High-throughput, high-fidelity HLA genotyping
with deep sequencing
Chunlin Wanga,1, Sujatha Krishnakumara,1, Julie Wilhelmya, Farbod Babrzadeha, Lilit Stepanyana, Laura F. Sub,
Douglas Levinsonc, Marcelo A. Fernandez-Viñad, Ronald W. Davisa,2, Mark M. Davise,f,2, and Michael Mindrinosa,2
aStanford Genome Technology Center,eHoward Hughes Medical Institute,fDepartment of Microbiology and Immunology,bDivision of Immunology and
Rheumatology, Department of Medicine,cDepartment of Psychiatry, anddDepartment of Pathology, Stanford University, Palo Alto, CA 94003
Contributed by Mark M. Davis, April 23, 2012 (sent for review March 9, 2012)
Human leukocyte antigen (HLA) genes are the most polymorphic
in the human genome. They play a pivotal role in the immune
response and have been implicated in numerous human patholo-
gies, especially autoimmunity and infectious diseases. Despite their
importance, however, they are rarely characterized comprehen-
sively because of the prohibitive cost of standard technologies and
the technical challenges of accurately discriminating between these
highly related genes and their many allelles. Here we demonstrate
a high-resolution, and cost-effective methodology to type HLA
genes by sequencing, which combines the advantage of long-range
amplification, the power of high-throughput sequencing platforms,
and a unique genotyping algorithm. We calibrated our method for
HLA-A, -B, -C, and -DRB1 genes with both reference cell lines and
clinical samples and identified several previously undescribed
alleles with mismatches, insertions, and deletions. We have further
demonstrated the utility of this method in a clinical setting by
typing five clinical samples in an Illumina MiSeq instrument with
a 5-d turnaround. Overall, this technology has the capacity to de-
liver low-cost, high-throughput, and accurate HLA typing by multi-
plexing thousands of samples in a single sequencing run, which will
enable comprehensive disease-association studies with large
cohorts. Furthermore, this approach can also be extended to in-
clude other polymorphic genes.
hematopoietic stem cell transplantation|sequence-based typing
T lymphocytes. This helps to initiate the adaptive immune re-
sponse in higher vertebrates and thus is critical to the detection
and identification of invading microorganisms (1). Six of the
HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, and -DRB1) are
extremely polymorphic and constitute the most important set
of markers for matching patients and donors for bone marrow
transplantation (2, 3). Specific HLA alleles have been found
to be associated with a number of autoimmune diseases, such as
multiple sclerosis (4), narcolepsy (5), celiac disease (6), rheu-
matoid arthritis (7), and type I diabetes (3, 8). Alleles have also
been noted to be protective in infectious diseases such as HIV (9,
10), and numerous animal studies have shown that these genes
are often the major contributors to disease susceptibility or re-
HLA genes are among the most polymorphic in the human
genome, and the changes in sequence affect the specificity of
antigen presentation and histocompatibility in transplantation. A
variety of methodologies have been developed for HLA typing at
the protein and nucleic acid level. Whereas earlier HLA typing
methods distinguished HLA antigens, modern methods such as
sequence-based typing (SBT) determine the nucleotide sequen-
ces of HLA genes for higher resolution. However, due to cost
and time constraints, HLA sequencing technologies have tradi-
tionally focused on the most polymorphic regions encoding the
peptide-binding groove that binds to HLA antigens, i.e., exons 2
and 3 for class I genes and exon 2 for class II genes. The antigen-
binding groove region of HLA molecules is the focus point of T-
uman leukocyte antigen (HLA) genes encode cell-surface
proteins that bind and display fragments of antigens to
cell receptor and mediates transplant rejection and graft-versus-
host diseases (GVHD). Regions other than the antigen-binding
groove need to be typed because some of those polymorphic sites
might affect or abrogate HLA protein expression such as the null
allele HLA-A*02:53N with a single-base insertion in exon 4.
Although the polymorphic regions of HLA genes predominantly
cluster within these exons, an increasing number of alleles dis-
play polymorphisms in other exons and introns as well. There-
fore, typing ambiguities can result from two or more alleles
sharing identical sequences in the targeted exons, but differing in
the exons that are not sequenced. Resolving these ambiguities is
costly and labor intensive, which makes current SBT methods
unsuitable for studies involving even a moderately large group
Here we demonstrate a unique method targeting a contiguous
segment of each of four polymorphic HLA genes (HLA-A, -B,
-C, and -DRB1), which define the minimal requirements for
HLA matching for allogeneic hematopoietic stem cell trans-
plantation (HSCT) (14). Each HLA gene is amplified from
genomic DNA in a single long-range PCR spanning the majority
of the coding regions and covering most known polymorphic
sites. This approach has several advantages. First, more poly-
morphic sites are sequenced to provide genotyping information
of higher definition and the physical linkage between exons can
be determined to resolve combination ambiguity. Second, long-
range PCR primers can be placed in less polymorphic regions,
allowing for improved resolution of genetic differences. Third,
exons of the same gene can be amplified in one fragment,
thereby decreasing coverage variability. We calibrated this
typing method on HLA-A, -B, -C, and -DRB1 genes using 40
reference cell-line samples in the sequence polymorphism ref-
erence panel provided by the International Histocompatibility
Working Group (IHWG, www.ihwg.org) The overall concor-
dance rate of 99% with previous results and verification of our
HLA typing results in the three discordant alleles by an in-
dependent sequencing technology demonstrate that this low-cost,
high-throughput HLA typing protocol provides a high level of
reliability. In addition, we tested our method on 59 clinical sam-
ples and found three previously undescribed alleles (two short
insertions and one single-base deletion), further illustrating the
ability of this method to discover previously undescribed alleles.
Author contributions: C.W., S.K., R.W.D., M.M.D., and M.M. designed research; C.W., S.K.,
J.W., F.B., L.S., L.F.S., and M.M. performed research; C.W., S.K., D.L., M.A.F.-V., and M.M.
contributed new reagents/analytic tools; C.W., S.K., D.L., M.A.F.-V., M.M.D., and M.M.
analyzed data; and C.W., S.K., M.M.D., and M.M. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
Data deposition: The sequence reported in this paper has been deposited in the GenBank
database (accession no. SRA051897).
1C.W. and S.K. contributed equally to this work.
2To whom correspondence may be addressed. E-mail: email@example.com, dbowe@
stanford.edu, or firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| May 29, 2012
| vol. 109
| no. 22www.pnas.org/cgi/doi/10.1073/pnas.1206614109
We designed PCR primers for each gene such that the most
polymorphic exons and the intervening sequences could be am-
plified as a single product. For class I genes HLA-A, -B, and -C,
primer sequences were selected to amplify the first seven exons.
For HLA-DRB1, we designed primers to capture exons 2–5 and
to avoid amplifying a large (approximately 8 kb) intron between
exons 1 and 2 (Fig. 1). Equimolar amounts of the four HLA gene
products were pooled to ensure equal representation of each
gene and ligated together to minimize bias in the representation
of the ends of the amplified fragments. These ligated products
were then randomly sheared to an average fragment size of 300–
350 bp and prepared for Illumina sequencing, after the addition
of unique barcodes to identify the source of genomic DNA for
each sample, using encoded sequencing adapters. Each sequenc-
ing adapter had a 7-base barcode between the sequencing primer
and the start of the DNA fragment being ligated. The barcodes
were designed such that at least 3 bases differed between any two
barcodes. Samples sequenced in the same lane were pooled to-
gether in equimolar amounts. The sequences of 150 bases from
both ends of each fragment for cell-line samples were deter-
mined using the Illumina GAIIx sequencing platform. For clin-
ical samples, the sequences of 100 and 150 bases from both ends
of each fragment were determined with the Illumina HiSeq2000
and MiSeq platforms, respectively.
For GAIIx sequence reads (counting each paired-end read as
two independent reads), 91.8% of the sequence reads were
parsed and separated according to their barcode tags. After
stripping the barcode tags, 95.5% (∼54 million sequence reads)
were aligned to genomic reference sequences from the In-
ternational ImMunoGene Tics (IMGT)-HLA (http://www.ebi.ac.
uk/imgt/hla/) database (15) with the National Center for Bio-
information Technology (NCBI) BLASTN program, resulting in
an average of 10,600 reads per position (coverage), which was
estimated on the basis of the number of reads mapped to ge-
nomic reference sequences without filtering. For clinical sam-
ples, 97.7% of the sequence reads from the HiSeq2000 instru-
ment were parsed and separated according to their barcode tags.
After stripping the barcode tags, 96.7% (around 152 million
sequence reads) were aligned to genomic references, resulting in
an estimated average of 10,000 reads per position.
Classical HLA Genotype Assignment. Although genomic DNA was
amplified and sequenced in our current approach, the standard
genotype-calling algorithm relies mainly on the alignment to
cDNA references from the IMGT-HLA database due to the lack
of genomic reference sequences. Of 6,398 cDNA reference
sequences for HLA-A, -B, -C, and -DRB1 genes in the IMGT-
HLA database released on October 10, 2011, only 375 (5.8%) of
them have genomic sequences. The IMGT-HLA database con-
tains sequences of HLA genes, pseudogenes, and related genes,
which allowed us to filter out sequences from pseudogenes or
other nonclassical HLA genes, such as HLA-E, -F, -G, -H, -J, -K,
-L, -V, -DRB2, -DRB3, -DRB4, DRB5, -DRB6, -DRB7, -DRB8,
After mapping, the alignments were parsed in the following
order: a best-match filter, a mismatch filter, a length filter, and
a paired-end filter. The best-match filter only kept alignments
with best bit scores. The mismatch filter eliminated alignments
containing either mismatches or gaps. The length filter deleted
alignments shorter than 50 bases in length if their corresponding
exons were longer than 50 bases. It also removed any alignments
shorter than their corresponding exons if those were less than 50
bases in length. Finally, the paired-end filter removed alignments
in which references were mapped to only one end of a paired-
end read, whereas at least one reference was mapped to both
ends of the paired-end read.
HLA genes share extensive similarities with each other, and
many pairs of alleles differ by only a single nucleotide; it is this
extreme allelic diversity that has made definitive SBT difficult
and subject to misinterpretation. For instance, due to the short
read lengths generated using the Illumina platform, it is possible
for the same read to map to multiple references. In this study,
sequencing was performed in the paired-end format so that the
combined specificity of paired-end reads could be used to min-
imize misassignment to an incorrect reference. Also, because of
sequence similarities among different alleles, combinations of
different pairs of alleles could result in a similar pattern of ob-
served nucleotide sequence, on the basis of the fortuitous mix-
ture of sequences. We noted that when reads were mapped onto
a correct reference sequence, they formed a continuous tiling
pattern over the entire sequenced region (Fig. 2 B.1 and B.2).
When reads were mapped onto an incorrect reference sequence,
they formed a staggered tiling pattern at some positions of the
sequenced region (Fig. 2 B.3.). To quantify this difference be-
tween the two alignment patterns, we counted the number of
“central reads” for any given point. Central reads (Fig. 2A) were
empirically defined as mapped reads for which the ratio between
the length of the left arm and that of the right arm related to
a particular point is between 0.5 and 2 (Fig. 2).
The genotype-calling algorithm is based on the assumption
that more reads are mapped to correct reference(s) than to in-
correct reference(s). We could, in a brute-force manner, enu-
merate all possible combinations of references and count the
number of mapped reads for each combination. However, due to
the large number of possible combinations, this approach is very
inefficient. Therefore, we applied a heuristic approach to elimi-
nate those implausible references first. We computed the mini-
mum coverage of overall reads (MCOR) and the minimum
genes. (A) For class I HLA gene (HLA-A, -B, and -C), the forward primer is
located in exon 1 near the first codon and the reverse primer is located in
exon 7. For HLA-DRB1, the forward primer is located at the boundary be-
tween intron 1 and exon 2 and the reverse primer is located within exon 5.
Note that the size of exons or introns in the drawing is not proportional to
their actual size. (B) Agarose gel (0.8%) showing amplicons from long-range
PCR. HLA-A, -B, and -C amplicons are 2.7 kb in length, and the -DRB1
amplicon is around 4.1 kb.
Location of long-range PCR primers and PCR amplicons in HLA
Wang et al.PNAS
| May 29, 2012
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coverage of central reads (MCCR) for each reference. We ig-
nored the MCCR values for 30 bases near intron/exon bound-
aries, which were always zero, on the basis of the definition of
central reads and the cutoff length (Fig. 2). We eliminated the
references with an MCOR less than 20 and an MCCR less than
10, as they were unlikely to be correct. From the remaining
references, we enumerated all possible combinations of either
one reference (homozygous allele) or two references (heterozy-
gous alleles) of the same locus, and counted the number of
distinct reads that mapped to each combination. To compensate
for a single reference (homozygous allele), the number of dis-
tinct reads was multiplied with an empirical value of 1.05 to
avoid miscalls due to spurious alignments. The member(s) in the
combination with maximum number of distinct reads were
assigned as the genotype of that particular sample.
The aforementioned procedure only used the sequence in-
formation in the aligned region to do genotype calling. Such
a process necessarily introduces bias in the interpretation, be-
cause it relies on existing reference data. However, unmapped
nucleotides outside aligned regions could also have important
sequence information for previously undescribed alleles. To
ensure that they were taken into consideration, we implemented
a program named EZ_assembler, which carries out de novo as-
sembly of mapped reads including their unmapped regions.
Briefly, we partitioned the mapped reads, including unmapped
regions, into tiled 40-base fragments with a 1-base offset. We
built a directed, weighted graph where each distinct fragment
was represented as a node and two consecutive fragments of the
same read were connected, and an edge between two nodes was
weighted with the frequency of reads from the two connected
nodes. A contig was constructed on the path with the maximum
sum of weights. By comparing a contig with its corresponding
reference sequence, we were able to identify differences between
a contig built from reads and its closest reference. We applied
the de novo assembly procedure for each candidate allele to
verify the accuracy of the HLA typing and to detect novel alleles.
Genotyping Four Highly Polymorphic HLA Genes in 40 Cell Lines. A
total of 40 cell-line–derived DNA samples of known HLA type
were obtained from IHWG and sequenced at four loci (HLA-A,
-B, -C, and -DRB1). We compared our predictions with the
genotypes reported in the public database for those cell lines. Out
of 229 alleles from the 40 cell lines typed for HLA-A, -B, -C, and
-DRB1 loci, the concordance of our approach with previously
determined HLA types was 99% (226/229, see Dataset S1). To
further test the accuracy of our approach, we evaluated these
discordant alleles by using an independent long-range PCR am-
plification, and sequenced the PCR products using Sanger
sequencing. The HLA-DRB1 locus in the cell-line FH11
(IHW09385) was previously reported as 01:01/11:01:02, which we
found to be 01:01/11:01:01. One nucleotide, 12 bases upstream
from theendofexon2,differentiated HLA-DRB1*11:01:01from
HLA-DRB1*11:01:02. Sanger sequencing verified that the HLA-
DRB1 locus of the cell-line FH11 is 01:01/11:01:01 (Fig. S1). The
reference alleles listed for the HLA-B locus of the cell-line FH34
(IHW09415) are 15/15:21 and, on the basis of our sequencing
showed that Illumina sequencing reads were aligned to both
HLA-B*15:21/15:35 references continuously. HLA-B*15:21 and
HLA-B*15:35 were different in three positions in exon 2, and
seven positions in exon 3. The Sanger sequencing chromatogram
indicated the presence of a mixture in the corresponding posi-
tions at exon 2, matching the expected combination of HLA-
B*15:21/15:35 (Fig. S2). The HLA-B locus of the cell-line ISH3
(IHW09369) was reported as homozygous for 15:26N in the
IHWGcell-line database.OurIllumina sequencing readsmapped
to exons 2–5, but not exon 1 of the HLA-B*15:26N reference.
Instead, the reads mapped to exons 1, 3, 4, and 5, but not exon 2
of the HLA-B*15:01:01:01 reference. There is no reference
the ratio between the length of the left arm and that of the right arm related to a particular point is between 0.5 and 2 (highlighted in red). (B) Mapping
pattern of sequencing reads onto correct references (A and B) and onto an incorrect reference (C). (C) Alignment of references A, B, and C around the anchor
point shown in B. Anchor points are marked as two double-arrow line.
Mapping patterns of sequencing reads on correct and incorrect references. (A) Central reads of an anchor point are defined as mapped reads, where
| www.pnas.org/cgi/doi/10.1073/pnas.1206614109Wang et al.
sequence available where the Illumina reads could tile continu-
ously across the reference sequence. The Sanger sequencing data
confirmed that ISH3 HLA-B allele had the exon 1 sequence as
that of 15:01:01:01 and the sequence of exons 2–5 of 15:26N (Fig.
S3).This finding suggests thateither thereis anerrorin theexon1
region of B*15:26N reference sequence or that it represents yet
another previously undescribed B*15 null allele.
Genotyping Four Highly Polymorphic HLA Genes in 59 Clinical
Samples. To test increased throughput using our approach, we
pooled 59 clinical samples and typed HLA-A, -B, -C, and -DRB1
in a single HiSeq2000 lane. Of these, 47 samples (samples 1–47,
Dataset S2) from an HLA disease association study were typed
both by our methodology and an oligonucleotide hybridization
assay. Even though the resolution of the probe-based assay was
lower, the pairwise comparisons of possible genotypes showed
overlap in at least one possible genotype for all loci in all samples.
There were no allele dropouts in testing by either methodology.
Twelve additional samples included specimens of HSCT patients
or donors that presented less common or unique allele types
(samples 48–59, Dataset S2). In this group, two samples with
insertions of 5 and 8 exonic nucleotide insertions were concor-
dantly typed by both classic Sanger sequencing and by the
methodology described in the present study (Fig. 3 1.a–c and
2.a–c). The occurrence of these insertions shows a change in the
reading frame with the occurrence of premature termination
codons; therefore, the corresponding mature HLA proteins of
these alleles are not expressed on the cell surface (null). In con-
ventional sequencing, both heterozygous alleles are coamplified
and sequenced. However, when one of the alleles contains an
mapped onto HLA-A*02:01:01:01 cDNA reference in one clinical sample. (1.b) Partial alignment between a contig derived from reads mapped onto HLA-
A*02:01:01:01 reference and HLA-A*02:01:01:01 reference. (1.c) Chromatogram of Sanger sequence on a clone derived from HLA-A PCR product from the
same sample. Black arrow 1 highlights a 5-base “TGGAC” insertion in coverage plot (1.a), alignment (1.b), and chromatogram (1.c). (2.a) Coverage of overall
reads (red) and central reads (blue) mapped onto HLA-B*40:02:01 cDNA reference in one clinical sample. (2.b) Partial alignment between a contig derived
from reads mapped onto HLA-B*40:02:01 reference and HLA-B*40:02:01 reference. (2.c) Chromatogram of Sanger sequence on a clone derived from HLA-B
PCR product from the same sample. Black arrow 2 highlights an 8-base “TTACCGAG” insertion in coverage plot (2.a), alignment (2.b) and chromatogram (2.c).
(3.a) Coverage of overall reads (red) and central reads (blue) mapped onto HLA-B*51:01:01 genomic reference in one clinical sample. (3.b) Partial alignment
between a contig derived from reads mapped onto HLA-B*51:01:01 reference and HLA-B*51:01:01 reference. (3.c) Chromatogram of Sanger sequence on
a clone derived from HLA-B PCR product from the same sample. Black arrow 3 highlights a single-base “A” deletion in coverage plot (3.a), alignment (3.b),
and chromatogram (3.c). In the coverage plots, exon regions are indicated with Roman numerals.
Identification and verification of three unique alleles with insertions and deletions. (1.a) Coverage of overall reads (red) and central reads (blue)
Wang et al.PNAS
| May 29, 2012
| vol. 109
| no. 22
insertion or deletion, it results in an off-phase heterozygous se-
quence andthe readoutis cumbersome andlaborious; in contrast,
the readout obtained by the unique methodology was straight-
forward. The precise identification of the type of insertion/de-
letion in these unique alleles is of crucial importance in clinical
histocompatibility practice. The allele containing the insertion or
deletion may not be expressed because the reading frame may
include changes in the amino acid sequence, resulting in the oc-
currence of premature termination codons or it may have altered
expression if the mutations are close to mRNA splicing sites (Fig.
3.3). If a mutation of this nature is overlooked, the evaluation of
the HLA typing match between a patient and an unrelated donor
could easily be incorrect.
In the present study, we identified the alleles B*40:01:02,
A*23:17, and C*07:01:02, which are thought to be rare (Dataset
them may bethe predominantallele oftheir group (B*40:01:02) or
more common than previously thought.
Recently, several laboratories (16–20) have developed high-
throughput HLA genotyping methodologies using massively
parallel sequencing strategies such as Roche/454 sequencing
(21). In all these high-throughput HLA-genotyping studies, with
the exception of the study by Lind et al. (19), a few polymorphic
exons were amplified separately and sequenced in a multiplexed
manner. In our approach, a large genomic region of each gene
including introns and the most polymorphic exons was amplified
in a single PCR and sequenced with a large excess of indepen-
dent paired-end reads. There are two major ambiguities/uncer-
tainties that arise from conventional SBT methods for HLA
genotyping: uncertainties that are commonly seen in typing pro-
tocols where alleles vary outside the targeted regions, and com-
bination ambiguities that are frequently encountered where
different allele combinations yield the same sequence pattern
(22). As more exons of a gene were sequenced, our method (Fig.
S4), which sequenced exons 1–7 for HLA class I genes and exons
2–5 for HLA-DRB1, substantially enhanced the allele resolution
and dramatically improved the combination resolution in com-
parison with the conventional SBT method, which sequences
exons 2 and 3 for HLA class I genes and exon 2 alone for HLA-
DRB1. In addition, the extensive sequence coverage allowed us
to largely overcome genotype calling artifacts. The paired-end
sequencing strategy extends the read length effectively to 400–500
bases, which matches that of the Roche/454 platform, while
allowing much higher throughput. The paired-end reads facili-
tated the determination of linkage phase across 400 bases in each
DNA fragment, and together with polymorphic sites in intron
regions, provided us with important phasing information that was
useful to resolve combination ambiguities.
We validated this long-range PCR amplification and next-
generation sequencing approach by retyping the 40 different
IHWG reference cell lines. The accuracy of this approach was
demonstrated with a high degree (overall 99%) of concordance
between our results and those reported in the reference data-
bases. The Sanger sequencing data confirmed our genotype-
calling results in the discordant alleles in all cell lines. Although
the number of new alleles in public databases has increased
dramatically in the past few years, the list is far from being ex-
haustive asmany ethnic groups have yet to be sequenced in depth.
In particular, populations from areas with high pathogen diversity
are expected with increased HLA diversity in relation to their
average genomic diversity (23). Therefore, the ability of a HLA
genotyping method to discover previously undescribed alleles is
significant. Our approach demonstrates the ability to identify
previously undescribed alleles that have insertions, deletions, and
substitutions. In particular, our strategy of using PCR primers
outside polymorphic regions for long-range PCR increases the
chance of capturing previously undescribed alleles.
Finally, we were interested in optimizing our approach to ac-
from 59 clinical samples typed in a single HiSeq2000 lane, 99.3%
of alleles meet the minimum coverage of 100, and the majority of
them are beyond 900 (Fig. S5). The ratios of minimum coverage
of heterozygous alleles of a gene in the same sample were under
four in all but two samples, indicating that heterozygous alleles of
the same gene were amplified with similar efficiencies and cov-
erage variation are largely due to pooling unevenness. Our sim-
ulation experiment showed that a minimum coverage of 20 could
provide reliable information for genotype calling. With an opti-
mized protocol to improve the pooling evenness, we project that
for HLA typing of four genes, we can pool about 180 samples in
one lane of Illumina HiSeq2000 or 2,700 samples in one
HiSeq2000 instrument run (15 lanes), respectively.
In conclusion, we demonstrate here a successful approach for
determining accurate HLA genotypes in a high-throughput
manner for large numbers of clinical samples simultaneously.
Having such a high throughput effectively lowers the cost per
sample. Indeed, in the setting of testing many subjects simulta-
neously, the cost for high-resolution typing by this methodology is
significantly lower than classical Sanger sequencing and it in the
same range or lower than the cost of probe-based assays, which
have a much lower typing resolution. Therefore, the combination
of high-resolution, high-throughput, and low cost will enable
comprehensive disease-association studies with large cohorts. The
HLA typing approach described here may also be useful in
obtaining high-resolution HLA results of donors and cord blood
units recruited or collected by registries of potential volunteer
donors for bone marrow transplantation and cord blood banks.
Successful outcomes of allogeneic hematopoietic stem cell
transplantation correlate well with close HLA matching between
the patient and the selected donor unit (14, 24). Also, in many
diseases early treatment including hematopoietic stem cell trans-
plantation soon after diagnosis, correlates with superior outcomes
(25). Listing donors and units with the corresponding high reso-
lution HLA type can dramatically accelerate the identification of
optimally compatible donors. On the other hand, we have also
demonstrated that the same approach can be adapted to accom-
modate the need for quick turnaround for urgent samples. With
the Illumina Miseq, we can type a few samples within 5 d. As
improved sequencing technologies are developed, we can adapt
the typing method to suit any sequencing platform, as the align-
ment algorithms and HLA genotype calling are independent of
the sequencing method.
The present study shows that the current knowledge of se-
quence variation in the HLA system can rapidly be expanded by
the application of the latest nucleotide sequencing technologies.
In the present study we were able to analyze comprehensively
segments of the HLA genes that have not been tested routinely.
The testing of these areas will allow us to gain insight into the
fine details of the possible evolutionary pathways of the HLA
variation. Furthermore, these methodologies may allow us to
refine the mapping of susceptibility factors, and potentially of
immunity-enabling features. In this regard, it may be possible to
extend this approach to all HLA genes to discern patient-specific
factors that may influence future vaccination strategies. Simi-
larly, we may be able to obtain more precise evaluation of the
HLA match grade between patients and unrelated donors in
solid organ and hematopoietic stem cell transplantation.
Materials and Methods
HLA typing reference cell lines were obtained from the IHWG (IHWG, www.
ihwg.org) at the Fred Hutchinson Cancer Research Center, Seattle. The
sequence polymorphism reference panel was used for validating the Illu-
mina HLA typing technology. The 47 clinical samples (samples 1–47, Dataset
| www.pnas.org/cgi/doi/10.1073/pnas.1206614109Wang et al.
S2) were drawn from the molecular genetics of schizophrenia I linkage Download full-text
sample (26), which is part of the National Institute of Mental Health Center
for Genetic Studies repository program (http://nimhgenetics.org). The other
12 clinical samples (samples 48–59, Dataset S2) were from specimens of
HSCT patients or donors that presented less common or novel allele types.
Each clinical specimen was collected after subjects signed a written
PCR Primer Design. To design gene-specific primers, we have analyzed all
available sequences and chosen primers that would ensure the amplification
of all known alleles for each gene. We have avoided regions of high vari-
ability, and where necessary, have designed multiple primers to ensure
amplification of all alleles. For class I HLA gene (HLA-A, -B, and -C), the
forward primer was located in exon 1 near the first codon, and the reverse
primer was located in exon 7. Only a limited number of genomic sequences
were available for HLA-DRB1 genes. Therefore, the PCR primer for HLA-DRB1
genes were placed in less divergent exons. Taking into consideration the size
DRB1 was placed at the boundary between intron 1 and exon 2, and the
reverse primer within exon 5. To ensure the robustness of the PCR, the first
exon of DRB1 was not included to avoid amplifying intron 1, which is about
8 kb in length.
ACKNOWLEDGMENTS. Funding from National Institutes of Health (NIH)
Grants U19AI090019, P01HG000205, GM62119 and Defense Threat Re-
duction Agency Grant HDTRA1-11-1-0058 (to R.W.D. and M.M.D.) and
from U19AI090019 and the Howard Hughes Medical Institute (to M.M.D.)
supported this work. L.F.S. is supported by an NIH K08 Award (K08
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| May 29, 2012
| vol. 109
| no. 22