BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data

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.

A machine-learning method for biobank-scale genetic prediction of blood group antigens

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank-scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.

Next Generation Sequencing of Red Blood Cell Antigens in Transfusion Medicine: Systematic Review and Meta-Analysis

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.

Haplotype sequence collection of ABO blood group alleles by long-read sequencing reveals putative A1-diagnostic variants

In the era of blood group genomics, reference collections of complete and fully resolved blood group gene alleles have gained high importance. For most blood groups, however, such collections are currently lacking, as resolving full-length gene sequences as haplotypes (ie, separated maternal/paternal origin) remains exceedingly difficult with both Sanger and short-read next-generation sequencing. Using the latest third-generation long-read sequencing, we generated a collection of fully resolved sequences for all 6 main ABO allele groups: ABO∗A1/A2/B/O.01.01/O.01.02/O.02. We selected 77 samples from an ABO genotype data set (n = 25 200) of serologically typed Swiss blood donors. The entire ABO gene was amplified in 2 overlapping long-range polymerase chain reactions (covering ∼23.6 kb) and sequenced by long-read Oxford Nanopore sequencing. For quality validation, 2 samples per ABO group were resequenced using Illumina and Pacific Biosciences technology. All 154 full-length ABO sequences were resolved as haplotypes. We observed novel, distinct sequence patterns for each ABO group. Most genetic diversity was found between, not within, ABO groups. Phylogenetic tree and haplotype network analyses highlighted distinct clades of each ABO group. Strikingly, our data uncovered 4 genetic variants putatively specific for ABO∗A1, for which direct diagnostic targets are currently lacking. We validated A1-diagnostic potential using whole-genome data (n = 4872) of a multiethnic cohort. Overall, our sequencing strategy proved powerful for producing high-quality ABO haplotypes and holds promise for generating similar collections for other blood groups. The publicly available collection of 154 haplotypes will serve as a valuable resource for molecular analyses of ABO, as well as studies about the function and evolutionary history of ABO.

Prediction of various blood group systems using Korean whole-genome sequencing data

Aims

This study established blood group analysis methods using whole-genome sequencing (WGS) data and conducted blood group analyses to determine the domestic allele frequency using public data from the Korean whole sequence analysis of the Korean Reference Genome Project conducted by the Korea Disease Control and Prevention Agency (KDCA).

Materials and methods

We analyzed the differences between the human reference sequences (hg19) and the conventional reference cDNA sequences of blood group genes using the Clustal Omega website, and established blood group analysis methods using WGS data for 41 genes, including 39 blood group genes involved in 36 blood group antigens, as well as the GATA1 and KLF1 genes, which are erythrocyte-specific transcription factor genes. Using CLC genomics Workbench 11.0 (Qiagen, Aarhus, Denmark), variant analysis was performed on these 41 genes in 250 Korean WGS data sets, and each blood group’s genotype was predicted. The frequencies for major alleles were also investigated and compared with data from the Korean rare blood program (KRBP) and the Erythrogene database (East Asian and all races).

Results

Among the 41 blood group-related genes, hg19 showed variants in the following genes compared to the conventional reference cDNA: GYPA, RHD, RHCE, FUT3, ACKR1, SLC14A1, ART4, CR1, and GCNT2. Among 250 WGS data sets from the Korean Reference Genome Project, 70.6 variants were analyzed in 205 samples; 45 data samples were excluded due to having no variants. In particular, the FUT3, GNCT2, B3GALNT1, CR1, and ACHE genes contained numerous variants, with averages of 21.1, 13.9, 13.4, 9.6, and 7.0, respectively. Except for some blood groups, such as ABO and Lewis, for which it was difficult to predict the alleles using only WGS data, most alleles were successfully predicted in most blood groups. A comparison of allele frequencies showed no significant differences compared to the KRBP data, but there were differences compared to the Erythrogene data for the Lutheran, Kell, Duffy, Yt, Scianna, Landsteiner-Wiener, and Cromer blood group systems. Numerous minor blood group systems that were not available in the KRBP data were also included in this study.

Conclusions

We successfully established and performed blood group analysis using Korean public WGS data. It is expected that blood group analysis using WGS data will be performed more frequently in the future and will contribute to domestic data on blood group allele frequency and eventually the supply of safe blood products.

Using Whole Genome Sequencing to Characterize Clinically Significant Blood Groups Among Healthy Older Australians

There have been no comprehensive studies of a full range of blood group polymorphisms within the Australian population. This problem is compounded by the absence of any databases carrying genomic information on chronically transfused patients and low frequency blood group antigens in Australia. Here, we use RBCeq, a web server–based blood group genotyping software, to identify unique blood group variants among Australians and compare the variation detected vs global data. Whole-genome sequencing data were analyzed for 2796 healthy older Australians from the Medical Genome Reference Bank and compared with data from 1000 Genomes phase 3 (1KGP3) databases comprising 661 African, 347 American, 503 European, 504 East Asian, and 489 South Asian participants. There were 661 rare variants detected in this Australian sample population, including 9 variants that had clinical associations. Notably, we identified 80 variants that were computationally predicted to be novel and deleterious. No clinically significant rare or novel variants were found associated with the genetically complex ABO blood group system. For the Rh blood group system, 2 novel and 15 rare variants were found. Our detailed blood group profiling results provide a starting point for the creation of an Australian blood group variant database.

RBCeq: A robust and scalable algorithm for accurate genetic blood typing

Background

While blood transfusion is an essential cornerstone of hematological care, patients requiring repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Despite the promise, user friendly methods to accurately identify blood types from next-generation sequencing data are currently lacking. To address this unmet need, we have developed RBCeq, a novel genetic blood typing algorithm to accurately identify 36 blood group systems.

Methods

RBCeq can predict complex blood groups such as RH, and ABO that require identification of small indels and copy number variants. RBCeq also reports clinically significant, rare, and novel variants with potential clinical relevance that may lead to the identification of novel blood group alleles.

Findings

The RBCeq algorithm demonstrated 99·07% concordance when validated on 402 samples which included 29 antigens with serology and 9 antigens with SNP-array validation in 14 blood group systems and 59 antigens validation on manual predicted phenotype from variant call files. We have also developed a user-friendly web server that generates detailed blood typing reports with advanced visualization (https://www.rbceq.org/).

Interpretation

RBCeq will assist blood banks and immunohematology laboratories by overcoming existing methodological limitations like scalability, reproducibility, and accuracy when genotyping and phenotyping in multi-ethnic populations. This Amazon Web Services (AWS) cloud based platform has the potential to reduce pre-transfusion testing time and to increase sample processing throughput, ultimately improving quality of patient care.

Funding

This work was supported in part by Advance Queensland Research Fellowship, MRFF Genomics Health Futures Mission (76,757), and the Australian Red Cross LifeBlood. The Australian governments fund the Australian Red Cross Lifeblood for the provision of blood, blood products and services to the Australian community.

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.

A novel algorithm comprehensively characterizes human RH genes using whole-genome sequencing data

RHD and RHCE genes encode Rh blood group antigens and exhibit extensive single-nucleotide polymorphisms and chromosome structural changes in patients with sickle cell disease (SCD). RH variation can drive loss of antigen epitopes or expression of new epitopes, predisposing patients with SCD to Rh alloimmunization. Serologic antigen typing is limited to common Rh antigens, necessitating a genetic approach to detect variant antigen expression. We developed a novel algorithm termed RHtyper for RH genotyping from existing whole-genome sequencing (WGS) data. RHtyper determined RH genotypes in an average of 3.4 and 3.3 minutes per sample for RHD and RHCE, respectively. In a validation cohort consisting of 57 patients with SCD, RHtyper achieved 100% accuracy for RHD and 98.2% accuracy for RHCE, when compared with genotypes obtained by RH BeadChip and targeted molecular assays and after verification by Sanger sequencing and independent next-generation sequencing assays. RHtyper was next applied to WGS data from an additional 827 patients with SCD. In the total cohort of 884 patients, RHtyper identified 38 RHD and 28 RHCE distinct alleles, including a novel RHD DAU allele, RHD* 602G, 733C, 744T 1136T. RHtyper provides comprehensive and high-throughput RH genotyping from WGS data, facilitating deconvolution of the extensive RH genetic variation among patients with SCD. We have implemented RHtyper as a cloud-based public access application in DNAnexus (https://platform.dnanexus.com/app/RHtyper), enabling clinicians and researchers to perform RH genotyping with next-generation sequencing data.

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.

Diversity of variant alleles encoding Kidd, Duffy, and Kell antigens in individuals with sickle cell disease using whole genome sequencing data from the NHLBI TOPMed Program

BACKGROUND

Genetic variants in the SLC14A1, ACKR1, and KEL genes, which encode Kidd, Duffy, and Kell red blood cell antigens, respectively, may result in weakened expression of antigens or a null phenotype. These variants are of particular interest to individuals with sickle cell disease (SCD), who frequently undergo chronic transfusion therapy with antigen-matched units. The goal was to describe the diversity and the frequency of variants in SLC14A1, ACKR1, and KEL genes among individuals with SCD using whole genome sequencing (WGS) data.

STUDY DESIGN AND METHODS

Two large SCD cohorts were studied: the Recipient Epidemiology and Donor Evaluation Study III (REDS-III) (n = 2634) and the Outcome Modifying Gene in SCD (OMG) (n = 640). Most of the studied individuals were of mixed origin. WGS was performed as part of the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program.

RESULTS

In SLC14A1, variants included four encoding a weak Jka phenotype and five null alleles (JKnull ). JKA*01N.09 was the most common JKnull . One possible JKnull mutation was novel: c.812G>T. In ACKR1, identified variants included two that predicted Fyx (FY*X) and one corresponding to the c.-67T>C GATA mutation. The c.-67T>C mutation was associated with FY*A (FY*01N.01) in four participants. FY*X was identified in 49 individuals. In KEL, identified variants included three null alleles (KEL*02N.17, KEL*02N.26, and KEL*02N.04) and one allele predicting Kmod phenotype, all in heterozygosity.

CONCLUSIONS

We described the diversity and distribution of SLC14A1, ACKR1, and KEL variants in two large SCD cohorts, comprising mostly individuals of mixed ancestry. This information may be useful for planning the transfusion support of patients with SCD.

Targeted exome sequencing designed for blood group, platelet, and neutrophil antigen investigations: Proof-of-principle study for a customized single-test system

BACKGROUND

Immunohematology reference laboratories provide red blood cell (RBC), platelet (PLT), and neutrophil typing to resolve complex cases, using serology and commercial DNA tests that define clinically important antigens. Broad-range exome sequencing panels that include blood group targets provide accurate blood group antigen predictions beyond those defined by serology and commercial typing systems and identify rare and novel variants. The aim of this study was to design and assess a panel for targeted exome sequencing of RBC, PLT, and neutrophil antigen-associated genes to provide a comprehensive profile in a single test, excluding unrelated gene targets.

STUDY DESIGN AND METHODS

An overlapping probe panel was designed for the coding regions of 64 genes and loci involved in gene expression. Sequencing was performed on 34 RBC and 17 PLT/neutrophil reference samples. Variant call outputs were analyzed using software to predict star allele diplotypes. Results were compared with serology and previous sequence genotyping data.

RESULTS

Average coverage exceeded 250×, with more than 94% of targets at Q30 quality or greater. Increased coverage revealed a variant in the Scianna system that was previously undetected. The software correctly predicted allele diplotypes for 99.5% of RBC blood groups tested and 100% of PLT and HNA antigens excepting HNA-2. Optimal throughput was 12 to 14 samples per run.

CONCLUSION

This single-test system demonstrates high coverage and quality, allowing for the detection of previously overlooked variants and increased sample throughput. This system has the potential to integrate genomic testing across laboratories within hematologic reference settings.

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 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.

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.

Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features

Protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of cells; thus, detecting PPIs is one of the most important issues in current molecular biology. Although much effort has been devoted to using high-throughput techniques to identify protein-protein interactions, the experimental methods are both time-consuming and costly. In addition, they yield high rates of false positive and false negative results. In addition, most of the proposed computational methods are limited in information about protein homology or the interaction marks of the protein partners. In this paper, we report a computational method only using the information from protein sequences. The main improvements come from novel protein sequence representation by combing the continuous and discrete wavelet transforms and from adopting weighted sparse representation-based classifier (WSRC). The proposed method was used to predict PPIs from three different datasets: yeast, human and H. pylori. In addition, we employed the prediction model trained on the PPIs dataset of yeast to predict the PPIs of six datasets of other species. To further evaluate the performance of the prediction model, we compared WSRC with the state-of-the-art support vector machine classifier. When predicting PPIs of yeast, humans and H. pylori dataset, we obtained high average prediction accuracies of 97.38%, 98.92% and 93.93% respectively. In the cross-species experiments, most of the prediction accuracies are over 94%. These promising results show that the proposed method is indeed capable of obtaining higher performance in PPIs detection.

Detailed Characterization of Human Induced Pluripotent Stem Cells Manufactured for Therapeutic Applications

We have recently described manufacturing of human induced pluripotent stem cells (iPSC) master cell banks (MCB) generated by a clinically compliant process using cord blood as a starting material (Baghbaderani et al. in Stem Cell Reports, 5(4), 647-659, 2015). In this manuscript, we describe the detailed characterization of the two iPSC clones generated using this process, including whole genome sequencing (WGS), microarray, and comparative genomic hybridization (aCGH) single nucleotide polymorphism (SNP) analysis. We compare their profiles with a proposed calibration material and with a reporter subclone and lines made by a similar process from different donors. We believe that iPSCs are likely to be used to make multiple clinical products. We further believe that the lines used as input material will be used at different sites and, given their immortal status, will be used for many years or even decades. Therefore, it will be important to develop assays to monitor the state of the cells and their drift in culture. We suggest that a detailed characterization of the initial status of the cells, a comparison with some calibration material and the development of reporter sublcones will help determine which set of tests will be most useful in monitoring the cells and establishing criteria for discarding a line.

Whole-exome sequencing for RH genotyping and alloimmunization risk in children with sickle cell anemia

RH genes are highly polymorphic and encode the most complex of the 35 human blood group systems. This genetic diversity contributes to Rh alloimmunization in patients with sickle cell anemia (SCA) and is not avoided by serologic Rh-matched red cell transfusions. Standard serologic testing does not distinguish variant Rh antigens. Single nucleotide polymorphism (SNP)-based DNA arrays detect many RHD and RHCE variants, but the number of alleles tested is limited. We explored a next-generation sequencing (NGS) approach using whole-exome sequencing (WES) in 27 Rh alloimmunized and 27 matched non-alloimmunized patients with SCA who received chronic red cell transfusions and were enrolled in a multicenter study. We demonstrate that WES provides a comprehensive RH genotype, identifies SNPs not interrogated by DNA array, and accurately determines RHD zygosity. Among this multicenter cohort, we demonstrate an association between an altered RH genotype and Rh alloimmunization: 52% of Rh immunized vs 19% of non-immunized patients expressed variant Rh without co-expression of the conventional protein. Our findings suggest that RH allele variation in patients with SCA is clinically relevant, and NGS technology can offer a comprehensive alternative to targeted SNP-based testing. This is particularly relevant as NGS data becomes more widely available and could provide the means for reducing Rh alloimmunization in children with SCA.

Evaluation of targeted exome sequencing for 28 protein-based blood group systems, including the homologous gene systems, for blood group genotyping

BACKGROUND

Blood group single nucleotide polymorphism genotyping probes for a limited range of polymorphisms. This study investigated whether massively parallel sequencing (also known as next-generation sequencing), with a targeted exome strategy, provides an extended blood group genotype and the extent to which massively parallel sequencing correctly genotypes in homologous gene systems, such as RH and MNS.

STUDY DESIGN AND METHODS

Donor samples (n = 28) that were extensively phenotyped and genotyped using single nucleotide polymorphism typing, were analyzed using the TruSight One Sequencing Panel and MiSeq platform. Genes for 28 protein-based blood group systems, GATA1, and KLF1 were analyzed. Copy number variation analysis was used to characterize complex structural variants in the GYPC and RH systems.

RESULTS

The average sequencing depth per target region was 66.2 ± 39.8. Each sample harbored on average 43 ± 9 variants, of which 10 ± 3 were used for genotyping. For the 28 samples, massively parallel sequencing variant sequences correctly matched expected sequences based on single nucleotide polymorphism genotyping data. Copy number variation analysis defined the Rh C/c alleles and complex RHD hybrids. Hybrid RHD*D-CE-D variants were correctly identified, but copy number variation analysis did not confidently distinguish between D and CE exon deletion versus rearrangement.

CONCLUSION

The targeted exome sequencing strategy employed extended the range of blood group genotypes detected compared with single nucleotide polymorphism typing. This single-test format included detection of complex MNS hybrid cases and, with copy number variation analysis, defined RH hybrid genes along with the RHCE*C allele hitherto difficult to resolve by variant detection. The approach is economical compared with whole-genome sequencing and is suitable for a red blood cell reference laboratory setting.

Crohn disease risk prediction-Best practices and pitfalls with exome data

The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.

ABO allele-level frequency estimation based on population-scale genotyping by next generation sequencing

BACKGROUND

The characterization of the ABO blood group status is vital for blood transfusion and solid organ transplantation. Several methods for the molecular characterization of the ABO gene, which encodes the alleles that give rise to the different ABO blood groups, have been described. However, the application of those methods has so far been restricted to selected samples and not been applied to population-scale analysis.

RESULTS

We describe a cost-effective method for high-throughput genotyping of the ABO system by next generation sequencing. Sample specific barcodes and sequencing adaptors are introduced during PCR, rendering the products suitable for direct sequencing on Illumina MiSeq or HiSeq instruments. Complete sequence coverage of exons 6 and 7 enables molecular discrimination of the ABO subgroups and many alleles. The workflow was applied to ABO genotype more than a million samples. We report the allele group frequencies calculated on a subset of more than 110,000 sampled individuals of German origin. Further we discuss the potential of the workflow for high resolution genotyping taking the observed allele group frequencies into account. Finally, sequence analysis revealed 287 distinct so far not described alleles of which the most abundant one was identified in 174 samples.

CONCLUSIONS

The described workflow delivers high resolution ABO genotyping at low cost enabling population-scale molecular ABO characterization.

Comprehensive red blood cell and platelet antigen prediction from whole genome sequencing: proof of principle

BACKGROUND

There are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next-generation sequencing data challenging, since it uses genomic coordinates.

STUDY DESIGN AND METHODS

The conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation.

RESULTS

Some major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS-based antigen predictions, providing proof of principle for this approach.

CONCLUSION

Detailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data.

Using red blood cell genomics in transfusion medicine

Blood types (blood group antigens) are heritable polymorphic antigenic molecules on the surface of blood cells. These were amongst the first human Mendelian traits identified, and the genetic basis of nearly all of the hundreds of blood types is known. Clinical laboratory methods have proven useful to identify selected blood group gene variants, and use of genetic blood type information is becoming widespread. However, the breadth and complexity of clinically relevant blood group genetic variation poses challenges. With recent advances in next-generation sequencing technologies, a more comprehensive DNA sequence-based genetic blood typing approach is now feasible. This chapter introduces the practitioner to high-resolution genetic blood typing beginning with an overview of the genetics of blood group antigens, the clinical problem of allosensitization, current blood type testing methods, and then discussion of next-generation sequencing and its application to the problem of genetic blood typing.