Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing

Area of Science:

• Genomics
• Immunogenetics

Background:

• Accurate human leukocyte antigen (HLA) typing is crucial for organ transplantation and cancer immunotherapy.
• Challenges in HLA typing include high sequence homology and extreme polymorphism of HLA genes.

Purpose of the Study:

• To introduce Orthanq, a novel statistical model and application for transparent and uncertainty-aware haplotype quantification.
• To enable accurate HLA typing with reported prediction uncertainty and mutation detection.

Main Methods:

• Development of a novel statistical model (Orthanq) for haplotype quantification.
• Application of the model to perform HLA typing on DNA-sequenced human samples.
• Utilizing existing pangenome alignments for comprehensive HLA locus typing.

Main Results:

• Orthanq provides transparent and uncertainty-aware quantification of haplotypes.
• The tool reports prediction uncertainty and detects mutations beyond known HLA types.
• Demonstrated superior accuracy and shorter runtimes compared to state-of-the-art HLA typers using 99 gold standard samples.

Conclusions:

• Orthanq is the first approach to directly utilize pangenome alignments for typing all HLA loci.
• The methodology can be generalized for applications beyond HLA typing, such as virus lineage quantification.
• Orthanq is publicly available for research use.