Benchmarking HLA genotyping from whole genome sequencing across multiple sequencing technologies

Abstract Background The hyper⍰polymorphic nature and structural complexity of the human leukocyte antigen (HLA) genomic region present challenges for accurate and scalable typing across diverse sample types. While whole⍰genome sequencing (WGS) offers the opportunity to infer HLA genotypes without targeted enrichment, systematic benchmarks across sequencing platforms, biospecimens and coverage levels remain limited. Results We assembled a multi-platform resource of WGS datasets derived from short-read (Illumina, MGI) and long-read (Oxford Nanopore Technologies R9 and R10) sequencing, spanning 29 biospecimens including cell lines, blood, buccal swab and saliva. We evaluated the performance of the HLA caller HLA*LA across 13 HLA genes, using a clinically validated assay as reference. WGS⍰based HLA genotyping achieved ∼95% accuracy across sequencing platforms, with Class I loci exhibiting higher accuracy than Class II. Cross⍰platform concordance was high, and performance remained consistent across Illumina, MGI and Oxford Nanopore chemistries. Analysis of blood, buccal swab and saliva samples showed that blood and buccal swabs supported accurate HLA inference, whereas saliva yielded reduced concordance. Down⍰sampling experiments demonstrated that 15x coverage was sufficient to retain >95% accuracy at two⍰field resolution, with lower depths supporting lower-resolution typing. Conclusions Our results demonstrate that WGS provides a robust, platform⍰agnostic framework for accurate HLA genotyping across sample types and coverage levels. These benchmarks establish practical conditions for reliable HLA inference and underscore the utility of WGS for population⍰scale HLA analyses and future clinical applications.