Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2020 Apr 23;15(4):e0232050. doi: 10.1371/journal.pone.0232050

Performance of a multiplexed amplicon-based next-generation sequencing assay for HLA typing

Chang Liu 1,*, Brian F Duffy 2, Eric T Weimer 3,4, Maureen C Montgomery 4, Jo-Ellen Jennemann 2, Rachel Hill 2, Donna Phelan 2, Lindsay Lay 2, Bijal A Parikh 1
Editor: Baochuan Lin5
PMCID: PMC7179861  PMID: 32324777

Abstract

Background

Next-generation sequencing (NGS) has enabled efficient high-resolution typing of human leukocyte antigen (HLA) genes with minimal ambiguity. Most commercially available assays amplify individual or subgroup of HLA genes by long-range PCR followed by library preparation and sequencing. The AllType assay simplifies the workflow by amplifying 11 transplant-relevant HLA genes in one PCR reaction. Here, we report the performance of this unique workflow evaluated using 218 genetically diverse samples.

Methods

Five whole genes (HLA-A/B/C/DQA1/DPA1) and six near-whole genes (HLA-DRB1/DRB345/DQB1/DPB1; excluding exon 1 and part of intron 1) were amplified in a multiplexed, long-range PCR. Manual library preparation was performed per manufacturer’s protocol, followed by template preparation and chip loading on the Ion Chef, and sequencing on the Ion S5 sequencer. Pre-specified rules for quality control and repeat testing were followed; technologists were blinded to the reference results. The concordance between AllType and reference results was determined at 2-field resolution. We also describe the ranges of input DNA and library concentrations, read number per sample and per locus, and key health metrics in relation to typing results.

Results

The concordance rates were 98.6%, 99.8% and 99.9% at the sample (n = 218), genotype (n = 1688), and allele (n = 3376) levels, respectively. Three genotypes were discordant, all of which shared the same G group typing results with the reference. Most ambiguous genotypes (116 out of 144, 80.6%) were due to the lack of exon 1 and intron 1 coverage for HLA-DRB1/DRB345/DQB1/DPB1 genes. A broad range of input DNA concentrations and library concentrations were tolerated. Per sample read numbers were adequate for accurate genotyping. Per locus read numbers showed some inter-lot variations, and a trend toward improved inter-locus balance was observed with later lots of reagents.

Conclusion

The AllType assay on the Ion Chef/Ion S5 platform offers a robust and efficient workflow for clinical HLA typing at the 2-field resolution. The multiplex PCR strategy simplifies the laboratory procedure without compromising the typing accuracy.

Introduction

Next-generation sequencing (NGS) has been rapidly adopted for HLA typing in clinical laboratories to evaluate donor-recipient histocompatibility. The key advantage of NGS over Sanger sequencing for high-resolution HLA typing is that NGS can phase the large number of sequence variants within HLA genes into haplotypes with minimal cis-trans ambiguity [1–3]. In addition, most current NGS HLA typing assays cover full or near-whole target HLA genes, allowing the detection of clinically relevant variants outside of key exons (exons 2 and 3 of class I genes and exon 2 of class II genes). The high throughput of NGS enables multiplexed sequencing of DNA enriched from multiple genes and many samples in one run, which maximizes the efficiency and lowers the per sample cost.

While sequencing of the whole genome [4], whole exome [5, 6], transcriptome [7, 8], or captured HLA genes can lead to successful HLA typing, most commercially available NGS assays amplify target HLA genes by polymerase chain reaction (PCR) followed by library preparation and sequencing [9–12]. One common approach is the “long-amplicon, short-read” approach. The whole, or near-whole HLA genes are enriched by PCR, and the amplicons are fragmented through enzyme digestion and then ligated to barcodes and sequencing adapters. Short sequencing reads from Illumina and Ion-torrent sequencers are demultiplexed and mapped to reference sequences to generate HLA typing of 2-field, and up to 4-field, resolution. This approach has been shown to achieve highly accurate typing results at a shortened turnaround time [9–12].

For clinical HLA laboratories, workflow efficiency is an important consideration when selecting an NGS-based HLA typing method. We evaluated the performance of the AllType assay (One Lambda, West Hills, CA), which enriches 11 HLA genes (HLA- A/B/C/DRB1/DRB345/DQB1/DQA1/DPB1/DPA1) in one multiplexed PCR reaction followed by library preparation and sequencing on the Ion Chef/Ion S5 platform. All other amplicon-based commercial assays reported in the literature, to our knowledge, amplify individual or subgroups of HLA genes by multiple PCR reactions. It is critical to assess the impact of factors, such as template DNA and library concentrations, inter-locus balance, and allele balance, on the success of this assay. Here, we present the results of our evaluation of the AllType workflow using 218 genetically diverse samples.

Materials and methods

Samples and reference typing results

We sequenced 218 unique samples in this study, including 71 de-identified patient samples (67 EDTA- or citrate-anticoagulated peripheral blood, 2 buccal specimens, and 2 bone marrow biopsy specimens), 96 external proficiency test (PT) specimens from three different vendors, and 51 cell line-derived DNA samples from the Sequence Polymorphism (SP) panel of the 13th International Histocompatibility Working Group (IHWG). The study falls outside the purview of the Human Research Protection Office in our institution, because de-identified specimens were tested, no links will enable the re-identification of subjects, and providers of the specimens were not involved in the design or conduct of the research. High-resolution reference typing results (2-field) were available for HLA-A/B/C/DRB1/DQB1 in all samples, For HLA-DPA1/DPB1/DQA1/DRB345, reference typing results were available in 133, 154, 163, and 153 samples, respectively. Reference typing results for local patient samples were generated by Sanger sequencing supplemented by sequence-specific oligonucleotide (SSO) and sequence-specific primer (SSP) methods. Reference results of PT specimens were consensus results among participating laboratories. Reference results of the SP panel were generated by the 13th IHWG and 17th International HLA and Immunogenetics Workshop. A total of 341 unique HLA alleles were included in this study, representing 204 common or well-documented (CWD) P groups (65.4% of all CWD P groups, version 2.0.0) (Table 1, S1 and S2 Tables) [13]. Homozygous genotypes comprised 15–19% of the genotypes at HLA-A/B/C/DRB1/DQB1 loci. Six null alleles from the current CWD catalog were included in the study, including A*24:11N, C*04:09N, DRB4*01:03:01:02N, DRB4*02:01N, DRB5*01:08N, and DRB5*01:10N. Four non-CWD null alleles, B*39:25N, B*15:26N, A*31:14N, and C*07:55N, were also represented in the study.

Table 1. Number of unique alleles, CWD P groups, non-CWD alleles in this study.

Locus Count of unique alleles CWD P groups Total CWD P groups Percent of all CWD P groups Alleles outside of CWD P groups
A 62 33 57 57.9% 24
B 91 59 102 57.8% 30
C 40 24 38 63.2% 9
DPA1 6 3 3 100.0% 2
DPB1 31 12 14 85.7% 12
DQA1 16 7 7 100.0% 1
DQB1 18 14 17 82.4% 1
DRB1 60 40 62 64.5% 14
DRB345 17 12 12 100% 4
Total 341 204 312 65.4% 97
Open in a new tab

Study design

We followed a pre-specified protocol shown in Fig 1 to ensure objective grading of the results. Samples must meet two health metrics to be graded for concordance: the allele balance must be 0.2 or higher, and the key exon coverage must be 100%. For failed samples, reanalysis was performed with an increased number of reads, if available, or the samples were typed for a second time. The primary endpoint of the study was the concordance between the AllType results and the reference results at the 2-field resolution. Technologists were blinded to the reference results during testing and data analysis. When ambiguity was encountered, we considered the National Marrow Donor Program (NMDP) policy to prioritize genotypes consisting of common or well-documented alleles (CWD). Discordant results were investigated to identify the potential cause. In addition, we determined the lower limit of genomic DNA concentration for the assay, the range of read coverage per sample and per locus, the range of key health metrics, and the ambiguity rate. Finally, we assessed the intra-run and inter-run reproducibility by testing three samples in triplicate and in four independent runs; these runs were performed by two technologists using three different lot of reagents. Different barcodes were used to index these reproducibility samples. We evaluated the concordance of the typing results and the correlation of key health metrics. Discordant samples were typed by TruSight HLA v2 and AlloSeq Assign (CareDx, Brisbane, CA) on the Illumina platform. The MinION device from Oxford Nanopore Technologies (ONT) was used to generate long reads to resolve cis-trans ambiguities in two discordant DPB1 genotypes.

Fig 1. Study design.

Fig 1

Open in a new tab

DNA extraction & quantification

Genomic DNA was extracted from blood specimens using the EZ1 DNA Blood 350 μL Kit (Qiagen, Hilden, Germany) on the EZ1 Advanced XL instrument. The proficiency testing cell lines (EBV-transformed B cells) from the University of California Los Angeles (UCLA) Immunogenetics Center, local buccal samples, and local biopsy specimens were extracted manually using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany).

AllType workflow & HLA typing assignment

The AllType assay (One Lambda, West Hills, CA) amplifies and sequences five whole genes (HLA- A/B/C/DQA1/DPA1) and six near-whole genes (HLA-DRB1/DRB345/DQB1/DPB1; exon 1 and part of intron 1 not included) for HLA typing. Target amplification, library preparation, template preparation and chip loading on the Ion Chef, and sequencing on the Ion S5 instrument were performed following procedures specified by the vendor. Torrent Suite (Version 5.10.1) was used for basecalling, demultiplexing, and managing run-level and barcode-level data. TypeStream Visual (TSV, Version 1.2; Catalog ALL-11LX_001, ALL-11LX_007 and ALL-11LX_013; IMGT Version 3.33.0.0) was used to analyze locus-level data to determine HLA genotypes at 2-field resolution if associated health metrics were met. The default analysis parameters and health metrics thresholds recommended by the vendor were used throughout the study except that the allele balance at any locus must be 0.2 or greater, instead of the default cutoff value of 0.3.

Data analysis

Concordance rates were reported at the sample, genotype, and allele levels. Homozygous genotypes were treated as two identical alleles at the same locus. DRB345 was treated as one genotype with up to two detectable alleles at one or two of these three loci depending on the haplotypes present in any one sample. Run-level quality metrics, including ion sphere particle (ISP) density, total reads, mean, median or mode read length, test fragment 50AQ17 and 100AQ17, were summarized for all ten runs in this study to define the ranges of these parameters. At the locus level, we examined the distributions of health metrics (key exon coverage, allele balance, uniformity, and mismatches in exons) and their impact on typing results.

Results

Concentrations of genomic DNA (gDNA), PCR amplicons, and individual libraries

The gDNA concentrations ranged from 5.6 to 80 ng/μL among all samples, and they were not significantly different between samples with adequate key-exon coverage (n = 207, median concentration: 25 ng/μL, range: 5.6–80 ng/μL) and those with inadequate coverage for one or more loci (n = 11, median: 25 ng/μL, range: 18–32 ng/μL) (Mann Whitney test [14], P = 0.58; Fig 2a).

Fig 2. DNA concentrations throughout the AllType workflow.

Fig 2

Open in a new tab

a) Genomic DNA (gDNA). b) PCR amplicons. c) Individual libraries (at 1:5 dilution). Concentrations for samples with adequate coverage for all loci (n = 207) or insufficient coverage for at least one loci (n = 11) are plotted separately. Black lines are medians and interquartile ranges.

The amplicon concentrations ranged from 2.0 to 43.8 ng/μL among all samples, and they were higher in samples with adequate key-exon coverage (median: 21.2 ng/μL, range: 2–43.8 ng/μL) compared with those with inadequate coverage (median: 13.7 ng/μL, range: 2.2–35.2 ng/μL) (P = 0.04; Fig 2b). However, lower amplicon concentrations could not reliably predict inadequate key-exon coverage. A receiver operating characteristic (ROC) analysis [15] showed an area under the curve of 0.682 (P = 0.042). Using < 5 ng/μL as the cutoff value, the sensitivity and specificity were 45.5% and 96.6%, respectively, for predicting inadequate key-exon coverage.

The library concentrations (at 1:5 dilution) ranged from 0.0076 to 5.76 ng/μL among all samples. Although lower library concentrations were enriched in the group with inadequate coverage, the library concentrations were not significantly different between samples with adequate key-exon coverage and those with inadequate coverage (P = 0.13; Fig 2c).

We also performed a sensitivity analysis on the effect of input gDNA concentrations. Seven gDNA specimens from a serial 1:2 dilution of one sample were sequenced in one run. The concentrations ranged from 1.5 to 100 ng/μL, which was broader than the concentrations of the 218 regular samples. The PCR amplicon concentrations ranged from 1.8 to 22.4 ng/μL and correlated well with the input gDNA concentrations (Spearman R = 0.976, P<0.001; Fig 3a). The library concentrations (at 1:5 dilution) ranged from 0.48 to 1.61 ng/μL and correlated well with the amplicon concentrations (R = 0.762, P<0.05; Fig 3b). Concordant typing results were obtained at all input gDNA concentrations, and the negative control (water) was negative.

Fig 3. Concentrations of genomic DNA (gDNA), amplicons, and libraries in the titration study.

Fig 3

Open in a new tab

a) Correlation between the gDNA concentrations and amplicon concentrations. b) Correlation between the amplicon concentrations and library concentrations.

Sequencing runs and run-level metrics

Two technologists performed ten sequencing runs with 16–48 samples per run. The run-level quality metrics are summarized in Fig 4. We observed a narrow distribution of 50AQ17 (median: 85%, interquartile range or IQR: 82.7–86.3%) and 100AQ17 (82%, 79.8–83%), defined as the percentages of the test fragment (TF) sequencing reads with an error rate of 1 in 50 and a minimum length of 50 and 100 bases, respectively. The median percent usable reads was 50.5% (IQR: 47–52.5%), which was dependent on the percent chip loading (88%, 86.5–89%), percent enrichment (99%, 99–99.3%), percent clonal ion-sphere particles (62%, 60.5–63.3%), and final library (82.5%, 77–83.3%) (Fig 4a and 4b). The median read length observed in this study was 261 bases, and the coefficients of variations (CVs) for the mean, median, and mode of read lengths across all runs were 13.6%, 15%, and 21.6%, respectively (Fig 4c). The shorter median read lengths in three runs (approximately 200 bases) were not associated with any operator (Fig 4d). Importantly, there was no correlation between shorter read lengths and increased typing errors or ambiguity.

Fig 4. Distribution of run-level metrics from ten sequencing runs.

Fig 4

Open in a new tab

a) Metrics for sequencing accuracy of test fragments (%50AQ17 and %100AQ17) and the percentage of reads usable for analysis. The latter is dependent on the percentages of ion-sphere particle (ISP) loading, template-positive ISP (enrichment), clonal ISP, and final library. b) Total reads usable for analysis. c) The mean, median, and mode of read lengths from 10 runs. d) The median read lengths stratified by technologists. Lines are medians and interquartile ranges.

Number of reads per sample and per locus

The number of reads per sample, after demultiplexing, showed a normal distribution in three representative runs (Fig 5a–5c), all including 32 samples per run but each using a different lot of AllType PCR primers. The mean reads number per sample ranged from 461,301 to 538,531 among these runs. The TSV software, by default, analyze up to 300,000 reads per sample for HLA typing. All but one sample in these three runs surpassed this threshold. The outlier in Run09 had 40,000 reads and was successfully typed with all health metrics met.

Fig 5. Distribution of the number of reads per sample and per locus.

Fig 5

Open in a new tab

The histogram of read count per sample was plotted for three independent runs performed with three different lots of AllType reagents: a) Run01, Lot001. b) Run08, Lot007. c) Run09, Lot013. The number of reads per locus was also summarized for these runs: d) Run01, Lot001. e) Run08, Lot007. f) Run09, Lot013. Each run included 32 barcoded samples. Median (solid line) and interquartile (dotted line) are shown in the violin plots (d-f).

We observed the following inter-locus and inter-lot difference in the number of reads mapped to each HLA locus (Fig 5d–5f). First, the number of reads per locus was higher for certain class II loci, i.e., DPB1, DQB1, DRB1, and DRB345, than class I loci in all three representative runs. This difference corresponds to the longer amplicons for these class II loci than those for class I loci. Second, the read number per locus was unbalanced among class I loci (A, B, and C) with the Lot 001 reagent (Fig 5d), while the inter-locus balance was improved with the Lot 007 and 013 reagents (Fig 5e and 5f). The imbalance among DPB1, DQB1, DRB1, and DRB345 was relatively modest. Third, we also normalized the number of reads per locus as a percentage of the total reads per sample and compared the values across different representative runs (Fig 6). Significant inter-lot differences were detected for A, B, C, DPA1, DQA1, and DQB1, which likely reflect reagent updates by the manufacturer to achieve more balanced coverage for these loci.

Fig 6. Inter-lot comparison of the percentages of total reads mapped to each locus.

Fig 6

Open in a new tab

Each lot is represented by one run with 32 barcoded samples. Boxes represent the median and interquartile range (IQR); whiskers represent 1.5 IQR. ** P<0.01, **** P<0.0001, by ordinary one-way ANOVA.

Health metrics: Key-exon coverage, allele balance, and repeat testing

The minimum key-exon coverage was above 100 reads for most samples across all loci (Fig 7a and 7b), suggesting that excellent coverage can be achieved with 16–48 samples per run. For DPA1, DQB1, DRB1, and DRB345, the allele balance deviated from a normal distribution with a large number of samples affected by significant allele imbalance (Fig 7c).

Fig 7. Distributions of minimum coverage depths in key exons and allele balance.

Fig 7

Open in a new tab

a) Minimum coverage depth for exon 2 of all loci. b) Minimum coverage depth for exon 3 of class I loci. c) Allele balance. Values from 218 samples are plotted. For samples that underwent repeat testing, the values from the repeat testing (not the initial run) were included in the plots. Line and dotted lines in the violin plots represent median and interquartile range (IQR), respectively.

Five samples had inadequate key-exon coverage, defined as less than 20 valid reads at any key-exon position for an allele, which was resolved upon reanalysis with additional reads (up to 600,000 per locus if available). Ten samples were re-typed due to inadequate coverage and unsuccessful reanalysis, giving a repeat testing rate of 4.6%. Seven of the ten samples passed all health metrics upon repeat testing. Three of the ten samples did not satisfy the key exon coverage for DQB1 in one sample and for DPB1 in all three samples. All three samples were PT samples stored at 4°C for over two years. Despite satisfactory gDNA concentrations (18–23 ng/μL), partial degradation of the specimens was observed on electrophoresis. Among all target loci, DRB1 (7 samples) was the most commonly affected by inadequate key-exon coverage. Only one sample had allele imbalance for DPB1 at the 4-field resolution, which did not affect reporting at the 2-field resolution, and the sample was not re-typed.

Health metrics: Mismatches in exons

A total of 46 mismatches in exons were observed in this study, where the consensus bases from the sequencing reads differed from the reference alleles called by TSV (S3 Table). These mismatches were in non-key exons only, and most were found at the DRB5 locus (67.4%), followed by DPA1 (17.4%), C (8.7%), DQA1 (4.4%), and DQB1 (2.2%). The closest 2-field HLA alleles were assigned as the final typing for comparison with the reference typing results.

Thirty two of the 46 exon mismatches (69.6%) were likely sequencing artifact (S3 Table). The G>T variant at exon 6 position 12703 (gDNA alignment position, E6-12703) of DRB5*01:01:01 was the most common mismatch in exons (n = 21) observed in our study, followed by the same mismatch with DRB5*01:02 (n = 4) and DRB5*02:02:01 (n = 4). These mismatches occurred primarily with Lot 001 and 007 reagents, but not Lot 013, and their interpretation was confounded by high background at the position suggestive of aberrant read mapping. Three mismatches occurred in homopolymer regions in three samples, and the supporting reads for these mismatches were all in one direction rather than bidirectional.

Fourteen of the 46 exon mismatches were supported by the sequencing reads (S3 Table). Except for two synonymous mutations, 12 mismatches were missense mutations that were predicted to cause substitutions in the protein sequence outside of the antigen recognition domains.

Concordance with reference typing and investigation of discordant results

The AllType typing results were concordant with all evaluable genotypes in 215 of the 218 samples, with a sample-level concordance rate of 98.6%. The overall concordance rates at the genotype and allele levels were 99.8% and 99.9%, respectively. Detailed results for each HLA locus are reported in Table 2. The three discordant genotypes and our investigation are described in Table 3. The discordant DRB1 genotype involved alleles in the same G group and could be explained by a single-base variant in exon 4 that was missed by TSV. The G group genotype, homozygous DRB1*09:01:02G, was in agreement between AllType and the reference. The two discordant DPB1 genotypes also share the same G group typing, DPB1*04:01:01G and DPB1*04:02:01G, and the discrepancies were due to cis-trans ambiguity. Both samples were PT samples from 2016 and 2017, and the reference typing results appeared to have considered the key exon (exon 2) only.

Table 2. Concordance at the genotype and allele levels.

Locus Genotype-level evaluation Allele-level evaluation
Evaluable genotypes* Concordance Concordance rate Evaluable alleles* Concordance Concordance rate
A 218 218 100.0% 436 436 100.0%
B 218 218 100.0% 436 436 100.0%
C 218 218 100.0% 436 436 100.0%
DPA1 133 133 100.0% 266 266 100.0%
DPB1 151 149 98.7% 302 298 98.7%
DQA1 163 163 100.0% 326 326 100.0%
DQB1 217 217 100.0% 434 434 100.0%
DRB1 217 216 99.5% 434 433 99.8%
DRB345 153 153 100.0% 306 306 100.0%
All loci 1688 1685 99.8% 3376 3371 99.9%
Open in a new tab

* Loci with unsatisfactory health metrics were excluded, including DQB1 in one sample, DPB1 in three samples, and DRB1 in one sample.

Table 3. Discordant results and further investigation.

Run-ID Barcode Locus Reference typing AllType result Investigation
3 55, 62 DPB1 DPB1*04:01 DPB1*04:02 DPB1*105:01 DPB1*126:01 OR DPB1*665:01 DPB1*126:01 All health metrics were met. The two genotypes by AllType were due to a sequence variant in exon 1 that was not covered by the assay. The reference and AllType genotypes indicated a cis-trans ambiguity across exons 2 and 3. The ambiguity was not resolved by alternative NGS methods on the Illumina platform. Long reads spanning exons 2 and 3 generated by nanopore sequencing were consistent with the reference typing.
6 40 DRB1 DRB1*09:01 DRB1*09:21 DRB1*09:21 DRB1*09:21 OR DRB1*09:21 DRB1*09:31 OR DRB1*09:31 DRB1*09:31 All health metrics were met. DRB1*09:01:02:01 has one base difference from DRB1*09:21 in exon 4 (E4-8874). DRB1*09:31 has a reference sequence that covers exons 1–3 only; it has one base difference from DRB1*09:01:02:01 and DRB1*09:21 in exon 1. DRB1*09:01 was not included in the genotype by AllType because the supportive reads were considered as high background rather than true signal. This seemed to be an idiosyncratic event unique to this sample, which was typed correctly by a newer version of TSV.
Open in a new tab

We typed the discordant samples by TruSight HLA [10] and AlloSeq Assign (CareDx, Brisbane, CA) assays on the Illumina platform. The DRB1 typing by both Illumina-based methods agreed with the reference. Neither Illumina-based method resolved the cis-trans ambiguity of the two DPB1 genotypes. Nanopore sequencing generated long reads capable of phasing the distant variants, which supported the genotype of DPB1*04:01 and DPB1*04:02.

Ambiguity

Ambiguity was encountered in 112 samples for 144 genotypes, and three categories were described as follows. First, 116 ambiguous genotypes (80.6%) involved DPB1, DQB1, DRB1, and DRB345 genes and were explained by the lack of exon 1 and intron 1 coverage. Second, 26 ambiguous genotypes (18.1%) were due to cis-trans ambiguity across different exons. All alternative genotypes included one or two rare alleles, which did not need to be resolved per current NMDP policy and could be reported using existing NMDP codes. Third, ambiguous genotypes due to intronic variant were frequently identical at the 2-field resolution except in two cases (1.4%), where B*27:05:02:04Q was listed as an alternative allele. B*27:05:02:04Q has an A at the 3’ end of intron 2, substituting a commonly observed G; the variant is predicted to disrupt splicing and result in an altered protein sequence. Upon inspection of the reads mapped to the region, B*27:05:02:04Q was ruled out in both cases due to the lack of read support.

Intra-run and inter-run reproducibility

Three samples for reproducibility evaluation were tested in triplicate in four runs, a total of 12 repeats for each sample. All genotypes were concordant among all repeats with the following two exceptions. One repeat of sample #1 did not meet the minimum reads requirement and was excluded from analysis. Sample #3 had allele imbalance for DRB1 in four out of 12 repeats, which was also excluded from analysis.

To assess the reproducibility of key health metrics, we first examined the pairwise correlation of mean exon 2 coverage across all loci between repeats of each sample. The correlation of 34 pairs of intra-run repeats showed Pearson coefficients ranging from 0.7 to 1. The inter-run correlations were lower than intra-run correlations with 17 out of 153 Pearson coefficients below 0.4, an arbitrary threshold for acceptable correlation. Exclusion of DQB1 from this analysis improved all coefficients to 0.4 or higher. We also examined the pairwise correlation of allele balance across all loci between repeats of each sample. The correlation of intra-run repeats showed coefficients ranging from 0.4 to 1. The inter-run correlations were, again, lower than intra-run correlations with 32 out of 153 Pearson coefficients below 0.4. Exclusion of DRB1 from this analysis improved all coefficients to 0.4 or higher.

Conclusion & discussion

This study established an excellent accuracy and ambiguity profile for the AllType workflow in 2-field HLA typing. We included genetically diverse samples of multiple specimen types that are routinely tested in a hospital-based HLA laboratory. We also implemented a pre-specified policy for repeat testing and concordance grading to ensure an objective evaluation. The assay tolerated a broad range of input DNA concentrations and individual library concentrations with a low repeat testing rate. The number of reads per sample and locus were adequate for the application. With the multiplexed PCR design, we observed some inter-lot variations in the per-locus reads number, as well as a trend towards a more balanced inter-locus coverage with later lots of reagents. We also observed increased exon mismatches with earlier lots but not a later lot of reagent (S3 Table), which underscored the need to characterize these mismatches during data analysis and to monitor their occurrence for quality assurance purposes. With satisfactory key exon coverage and allele balance, 99.8% of the AllType genotypes were concordant with the reference results. For the three discordant genotypes, the AllType results were clinically acceptable in all three cases, where the G group typing results agreed with the reference.

Our findings confirm the recent report by Cargou and colleagues on the performance of the AllType workflow [11]. We also present additional information on the accuracy of 2-field typing for DQA1 and DPA1, which may be beneficial for solid-organ donor typing [16]. Moreover, we describe the distributions of key health metrics, gDNA and library concentrations, and reads number per sample and per locus. These data are valuable for quality monitoring, decision-making on repeat typing, and troubleshooting in times of testing failure. The minimum guidelines on the validation of NGS HLA typing by the American Society for Histocompatibility and Immunogenetics (ASHI) also emphasizes the evaluation of health metrics associated with NGS workflows in clinical HLA laboratories. Related to the report by Cargou and our study, Barone and colleagues reported the satisfactory performance of a similar workflow on the Ion-torrent platform, NXType [12], which features two separate PCR reactions for class I and II HLA genes, respectively. De Santis’s group also reported a highly accurate methods based on the Ion-torrent sequencer, and four PCR reactions were used to amplify a total of 11 HLA genes [17].

The “long-amplicon, short-reads” approach to NGS-based HLA typing has also been implemented successfully on the Illumina platform. Several commercially available assays have achieved comparable accuracy and efficiency based on well-designed validation studies [9, 10, 18–20]. The general workflows of these assays are similar to AllType, except for some differences in the PCR strategy, barcoding method, and sequencing chemistry. Typical for these assays, target HLA genes are amplified separately and then indexed with barcodes for each locus or each sample. For example, the Omixon assay analyzes up to 40,000 reads for locus-indexed libraries and 200,000 reads for sample-indexed libraries [9]. With the Illumina sequencing chemistry, the sequencing reads have a lower error rate than reads from the Ion-torrent sequencer; the paired-end reads with longer library fragments may theoretically enable the phasing of more distant variants. Nevertheless, Ion-torrent S5 has a shorter sequencing time of approximately 7 hours; the higher error rate and its typical read length of 200–300 bases appear to be inconsequential for the AllType application.

Our study has a few limitations. First, this is a single-center, single-platform evaluation with a modest sample size. While we provided in-depth information on the AllType assay, other multi-center, multi-platform comparison studies are invaluable in contrasting the performance of different NGS assays relative to each other [21, 22]. Second, our study did not evaluate HLA typing beyond the 2-field resolution. Two-field HLA typing remains the gold standard for recipient-donor matching in allogeneic hematopoietic stem cell transplantation [23]. Few clinical laboratories are currently reporting 3-field or 4-field HLA typing, although a potential role for this ultra-high resolution (UHR) typing is emerging in several recent studies [24, 25]. One potential barrier for the validation of UHR typing is the availability of clinical samples with known 3- and 4-field typing; some reference cell lines typed recently at UHR [21, 26] may be an acceptable alternative. Third, we handled exon mismatches by in-depth review of read mapping data and did not attempt to verify them by a different method. No mismatch occurred in key exons during our study; the closest alleles in the HLA/IMGT database [27] were assigned and used for concordance grading. We took this pragmatic approach to mimic the intended routine practice in our laboratory. Our current standard operating procedure also requires that mismatches in key exons that change the protein sequence must be verified by an orthogonal method.

The future landscape of NGS-based HLA typing will be defined by continued innovation and clinical investigation. Additional technologies such as the single molecule real-time (SMRT) sequencing on the Pacific Biosciences’ platform has been effectively employed for HLA typing [26, 28, 29]. The technology enables the “long-amplicon, long-reads” approach, which allows the long-range amplicons to be sequenced as a whole without fragmentation. This feature simplifies the library preparation and enhances the capability of phasing distant variants. Oxford Nanopore Technologies may offer another option for implementing the “long-amplicon, long-reads” approach for HLA typing [30–32], although better bioinformatics tools are needed to overcome the high error rate of the nanopore sequencing reads. On the other end of the spectrum, Lange and colleagues took the “short-amplicon, short-reads” approach and developed a workflow to sequence selected exons only on the Illumina platform. With the bypass of conventional library preparation and a high degree of automation, this method maximized the efficiency for donor registry HLA typing [33, 34]. Together with AllType and other Illumina-based assays discussed above, HLA laboratories will have many good options to meet their diverse clinical needs.

Supporting information

S1 Table. Genotypes of 218 unique samples.

(XLSX)

Click here for additional data file. (28.3KB, xlsx)
S2 Table. Alleles not in the CWD P group list.

(XLSX)

Click here for additional data file. (12.5KB, xlsx)
S3 Table. Mismatches in exons.

(XLSX)

Click here for additional data file. (13.4KB, xlsx)

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This study was supported by NIH NIAID award number R41 AI142919-01 (C.L.). There was no additional external funding received for this study.

References

  • 1.Hosomichi K, Shiina T, Tajima A, Inoue I. The impact of next-generation sequencing technologies on HLA research. J Hum Genet. 2015;60(11):665–73. Epub 2015/08/28. 10.1038/jhg.2015.102 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Erlich HA. HLA typing using next generation sequencing: An overview. Hum Immunol. 2015;76(12):887–90. Epub 2015/03/18. 10.1016/j.humimm.2015.03.001 . [DOI] [PubMed] [Google Scholar]
  • 3.Erlich H. HLA DNA typing: past, present, and future. Tissue Antigens. 2012;80(1):1–11. Epub 2012/06/02. 10.1111/j.1399-0039.2012.01881.x . [DOI] [PubMed] [Google Scholar]
  • 4.Nariai N, Kojima K, Saito S, Mimori T, Sato Y, Kawai Y, et al. HLA-VBSeq: accurate HLA typing at full resolution from whole-genome sequencing data. BMC Genomics. 2015;16 Suppl 2:S7 Epub 2015/02/25. 10.1186/1471-2164-16-s2-s7 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Liu C, Yang X, Duffy B, Mohanakumar T, Mitra RD, Zody MC, et al. ATHLATES: accurate typing of human leukocyte antigen through exome sequencing. Nucleic Acids Res. 2013;41(14):e142 Epub 2013/06/12. 10.1093/nar/gkt481 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Bai Y, Ni M, Cooper B, Wei Y, Fury W. Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads. BMC Genomics. 2014;15:325 Epub 2014/06/03. 10.1186/1471-2164-15-325 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Boegel S, Scholtalbers J, Lower M, Sahin U, Castle JC. In Silico HLA Typing Using Standard RNA-Seq Sequence Reads. Methods Mol Biol. 2015;1310:247–58. Epub 2015/05/31. 10.1007/978-1-4939-2690-9_20 . [DOI] [PubMed] [Google Scholar]
  • 8.Boegel S, Lower M, Schafer M, Bukur T, de Graaf J, Boisguerin V, et al. HLA typing from RNA-Seq sequence reads. Genome Med. 2012;4(12):102 Epub 2012/12/25. 10.1186/gm403 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gandhi MJ, Ferriola D, Lind C, Duke JL, Huynh A, Papazoglou A, et al. Assessing a single targeted next generation sequencing for human leukocyte antigen typing protocol for interoperability, as performed by users with variable experience. Hum Immunol. 2017;78(10):642–8. Epub 2017/07/25. 10.1016/j.humimm.2017.07.012 . [DOI] [PubMed] [Google Scholar]
  • 10.Weimer ET, Montgomery M, Petraroia R, Crawford J, Schmitz JL. Performance Characteristics and Validation of Next-Generation Sequencing for Human Leucocyte Antigen Typing. J Mol Diagn. 2016;18(5):668–75. Epub 2016/07/05. 10.1016/j.jmoldx.2016.03.009 . [DOI] [PubMed] [Google Scholar]
  • 11.Cargou M, Ralazamahaleo M, Blouin L, Top I, Elsermans V, Andreani M, et al. Evaluation of the AllType kit for HLA typing using the Ion Torrent S5 XL platform. Hla. 2019. Epub 2019/10/03. 10.1111/tan.13708 . [DOI] [PubMed] [Google Scholar]
  • 12.Barone JC, Saito K, Beutner K, Campo M, Dong W, Goswami CP, et al. HLA-genotyping of clinical specimens using Ion Torrent-based NGS. Hum Immunol. 2015;76(12):903–9. 10.1016/j.humimm.2015.09.014 . [DOI] [PubMed] [Google Scholar]
  • 13.Mack SJ, Cano P, Hollenbach JA, He J, Hurley CK, Middleton D, et al. Common and well-documented HLA alleles: 2012 update to the CWD catalogue. Tissue Antigens. 2013;81(4):194–203. Epub 2013/03/21. 10.1111/tan.12093 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hart A. Mann-Whitney test is not just a test of medians: differences in spread can be important. Bmj. 2001;323(7309):391–3. Epub 2001/08/18. 10.1136/bmj.323.7309.391 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Obuchowski NA, Bullen JA . Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys Med Biol. 2018;63(7):07tr1. Epub 2018/03/08. 10.1088/1361-6560/aab4b1 . [DOI] [PubMed] [Google Scholar]
  • 16.Huang Y, Dinh A, Heron S, Gasiewski A, Kneib C, Mehler H, et al. Assessing the utilization of high-resolution 2-field HLA typing in solid organ transplantation. Am J Transplant. 2019;19(7):1955–63. Epub 2019/01/10. 10.1111/ajt.15258 . [DOI] [PubMed] [Google Scholar]
  • 17.Truong L, Matern B, D'Orsogna L, Martinez P, Tilanus MGJ, De Santis D. A Novel Multiplexed 11 Locus Hla Full Gene Amplification Assay Using Next Generation Sequencing. Hla. 2019. Epub 2019/10/17. 10.1111/tan.13729 . [DOI] [PubMed] [Google Scholar]
  • 18.Allen ES, Yang B, Garrett J, Ball ED, Maiers M, Morris GP. Improved accuracy of clinical HLA genotyping by next-generation DNA sequencing affects unrelated donor search results for hematopoietic stem cell transplantation. Hum Immunol. 2018;79(12):848–54. Epub 2018/10/15. 10.1016/j.humimm.2018.10.008 . [DOI] [PubMed] [Google Scholar]
  • 19.Seshasubramanian V, Venugopal M, A DSK, Naganathan C, Manisekar NK, Kumar YN, et al. Application of high-throughput next-generation sequencing for HLA typing of DNA extracted from postprocessing cord blood units. Hla. 2019;94(2):141–6. Epub 2019/05/06. 10.1111/tan.13565 . [DOI] [PubMed] [Google Scholar]
  • 20.Yin Y, Lan JH, Nguyen D, Valenzuela N, Takemura P, Bolon YT, et al. Application of High-Throughput Next-Generation Sequencing for HLA Typing on Buccal Extracted DNA: Results from over 10,000 Donor Recruitment Samples. PLoS One. 2016;11(10):e0165810 Epub 2016/11/01. 10.1371/journal.pone.0165810 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Creary LE, Guerra SG, Chong W, Brown CJ, Turner TR, Robinson J, et al. Next-generation HLA typing of 382 International Histocompatibility Working Group reference B-lymphoblastoid cell lines: Report from the 17th International HLA and Immunogenetics Workshop. Hum Immunol. 2019;80(7):449–60. Epub 2019/03/08. 10.1016/j.humimm.2019.03.001 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Osoegawa K, Vayntrub TA, Wenda S, De Santis D, Barsakis K, Ivanova M, et al. Quality control project of NGS HLA genotyping for the 17th International HLA and Immunogenetics Workshop. Hum Immunol. 2019;80(4):228–36. Epub 2019/02/10. 10.1016/j.humimm.2019.01.009 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Dehn J, Spellman S, Hurley CK, Shaw BE, Barker JN, Burns LJ, et al. Selection of Unrelated Donors and Cord Blood Units for Hematopoietic Cell Transplantation: Guidelines from NMDP/CIBMTR. Blood. 2019. Epub 2019/07/12. 10.1182/blood.2019001212 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Mayor NP, Hayhurst JD, Turner TR, Szydlo RM, Shaw BE, Bultitude WP, et al. Better HLA Matching as Revealed Only by Next Generation Sequencing Technology Results in Superior Overall Survival Post-Allogeneic Haematopoietic Cell Transplantation with Unrelated Donors. Biology of Blood and Marrow Transplantation. 2018;24(3):S46. [Google Scholar]
  • 25.Vazirabad I, Chhabra S, Nytes J, Mehra V, Narra RK, Szabo A, et al. Direct HLA Genetic Comparisons Identify Highly Matched Unrelated Donor-Recipient Pairs with Improved Transplantation Outcome. Biol Blood Marrow Transplant. 2019;25(5):921–31. Epub 2018/12/12. 10.1016/j.bbmt.2018.12.006 . [DOI] [PubMed] [Google Scholar]
  • 26.Turner TR, Hayhurst JD, Hayward DR, Bultitude WP, Barker DJ, Robinson J, et al. Single molecule real-time DNA sequencing of HLA genes at ultra-high resolution from 126 International HLA and Immunogenetics Workshop cell lines. Hla. 2018;91(2):88–101. Epub 2017/11/25. 10.1111/tan.13184 . [DOI] [PubMed] [Google Scholar]
  • 27.Robinson J, Halliwell JA, Hayhurst JD, Flicek P, Parham P, Marsh SG. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 2015;43(Database issue):D423–31. Epub 2014/11/22. 10.1093/nar/gku1161 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Mayor NP, Robinson J, McWhinnie AJ, Ranade S, Eng K, Midwinter W, et al. HLA Typing for the Next Generation. PLoS One. 2015;10(5):e0127153 10.1371/journal.pone.0127153 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ambardar S, Gowda M. High-Resolution Full-Length HLA Typing Method Using Third Generation (Pac-Bio SMRT) Sequencing Technology. Methods Mol Biol. 2018;1802:135–53. Epub 2018/06/03. 10.1007/978-1-4939-8546-3_9 . [DOI] [PubMed] [Google Scholar]
  • 30.Liu C, Xiao F, Hoisington-Lopez J, Lang K, Quenzel P, Duffy B, et al. Accurate Typing of Human Leukocyte Antigen Class I Genes by Oxford Nanopore Sequencing. J Mol Diagn. 2018;20(4):428–35. Epub 2018/04/07. 10.1016/j.jmoldx.2018.02.006 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Lang K, Surendranath V, Quenzel P, Schofl G, Schmidt AH, Lange V. Full-Length HLA Class I Genotyping with the MinION Nanopore Sequencer. Methods Mol Biol. 2018;1802:155–62. Epub 2018/06/03. 10.1007/978-1-4939-8546-3_10 . [DOI] [PubMed] [Google Scholar]
  • 32.Ton KNT, Cree SL, Gronert-Sum SJ, Merriman TR, Stamp LK, Kennedy MA. Multiplexed Nanopore Sequencing of HLA-B Locus in Maori and Pacific Island Samples. Front Genet. 2018;9:152 Epub 2018/05/16. 10.3389/fgene.2018.00152 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Schofl G, Lang K, Quenzel P, Bohme I, Sauter J, Hofmann JA, et al. 2.7 million samples genotyped for HLA by next generation sequencing: lessons learned. BMC Genomics. 2017;18(1):161 Epub 2017/02/16. 10.1186/s12864-017-3575-z . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Lange V, Bohme I, Hofmann J, Lang K, Sauter J, Schone B, et al. Cost-efficient high-throughput HLA typing by MiSeq amplicon sequencing. BMC Genomics. 2014;15:63 Epub 2014/01/28. 10.1186/1471-2164-15-63 . [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Genotypes of 218 unique samples.

(XLSX)

Click here for additional data file. (28.3KB, xlsx)
S2 Table. Alleles not in the CWD P group list.

(XLSX)

Click here for additional data file. (12.5KB, xlsx)
S3 Table. Mismatches in exons.

(XLSX)

Click here for additional data file. (13.4KB, xlsx)

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES