ABO Genotyping finds more A2 to B kidney transplant opportunities than lectin-based subtyping.

ABO compatibility is important for kidney transplantation, with longer waitlist times for blood group B kidney transplant candidates. However, kidneys from non-A1 (eg, A2) subtype donors, which express less A antigen, can be safely transplanted into group B recipients. ABO subtyping is routinely performed using anti-A1 lectin, but DNA-based genotyping is also possible. Here, we compare lectin and genotyping testing. Lectin and genotype subtyping was performed on 554 group A deceased donor samples at 2 transplant laboratories. The findings were supported by 2 additional data sets of 210 group A living kidney donors and 124 samples with unclear lectin testing sent to a reference laboratory. In deceased donors, genotyping found 65% more A2 donors than lectin testing, most with weak lectin reactivity, a finding supported in living donors and samples sent for reference testing. DNA sequencing and flow cytometry showed that the discordances were because of several factors, including transfusion, small variability in A antigen levels, and rare ABO∗A2.06 and ABO∗A2.16 sequences. Although lectin testing is the current standard for transplantation subtyping, genotyping is accurate and could increase A2 kidney transplant opportunities for group B candidates, a difference that should reduce group B wait times and improve transplant equity.

Multiple GYPB gene deletions associated with the U- phenotype in those of African ancestry.

BACKGROUND: The MNS blood group system is defined by three homologous genes: GYPA, GYPB, and GYPE. GYPB encodes for glycophorin B (GPB) carrying S/s and the "universal" antigen U. RBCs of approximately 1% of individuals of African ancestry are U- due to absence of GPB. The U- phenotype has long been attributed to a deletion encompassing GYPB exons 2 to 5 and GYPE exon 1 (GYPB*01N). STUDY DESIGN AND METHODS: Samples from two U-individuals underwent Illumina short read whole genome sequencing (WGS) and Nanopore long read WGS. In addition, two existing WGS datasets, MedSeq (n = 110) and 1000 Genomes (1000G, n = 2535), were analyzed for GYPB deletions. Deletions were confirmed by Sanger sequencing. Twenty known U- donor samples were tested by a PCR assay to determine the specific deletion alleles present in African Americans. RESULTS: Two large GYPB deletions in U- samples of African ancestry were identified: a 110 kb deletion extending left of GYPB (DEL_B_LEFT) and a 103 kb deletion extending right (DEL_B_RIGHT). DEL_B_LEFT and DEL_B_RIGHT were the most common GYPB deletions in the 1000 Genomes Project 669 African genomes (allele frequencies 0.04 and 0.02). Seven additional deletions involving GYPB were seen in African, Admixed American, and South Asian samples. No samples analyzed had GYPB*01N. CONCLUSIONS: The U- phenotype in those of African ancestry is primarily associated with two different complete deletions of GYPB (with intact GYPE). Seven additional less common GYPB deletion backgrounds were found. GYPB*01N, long assumed to be the allele commonly encoding U- phenotypes, appears to be rare.

Overcoming the challenges of interpreting complex and uncommon RH alleles from whole genomes.

BACKGROUND AND OBJECTIVES: Rh is one of the most diverse and complex blood group systems. Recently, next generation sequencing (NGS) has proven to be a viable option for RH genotyping. We have developed automated software (bloodTyper) for determining alleles encoding RBC antigens from NGS-based whole genome sequencing (WGS). The bloodTyper algorithm has not yet been optimized and evaluated for complex and uncommon RH alleles. MATERIALS AND METHODS: Twenty-two samples with previous polymerase chain reaction (PCR) and Sanger sequencing-based RH genotyping underwent WGS. bloodTyper was used to detect RH alleles including those defined by structural variation (SV) using a combination of three independent strategies: sequence read depth of coverage, split reads and paired reads. RESULTS: bloodTyper was programmed to identify D negative and positive phenotypes as well as the presence of alleles encoding weak D, partial D and variant RHCE. Sequence read depth of coverage calculation accurately determined RHD zygosity and detected the presence of RHD/RHCE hybrids. RHCE*C was determined by sequence read depth of coverage and by split read methods. RHD hybrid alleles and RHCE*C were confirmed by using a paired read approach. Small SVs present in RHCE*CeRN and RHCE*ceHAR were detected by a combined read depth of coverage and paired read approach. CONCLUSIONS: The combination of several different interpretive approaches allowed for automated software based-RH genotyping of WGS data including RHD zygosity and complex compound RHD and RHCE heterozygotes. The scalable nature of this automated analysis will enable RH genotyping in large genomic sequencing projects.

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 Fyb and one N serologic typing errors and one undetected SNP encoding a Jknull 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.

A whole genome approach for discovering the genetic basis of blood group antigens: independent confirmation for P1 and Xga.

BACKGROUND: Although P1 and Xga are known to be associated with the A4GALT and XG genes, respectively, the genetic basis of antigen expression has been elusive. Recent reports link both P1 and Xga expression with nucleotide changes in the promotor regions and with antigen-negative phenotypes due to disruption of transcription factor binding. STUDY DESIGN AND METHODS: Whole genome sequencing was performed on 113 individuals as part of the MedSeq Project with serologic RBC antigen typing for P1 (n = 77) and Xga (n = 15). Genomic data were analyzed by two approaches, nucleotide frequency correlation and serologic correlation, to find A4GALT and XG changes associated with P1 and Xga expression. RESULTS: For P1, the frequency approach identified 29 possible associated nucleotide changes, and the serologic approach revealed four among them correlating with the P1+/P1- phenotype: chr22:43,115,523_43,115,520AAAG/delAAAG (rs66781836); chr 22:43,114,551C/T (rs8138197); chr22:43,114,020 T/G (rs2143918); and chr22:43,113,793G/T (rs5751348). For Xga , the frequency approach identified 82 possible associated nucleotide changes, and among these the serologic approach revealed one correlating with the Xg(a+)/Xg(a-) phenotype: chrX:2,666,384G/C (rs311103). CONCLUSION: A bioinformatics analysis pipeline was created to identify genetic changes responsible for RBC antigen expression. This study, in progress before the recently published reports, independently confirms the basis for P1 and Xga . Although this enabled molecular typing of these antigens, the Y chromosome PAR1 region interfered with Xga typing in males. This approach could be used to identify and confirm the genetic basis of antigens, potentially replacing the historical approach using family pedigrees as genomic sequencing becomes commonplace.

PIGG defines the Emm blood group system.

Emm is a high incidence red cell antigen with eight previously reported Emm- probands. Anti-Emm appears to be naturally occurring yet responsible for a clinically significant acute hemolytic transfusion reaction. Previous work suggests that Emm is located on a GPI-anchored protein, but the antigenic epitope and genetic basis have been elusive. We investigated samples from a South Asian Indian family with two Emm- brothers by whole genome sequencing (WGS). Additionally, samples from four unrelated Emm- individuals were investigated for variants in the candidate gene. Filtering for homozygous variants found in the Emm- brothers and by gnomAD frequency of < 0.001 resulted in 1818 variants with one of high impact; a 2-bp deletion causing a frameshift and premature stop codon in PIGG [NM_001127178.3:c.2624_2625delTA, p.(Leu875*), rs771819481]. PIGG encodes for a transferase, GPI-ethanolaminephosphate transferase II, which adds ethanolamine phosphate (EtNP) to the second mannose in a GPI-anchor. The four additional unrelated Emm- individuals had various PIGG mutations; deletion of Exons 2-3, deletion of Exons 7-9, insertion/deletion (indel) in Exon 3, and new stop codon in Exon 5. The Emm- phenotype is associated with a rare deficiency of PIGG, potentially defining a new Emm blood group system composed of EtNP bound to mannose, part of the GPI-anchor. The results are consistent with the known PI-linked association of the Emm antigen, and may explain the production of the antibody in the absence of RBC transfusion. Any association with neurologic phenotypes requires further research.

Automated typing of red blood cell and platelet antigens: a whole-genome sequencing study.

BACKGROUND: There are more than 300 known red blood cell (RBC) antigens and 33 platelet antigens that differ between individuals. Sensitisation to antigens is a serious complication that can occur in prenatal medicine and after blood transfusion, particularly for patients who require multiple transfusions. Although pre-transfusion compatibility testing largely relies on serological methods, reagents are not available for many antigens. Methods based on single-nucleotide polymorphism (SNP) arrays have been used, but typing for ABO and Rh-the most important blood groups-cannot be done with SNP typing alone. We aimed to develop a novel method based on whole-genome sequencing to identify RBC and platelet antigens. METHODS: This whole-genome sequencing study is a subanalysis of data from patients in the whole-genome sequencing arm of the MedSeq Project randomised controlled trial (NCT01736566) with no measured patient outcomes. We created a database of molecular changes in RBC and platelet antigens and developed an automated antigen-typing algorithm based on whole-genome sequencing (bloodTyper). This algorithm was iteratively improved to address cis-trans haplotype ambiguities and homologous gene alignments. Whole-genome sequencing data from 110 MedSeq participants (30 × depth) were used to initially validate bloodTyper through comparison with conventional serology and SNP methods for typing of 38 RBC antigens in 12 blood-group systems and 22 human platelet antigens. bloodTyper was further validated with whole-genome sequencing data from 200 INTERVAL trial participants (15 × depth) with serological comparisons. FINDINGS: We iteratively improved bloodTyper by comparing its typing results with conventional serological and SNP typing in three rounds of testing. The initial whole-genome sequencing typing algorithm was 99·5% concordant across the first 20 MedSeq genomes. Addressing discordances led to development of an improved algorithm that was 99·8% concordant for the remaining 90 MedSeq genomes. Additional modifications led to the final algorithm, which was 99·2% concordant across 200 INTERVAL genomes (or 99·9% after adjustment for the lower depth of coverage). INTERPRETATION: By enabling more precise antigen-matching of patients with blood donors, antigen typing based on whole-genome sequencing provides a novel approach to improve transfusion outcomes with the potential to transform the practice of transfusion medicine. FUNDING: National Human Genome Research Institute, Doris Duke Charitable Foundation, National Health Service Blood and Transplant, National Institute for Health Research, and Wellcome Trust.