novel algorithm, called RHtyper, for RH variation characteristics from WGS data was proposed by Chang et al. (66).

The third WES-based NGS strategy makes the prediction of BGAs/HPAs from coding regions of existing individual data possible. Fox et al. presented the usefulness of this sort of NGS for ABO genotyping in over 6,000 WES-typed cases revealing novel structural variants (31). Schoeman et al. employed a commercial WES-panel for predicting 28 BGAs in 28 blood donors using manual analysis of CNVs of exons to identify homological RH and GYP genes (36). Later, from WES-data of indigenous Australians, they determined the complete BG profile of this ethnic cohort revealing 152 novel variants (47). Chou et al. proved the utility of WES analysis for RH genotyping to improve red blood cell matching in SCD patients (50). Lane et al. achieved 100% accuracy in BG analysis using the automated WES-based bloodTyper software (48). The WES strategy is very useful for resolving the genetic background of orphan and novel antigens or even new blood group systems such as SARA, Mi(a), k, In(Lu), CORS, DEL, AnWj, At(a), Vel, AUG, KANNO, Xg, CTL2, MAM, PEL, EMM (49,53-59,81-88). WES is cheaper and faster than WGS and there are commercial exome panels available. WES gives data on exons and intronic regions near exon boundaries and in the context of BGA genotyping only P1, Xg(a) and ABO intron 1 are now known to have SNVs not captured by WES but interfering with the presence of BGAs (20,63). The read depth and throughput in the WES strategy are higher than in WGS, but short reads may be misaligned to homological genes and also CNV analysis is more complex, however, WES produces small data files facilitating bioinformatic work and data storage. Recently, Lane et al. have proposed a flexible software called bloodTyper, adapted to analyze data from WES data as well as BGA panels (48). Also, Montemayor et al. have just provided DTM-Tools—an open-source software for Kell, Duffy and Kidd blood group interpretation from an existing 1,018 ES data files generated for the ClinSeq cohort (52). The newest long-read single molecule sequencing technique, called third generation, helps to overcome the limitation of short reads where phasing of associated polymorphisms generates complicated bioinformatic analysis. The solution is very useful for the determination of reference BGA haplotypes and the confirmation of novel findings (see Tables 1,2). The technology does not require preparation of any library but works directly from genomic DNA. Here, there are two technical solutions for the detection of sequencing based on fluorescence or electric current. In Pacific Bioscience the dye-labelled nucleotides emit signals in real time of incorporation allowing up to 45 kb of single read (89). The second solution—Oxford Nanopore sequencing is based on passing of DNA strands through protein nanopores embedded in a membrane where special electric sensors connected to the pores measure the change in electric current made by altering the conformation of the pores (90). The technique is prone to concentration of DNA strands and homopolymer fragments. However, the main limitation of single molecule sequencing is the high percentage of indel errors that requires correcting through deeper sequencing and also sophisticated bioinformatic analysis.

Genetic background of BGAs and NGS

The utility of short-read systems for BGA genotyping depends on the kind of mutation responsible for a wide landscape of antigen variety. Firstly, genetic variants encoding BGAs mainly belong to SNVs exchanging one nucleotide and converting an amino acid (1,91). The change is crucial for expressing an antithetical epitope and triggering an antibody response in individuals that lack the opposite structure in their red cell membranes. Also, one or several single changes are met in genes encoding enzymes relevant for the synthesis of polysaccharide antigens; altering their reactivity and substrate specificity, leading to e.g., a different pattern of ABO forward/reverse typing. SNVs may occur in coding as well as splice site regions, regulatory elements and promotors (92). Such mutations lead to premature stop codons; disturbing splicing or turning off the transcription and finally causing serologically null phenotypes directly in peptide chains or indirectly through inactive enzymes (e.g., ABO*O.01). Also, there are deletions of short or long fragments (i.e., Vel and RhD negativity) and conversions of highly homological genes (i.e., RHD/RHCE, GYPA/B/E) leading to hybrid variants that express new antigens or a lack of some (partial D). Diverse molecular backgrounds of BGAs lead to different strategies to analyze NGS results. All the reports of NGS of SNV-based antigens have a high accuracy independent of the NGS platforms and designed panels used (see Table 1). But, as mentioned above, the phasing of several SNVs that are located far away in the presence of the second allele and the mapping of short reads to the correct reference in hybrid variants, formed by RHD/RHCE, GYPA/GYPB and ABO, are challenging and demand sophisticated bioinformatic analysis (31,62,65,66). Also, the correct primer design to the most conservative regions is crucial for omitting allele dropouts and falsely antigen-negative results (22,23).

Applications of BGA NGS

In the field of immunohematology NGS technology is used for several applications but mainly for blood donor screening, to puzzle out the background of complicated serological cases and for NIPT of fetal antigens.

Short-read NGS is useful mainly for BGA panel genotyping for donor screening in order to extend the donor registry for immunized and transfusion dependent patients (see Table 1). High throughput capacity makes this sort of typing cost-effective. Collecting such big data from panels for hundreds of multiplexed donors with automatic transfer of genotype to serological phenotypes will facilitate widely-matched blood product provision to recipients (93). This application for screening is analogous to donor HLA sequencing which has been successfully implemented in the field of transplantation (24). But HLA NGS diagnostics also covers recipient screening. Nowadays, NGS methodology is too expensive for the routine BGA genotyping of a single patient. However, bioinformatic extraction of donors, genetically fitting a patient’s wide BGA genotype, leads to perfect donor/recipient blood matching, undoubtedly eliminating alloimmunization events. For now, patient’s sequencing is narrowed down to a group of transfusion-dependent recipients and the most complicated cases with discrepant serological results (42,43). But extracting BGA data from individuals with previously performed WES/WGS for other reasons is becoming an important tool in the era of personalized medicine (93). For blood recipients in whom there are difficulties in the serological typing of a single antigen NGS, it also gives the possibility of low-cost testing through joining the library for only a single target gene to experiments performed with a similar depth of sequencing.

All applications generate basic knowledge on BGA genetics in association with a determined serological pattern of reactions and often with the discovery of a new allele. So now we are still revealing unknown sequences widening the database of alleles encoding BGAs in particular populations and ethnic groups. Nearly all publications on applying NGS for BGA genotyping contain reports of a new polymorphism found unexpectedly in serologically characterized individuals (listed in Table 2).

Conclusions

It should be emphasized that at present BGA NGS is a technology that is being set up, implemented and requires validation. BGA NGS is at the stage of collecting and completing genetic data with non-ideal tools (phasing of short reads, reading errors, allele drop-outs, homological allele variations) but it is also at the stage of revealing the wide world of BGA alleles and building reference databases, designing software for correct analysis that omits any technical limitations of the technology and creating programs similar to a Google translator for the automatic conversion of genetic data to transfusion language (48,62,94). Currently, the correlation between NGS and phenotyping/genotyping results varies from 92% to 99%. So far, there are no commercially available BGA NGS panels for CE-IVD diagnostics combined with dedicated software for translating genotype to phenotype. Undoubtedly, it is a matter of time until such tests, analogically to HLA, will be commercially available on the immunohematological market as well as dedicated external quality control programs.

Summarizing, the recently published works on NGS-based genotyping of BGAs have made a huge contribution to understanding the allelic variants responsible for the presence of antigenic specificities. Applying NGS protocols is useful for donor BGA screening as well as the scientific research of novel SNVs and structural variations and also in building the basis of reference sequences for BGA interpretation. The current state of BGA NGS requires a deep standardization and validation of all the steps including bioinformatic tools as well as ethical guidelines before it can be implemented in routine BGA diagnostics. Also, setting a new regulation for BG NGS validation is a separate issue, because the number of tested samples in the classical serological validation does not correspond to massive parallel sequencing technology.

Acknowledgments

Funding: The work was supported from the IHTM funds (No. B2-T4-2021).

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editor (Frederik Banch Clausen) for the series “Blood Group Genotyping” published in Annals of Blood. The article has undergone external peer review.

Reporting Checklist: The author has completed the Narrative Review reporting checklist. Available at https://aob.amegroups.com/article/view/10.21037/aob-21-39/rc

Peer Review File: Available at https://aob.amegroups.com/article/view/10.21037/aob-21-39/prf

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://aob.amegroups.com/article/view/10.21037/aob-21-39/coif). The series “Blood Group Genotyping” was commissioned by the editorial office without any funding or sponsorship. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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