Hla typing

Version Compatibility

Reference examples tested with: OptiType 1.3.5, HLA-LA 1.0.4, T1K 1.0.6 (Song 2023), Polysolver 4.0, HLA-HD 1.7.1, arcasHLA 0.6.0, StarPhase 1.0+ (PacBio), HIBAG 1.40+, samtools 1.19+, bwa-mem 0.7.17+. IPD-IMGT/HLA database release frequency is quarterly; tools must be re-bundled with the current release to capture new alleles (~38,000 alleles at Jan 2024; ~43,000+ by Jul 2025).

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Tool reference-bundle vintage matters more than algorithm choice for non-European cohorts; a 2022-bundled HLA-LA will silently miss thousands of post-2022 alleles dominant in African and South Asian ancestry.

HLA Typing for Clinical Applications

'Determine HLA genotype for HSCT / neoantigen prediction / PGx screening' -> Call HLA class I (A, B, C) and class II (DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1) alleles at the resolution required by the downstream application.

• CLI (general-purpose all-rounder): t1k --preset hla -1 R1.fq -2 R2.fq -f hla_reference.fa
• CLI (class I gold standard from WES/WGS): OptiTypePipeline.py -i R1.fq R2.fq -d
• CLI (class I + II with PRG): HLA-LA.pl --BAM input.bam --graph PRG_MHC_GRCh38_withIMGT
• CLI (RNA-seq): arcasHLA extract sample.bam -o out && arcasHLA genotype out/sample.extracted.fq.gz
• CLI (long-read transplant-grade): PacBio HiFi StarPhase
• R (imputation from SNP arrays): HIBAG::predict() with ancestry-stratified reference panel

Resolution Levels and What Each Application Requires

HLA nomenclature: HLA-A*02:01:01:01 = family : protein-changing : synonymous : intronic/UTR. Expression suffixes: N (null; DNA present, no protein expressed); L (low expression); S (secreted); Q (questionable); A (aberrant). A serologically apparent DR4-positive donor carrying DRB4*01:03:01:02N is functionally DR53-negative; a classic HSCT donor-selection failure.

Application	Min resolution	Why
HSCT (unrelated donor)	6-field (12/12 match)	Null alleles + permissive DPB1 + Bw4/Bw6 + TCE3 core/non-core
Solid organ transplant	4-field (2-digit:2-digit)	Eplet-level epitope match (HLAMatchmaker, PIRCHE-II)
ICI neoantigen prediction	4-field class I + II	NetMHCpan-4.1 minimum
HLA-disease association	4-field	Standard for GWAS HLA fine-mapping
HLA-B*57:01 abacavir screen	4-field, specific	Other *57 alleles (*57:03) do NOT cause HSS
HLA-B*15:02 carbamazepine	4-field, specific	*15:02 only; *15:01 (NFE-common) is not the risk allele

G-Groups vs P-Groups: Routinely Confused

• G-groups collapse alleles with identical DNA sequence across the antigen-recognition exons (class I exons 2-3; class II exon 2). Use for sequence-level lab QC.
• P-groups collapse alleles encoding identical mature protein across class I positions 1-90 (or class II beta1 domain positions 1-94). Use for epitope-based matching and neoantigen prediction.

DRB1 + DRB3/4/5 Linkage: The Mandatory Sanity Check

DR haplotype linkage is fixed and is the canonical sanity check on any DR typing:

DRB1 allele family	Linked DRB3/4/5
DR1 (*01), DR8 (*08), DR10 (*10)	None
DR3 (*03), DR11 (*11), DR12 (*12), DR13 (*13), DR14 (*14)	DRB3
DR4 (*04), DR7 (*07), DR9 (*09)	DRB4
DR15 (*15), DR16 (*16)	DRB5

Any caller reporting DRB4 with DRB1*15:01 is broken or has a chimera. Use this as a routine QC check on automated pipelines.

Algorithmic Taxonomy: Short-Read Tools

Tool	Class I	Class II	KIR	Resolution	Approach	Fails when
OptiType (Szolek 2014 Bioinformatics 30:3310)	Yes (~97% 4-digit)	No	No	4-field	ILP on exons 2-3	Class II needed; very deep contamination
Polysolver (Shukla 2015 Nat Biotechnol 33:1152)	Yes (~95% 4-digit)	No	No	4-field	Allele-specific ref alignment	Class II; non-European ancestry under-typing
HLA-LA (Dilthey 2019 Bioinformatics 35:4394)	Yes (~94% class I)	Yes (strong class II)	No	4-field	Graph-based PRG	High RAM/disk (~30-100 GB scratch)
T1K (Song 2023 Genome Res)	Yes (~99% 4-digit)	Yes (~99%)	Yes (KIR + KIR3DL2 ligand)	4-field	EM on consensus reference	Newer; less benchmarking on edge cases
HLA-HD (Kawaguchi 2017 Hum Mutat 38:788)	Yes (~98%)	Yes (~95%)	No	4-field	Bowtie2 against IPD-IMGT	License required for commercial use
arcasHLA (Orenbuch 2020 Bioinformatics 36:33)	Yes (~100% 2-field)	Yes (>99% 2-field)	No	4-field from RNA-seq	EM on STAR alignment	DNA-seq; population prior bias in non-EUR
PHLAT, HLAforest, HLAminer, seq2HLA, HLAreporter	Yes	Some	No	Mostly 2-4 field	Various	Older; superseded

Operational benchmark consensus: in the Claeys 2023 BMC Genomics 13-tool benchmark (Matey-Hernandez 2018), HLA-HD was the top class-II caller and OptiType (WES) / arcasHLA (RNA) the class-I anchors. T1K (Song 2023, not in that benchmark) adds class I + II + KIR co-typing in one pass and is the 2024-2026 all-rounder recommendation for WGS/WES.

Long-Read and Ultra-High-Resolution

Tool	Platform	Resolution	Use case
StarPhase (PacBio official 2024+)	PacBio HiFi	8-field (full-field)	Transplant-grade typing
HLA*ASM	PacBio HiFi	8-field	Assembly-based
FuFiHLA (2025 bioRxiv)	PacBio HiFi + ONT R10	8-field	Platform-agnostic
HLAminer streaming (Warren 2025)	ONT long-read	4-field	Streaming nanopore
pbaa + StarPhase	PacBio amplicon	8-field	Cost-effective targeted typing

ONT R9 was historically unreliable for null-allele discrimination due to homopolymer errors; R10.4 with duplex closes the gap for class I and is competitive with PacBio HiFi for class II. PacBio HiFi remains the gold standard for DPB1 4-field typing.

SNP-Based HLA Imputation: The Ancestry Footgun

When only SNP-array genotypes are available (GWAS cohorts), use imputation:

Tool	Approach	Reference panel	Best for
HIBAG (Zheng 2014 Pharmacogenomics J 14:192)	Random forest from SNP-array	Pre-fit per-ancestry classifiers (EUR, AS, AFR, HIS)	Population-stratified GWAS
HLA-TAPAS (Luo 2021 Nat Genet 53:1504)	Multi-ancestry imputation	21,546 multi-ancestry reference	Cross-ancestry GWAS
HLA*IMP:02 (Dilthey 2013)	Hidden Markov	EUR-only	Legacy; EUR-only
SNP2HLA (Jia 2013)	Beagle-based	Type 1 Diabetes / EUR	Older; EUR-only
CookHLA (Cook 2021)	Hybrid SNP2HLA + supplementary	Multi-ancestry refs	Modern alternative to SNP2HLA
Multi-Ethnic Reference Panel (Degenhardt 2019)	Multi-ancestry imputation	Cross-population samples	Cross-ancestry GWAS

Critical caveat: imputation panel quality is the limiting factor, NOT the imputation algorithm. EUR-trained HIBAG on East-Asian SNP-array data produces confidently wrong calls. African-ancestry imputation accuracy drops 10-20 percentage points without an ancestry-matched panel (Douillard 2024 HLA). For populations underrepresented in IPD-IMGT/HLA itself, imputation is fundamentally limited regardless of method.

Decision Tree by Scenario

Scenario	Recommended path	Why
WGS/WES, class I only, max speed	OptiType	Best class-I accuracy, ILP-based, fast
WGS/WES, class I + II, general-purpose	T1K	Best all-rounder; class I + II + KIR co-typing
WGS/WES, class II reference grade	HLA-LA	Strong class-II accuracy (graph-based PRG)
RNA-seq tumor/normal for ICI	arcasHLA	RNA-seq native; expressed-allele-aware
Transplant 6+ field resolution	StarPhase (PacBio HiFi)	8-field native; reference standard
Cost-effective targeted typing	pbaa + StarPhase amplicons	Lower cost than WGS
TCGA-style cancer cohort	Polysolver	TCGA convention; reproduces published values
SNP array (e.g., UKB)	HIBAG with population-matched panel	No sequencing data
Multi-ancestry GWAS	HLA-TAPAS	Cross-ancestry reference
Class II DPB1 4-field certainty	StarPhase or HiFi	Pre-2021 WES kits under-cover DPB1
ONT-only data	T1K or HLAminer streaming for class I; ONT R10.4+ duplex for class II	R9 unreliable for nulls

HLA and Pharmacogenomics

HLA allele	Drug	Reaction	Population enrichment	OR
B*57:01	Abacavir	Hypersensitivity syndrome	All ancestries (5-8% NFE)	~100
B*15:02	Carbamazepine, oxcarbazepine	SJS/TEN	Han Chinese, Thai, Malay (>=5%)	~2500
B*58:01	Allopurinol	SJS/TEN	Han Chinese, Korean, Thai	~580
A*31:01	Carbamazepine	DRESS, MPE	Europeans, Japanese	~12
B*13:01	Dapsone	DDS	Han Chinese, SE Asian	--
B*35:02 (NOT *35:01)	Minocycline	DILI	All ancestries	--
B*35:01	TMP-SMX	DILI	Mixed	--
B*14:01	TMP-SMX	DILI	African	--
A*33:01/03	Terbinafine	DILI	Multi-ancestry	--
DRB1*15:01 + DQB1*06:02 haplotype	Amoxicillin-clavulanate	DILI	Europeans	--
B*15:13	Phenytoin	SJS	Malaysian	--

Operational rule: Pharmacogenomic HLA screening requires 4-field resolution; 2-field (e.g., "B*15") misses the specific allele.

Standard Workflow: T1K on WGS/WES

Goal: Type HLA class I, class II, KIR from short-read sequencing with KIR3DL1 Bw4/Bw6 ligand prediction.

Approach: Extract MHC-region reads, run T1K with IPD-IMGT/HLA reference; T1K outputs allele-pair calls + class II haplotype + KIR.

# Extract chr6:28-34 Mb plus alt contigs (alt-aware alignment is critical)
samtools view -b -h input.bam chr6:28000000-34000000 chr6_GL000250v2_alt chr6_GL000251v2_alt \
                              chr6_GL000252v2_alt chr6_GL000253v2_alt chr6_GL000254v2_alt \
                              chr6_GL000255v2_alt chr6_GL000256v2_alt > hla_region.bam

samtools sort -n hla_region.bam -o hla_sorted.bam
samtools fastq -1 hla_R1.fq -2 hla_R2.fq -s singletons.fq -0 /dev/null hla_sorted.bam

# Run T1K (preset hla; includes class I + II).
# Some releases ship the entry point as `run-t1k` (a wrapper script) rather than `t1k`;
# verify with `which run-t1k` / `which t1k` before scripting.
t1k --preset hla \
    -1 hla_R1.fq -2 hla_R2.fq \
    -f hla_idx/hlaidx_rna_seq.fa \
    -o sample_hla \
    --threads 8

# Output: sample_hla_genotype.tsv with HLA-A, B, C, DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1

OptiType for Class I (TCGA-Compatible)

Goal: Type HLA-A, B, C at 4-field from WES with high accuracy.

Approach: Razers3-based alignment to IMGT class-I reference; ILP optimization to assign reads to allele pairs.

samtools view -h input.bam chr6:28000000-34000000 | samtools fastq -1 R1.fq -2 R2.fq -
OptiTypePipeline.py -i R1.fq R2.fq -d -o optitype_out -c config.ini

# config.ini
[mapping]
razers3=/usr/bin/razers3
threads=8
[ilp]
solver=glpk
threads=8
[behavior]
deletebam=true
unpaired_weight=0
use_discordant=false

HLA-LA for Class II (PRG-Based)

Goal: Type both class I and class II at 4-field with the highest class-II accuracy of any WES tool.

Approach: Population reference graph (PRG) covering the MHC; HLA-LA maps reads to the PRG and infers the most likely paths.

HLA-LA.pl \
    --BAM input.bam \
    --graph PRG_MHC_GRCh38_withIMGT \
    --workingDir hla_la_out \
    --sampleID sample_name \
    --maxThreads 8

# Output: hla_la_out/sample_name/hla/R1_bestguess_G.txt
# Format: Locus, Allele1, Allele2, AverageCoverage

arcasHLA for RNA-seq

Goal: Type HLA class I + II directly from RNA-seq for ICI neoantigen prediction.

Approach: Extract HLA-mapped reads from STAR BAM, EM-based genotype call against IMGT.

# Update reference to current IPD-IMGT/HLA release
arcasHLA reference --update

# Extract and genotype
arcasHLA extract sample.bam -o arcas_out --threads 8
arcasHLA genotype arcas_out/sample.extracted.fq.gz -o arcas_out --threads 8 --population prior

# Output: arcas_out/sample.genotype.json

SNP-Array Imputation (HIBAG): For GWAS Cohorts

Goal: Impute HLA from SNP array genotypes when sequencing is unavailable.

Approach: HIBAG random-forest classifier with population-matched reference panel.

library(HIBAG)

# Population-matched panel is critical; mismatch causes systematic errors
# Available panels: EUR, ASN, AFR, HIS (download from HIBAG release page)
load('European-HLA4-hg19.RData')

# Load PLINK genotype (.bed/.bim/.fam)
gen <- hlaBED2Geno(bed.fn='cohort.bed', fam.fn='cohort.fam', bim.fn='cohort.bim')

# Predict each locus
hla_A <- predict(model.list[['A']], gen, type='response+prob')
hla_B <- predict(model.list[['B']], gen, type='response+prob')
hla_DRB1 <- predict(model.list[['DRB1']], gen, type='response+prob')

# Filter on probability >= 0.5 for downstream use; lower for exploratory

Per-Operation Failure Modes

1. Alt-aware alignment missing

• Trigger: BAM was aligned with bwa-mem against GRCh38 without --alt-aware; HLA reads are coerced to chr6 primary contigs.
• Mechanism: GRCh38 has ~8 alternate HLA contigs (chr6_GL000250v2_alt, etc.); without alt-aware alignment, reads from these regions get assigned to suboptimal positions on the primary chr6.
• Symptom: HLA typing accuracy drops 5-10 percentage points; high read-coverage variants get miscalled.
• Fix: Re-align the HLA region with bwa-mem-alt or use the original cDNA reference for HLA typing (extract reads to FASTQ first).

2. Stale IPD-IMGT/HLA bundle

• Trigger: Tool was installed in 2022 with the corresponding IPD-IMGT/HLA release; never updated.
• Mechanism: ~5000+ new alleles added between 2022 and 2025; new alleles dominant in under-represented ancestries.
• Symptom: Non-European samples get common alleles reported as ambiguous or as the closest legacy match.
• Fix: Update the tool's reference bundle (HLA-LA: rebuild PRG; T1K: re-run t1k-build; OptiType: update data/hla_reference_dna.fasta).

3. EUR-trained imputation on non-EUR samples

• Trigger: Use HIBAG European panel on East-Asian or African ancestry samples.
• Mechanism: Random forest was trained on EUR allele frequencies; non-EUR alleles missing from training set.
• Symptom: Confidently wrong calls; high probability assigned to incorrect alleles.
• Fix: Use ancestry-matched HIBAG panel; or switch to HLA-TAPAS multi-ancestry; or fall back to sequencing.

4. Cross-mapping DRB-related loci

• Trigger: Naive bwa-mem alignment without read-grouping at DRB1/DRB3/DRB4/DRB5.
• Mechanism: DRB1, DRB3, DRB4, DRB5 share extensive sequence identity; reads map ambiguously.
• Symptom: DR3/DR4/DR5 paralog reads contaminate DRB1 calls; haplotype linkage rule (e.g., DRB1*15:01 + DRB5) violated.
• Fix: Use HLA-LA or T1K which model paralogous loci jointly; verify DRB1+DRB3/4/5 haplotype rule.

5. DPB1 under-coverage in pre-2021 WES kits

• Trigger: Used SureSelect v5 or Nextera Rapid Capture WES; DPB1 reports homozygous typing.
• Mechanism: Pre-2021 capture kits under-covered DPB1 exon 2.
• Symptom: Heterozygous DPB1 reported as homozygous; affects HSCT matching.
• Fix: Confirm capture coverage at DPB1; if insufficient, supplement with targeted amplicon or use WGS/long-read.

6. Class II expression-allele confusion

• Trigger: Report DRB4*01:03:01:02N as functional DR53.
• Mechanism: N-suffix = null allele (DNA present but no protein expressed).
• Symptom: Functionally DR53-negative donor reported as DR53-positive; transplant matching failure.
• Fix: Parse 4-field suffix (N, L, S, Q, A); treat N as null in functional analysis; preserve full nomenclature for typing report.

7. Specific allele vs allele family confusion

• Trigger: PGx screen reports "B*57" carrier as abacavir-risk-positive.
• Mechanism: HLA-B*57 family includes *57:01 (abacavir HSS risk), *57:02, *57:03 (no HSS risk).
• Symptom: False-positive abacavir contraindication; patient denied effective therapy.
• Fix: Report at 4-field minimum; B*57:01 specifically, not B*57.

8. KIR co-typing mistaken for HLA

• Trigger: Report KIR allele as HLA.
• Mechanism: KIR (chromosome 19) and HLA (chromosome 6) are functionally paired (KIR3DL1 binds HLA-Bw4) but are distinct loci.
• Symptom: Wrong locus annotation; downstream tools fail.
• Fix: Use T1K which co-types HLA + KIR + KIR3DL2 ligand and labels output correctly.

Reconciliation: When Tools Disagree

Pattern	Likely cause	Action
OptiType vs HLA-LA class I disagree	Stale reference bundle in one; non-EUR ancestry	Update both; rerun; prefer the one with current reference
HLA-LA vs T1K class II disagree	DRB1+DRB3/4/5 haplotype rule violated in one	Check haplotype linkage; the consistent caller is correct
HIBAG vs sequencing disagree	EUR-trained model on non-EUR sample	Trust sequencing; use ancestry-matched HIBAG panel
Tumor vs normal HLA differ	Tumor LOH at HLA locus (frequent in NSCLC, HNSCC)	Run LOHHLA / DASH to confirm somatic loss; report germline + somatic
DPB1 homozygous on WES, het on WGS	WES kit under-covers DPB1 exon 2	Trust WGS; flag WES result as low confidence
Class I 4-field stable across tools, class II differs	Class II is fundamentally harder	Prefer HLA-LA or StarPhase for class II
arcasHLA vs OptiType for tumor RNA	arcasHLA returns expressed-allele only (may miss silenced allele due to LOH)	Confirm with DNA-based typing for transplant context

Quantitative Thresholds and Conventions

Threshold	Convention	Source
IPD-IMGT/HLA quarterly release	Updates Jan/Apr/Jul/Oct	IPD-IMGT/HLA database
Current allele count	~43,000+ at Jul 2025	IPD-IMGT/HLA database release notes (Barker DJ et al, NAR DB issue)
HLA region coordinates	chr6:28000000-34000000 (GRCh38)	Standard
HLA-LA RAM requirement	~30-100 GB scratch	HLA-LA documentation
OptiType class I 4-digit accuracy	~98% (1000G benchmark)	Claeys 2023
Polysolver class I 4-digit accuracy	~95%	Matey-Hernandez 2018
HLA-HD class II accuracy	Top class-II WES tool	Claeys 2023
T1K class I + II accuracy	~99% / ~99%	Song 2023
HIBAG probability cutoff	>=0.5 for clinical-grade; >=0.3 for exploratory	HIBAG documentation
1000G allele coverage	~60-70% of African-ancestry alleles still under-represented in IPD-IMGT/HLA	Robinson 2024
HSCT matching standard	10/10 or 12/12 at 6-field	NMDP/WMDA guidelines
TCE3 core alleles	DPB1*02:01, *04:01, *04:02, *23:01	Arrieta-Bolaños 2022 Blood 140:659

CIWD v3.0.0 Ambiguity Catalogue

Hurley 2020 HLA 95:516; compiled from >8M unrelated HSCT donors across 7 geographic/ancestral groups. Categories: Common (18%, n=545), Intermediate (17%, n=513), Well-Documented (65%, n=1,997) at 2-field. Replaces legacy CWD 2.0 (Mack 2013); many older pipelines still hardcode CWD 2.0; a quiet quality failure.

TCE3 Core vs Non-Core (Arrieta-Bolaños 2022/2024 Blood)

DPB1 mismatch GvHD/relapse risk depends on TCE3 group:

• Core (DPB1*02:01, *04:01, *04:02, *23:01): GvHD reduction with permissive mismatch in the GvH direction.
• Non-core: Relapse-protection effects predominate.

Now operational in NMDP donor selection algorithms; legacy TCE3 frameworks (Crocchiolo 2009) lack this stratification.

Common Errors

Symptom	Cause	Solution
HLA-DRA in output (DRB1 expected)	Tool confused paralogs	Use HLA-LA or T1K which model paralog loci correctly
Class II reports "no call"	Pre-2021 WES kit under-covers class II	Switch to WGS or amplicon
Tumor and normal HLA differ	LOH at HLA locus	Confirm with LOHHLA; report germline call as ground truth
Imputation reports rare allele with high probability	Reference panel mismatch with cohort ancestry	Switch to ancestry-matched panel
4-field call but only 2-field appears in report	Tool default truncation	Use --full-field or equivalent flag
Same sample gives different 4-field calls across runs	Stochastic tie-breaking	Pin random seed; report all equally-supported calls
DRB4 with DRB1*15	Linkage rule violated; bug or chimera	Re-run; check for sample swap
Null allele not reported in summary	Tool drops N-suffix; output is misleading	Use raw 4-field output; never strip suffixes for clinical reports

Anticipated Reviewer Pushback

Pushback	Standard response
"Why T1K when HLA-LA is the published reference?"	T1K matches HLA-LA accuracy on class II while also typing class I + KIR in one pass with lower RAM; we cite both.
"These African-ancestry samples have low confidence"	IPD-IMGT/HLA still under-represents African ancestry (~30-40% allele gap); we ran with current 2025 release; for transplant we recommend long-read confirmation.
"DRB1 vs DRB3/4/5 reported inconsistently"	We verified DRB1+DRB3/4/5 linkage rule on each sample as routine QC; flagged violations for re-typing.
"Why is HLA-B*15:01 not flagged for carbamazepine?"	*15:01 (NFE common) is not the SJS risk allele; *15:02 (Han Chinese) is. PGx requires 4-field specificity.
"Imputation results differ from sequencing"	Imputation panel quality is the limiting factor; EUR-trained HIBAG on non-EUR is unreliable; we used ancestry-matched panel.
"TCGA pipeline used Polysolver, why T1K?"	TCGA convention is Polysolver; for current analysis we use T1K which has better class-II and KIR coverage. We can reproduce Polysolver if back-comparison needed.

References

• Robinson J et al. 2024. 25 years of the IPD-IMGT/HLA Database. HLA 103:e15549.
• Barker DJ et al. 2026. The IPD-IMGT/HLA database: recent developments in sequence submission. Nucleic Acids Res 54:D1152.
• Szolek A et al. 2014. OptiType: precision HLA typing from NGS data. Bioinformatics 30:3310.
• Dilthey AT et al. 2019. HLA*LA; HLA typing from linearly projected graph alignments. Bioinformatics 35:4394.
• Song L et al. 2023. Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data. Genome Res 33:923.
• Shukla SA et al. 2015. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol 33:1152.
• Kawaguchi S et al. 2017. HLA-HD: An accurate HLA typing algorithm for next-generation sequencing data. Hum Mutat 38:788.
• Orenbuch R et al. 2020. arcasHLA: high-resolution HLA typing from RNAseq. Bioinformatics 36:33.
• Claeys A et al. 2023. Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. BMC Genomics 24:247.
• Matey-Hernandez ML et al. 2018. Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios. BMC Bioinformatics 19:239.
• Zheng X et al. 2014. HIBAG; HLA genotype imputation with attribute bagging. Pharmacogenomics J 14:192.
• Luo Y et al. 2021. A high-resolution HLA reference panel capturing global population diversity. Nat Genet 53:1504.
• Hurley CK et al. 2020. Common, intermediate and well-documented HLA alleles in world populations: CIWD version 3.0.0. HLA 95:516.
• Arrieta-Bolaños E et al. 2022. A core group of structurally similar HLA-DPB1 alleles drives permissiveness after HCT. Blood 140:659.
• Arrieta-Bolaños E et al. 2024. Directionality of HLA-DP permissive mismatches improves risk prediction. Blood 144:1747.
• Douillard V et al. 2024. Optimal population-specific HLA imputation with dimension reduction. HLA 103:e15282.

Related Skills

• clinical-databases/pharmacogenomics - HLA-drug interactions, abacavir/carbamazepine screening
• immunoinformatics/mhc-binding-prediction - Downstream HLA-peptide binding for neoantigen
• workflows/neoantigen-pipeline - HLA typing as upstream step
• clinical-databases/clinvar-lookup - HLA disease associations
• population-genetics/population-structure - Ancestry-aware imputation context