AbstractAccurate typing of human leukocyte antigen (HLA), a histocompatibility test, is important because HLA genes play various roles in immune responses, and have also been shown to be associated with many diseases such as cancer. The current gold standard for HLA typing uses DNA sequencing technology combined with sequence enrichment techniques using specially designed primers or probes, causing it to be slow and labor-intensive. Although there exist enrichment-free computational methods that use various types of sequencing data, hyper-polymorphism found in HLA region of the human genome makes it challenging to type HLA genes with high accuracy from whole genome sequencing data. Furthermore, these methods are database-matching approaches where their output is inherently limited by the completeness of already known types, forcing them to find the best matching known alleles from a database, thereby causing them to be unsuitable for discovery of rare or novel alleles. In order to ensure both high accuracy as well as the ability to type novel alleles, we have developed a graph-guided assembly technique for classical HLA genes, which is capable of assembling phased, full-length haplotype sequences of typing exons given high-coverage (>30-fold) whole genome sequencing data. Our method delivers highly accurate HLA typing, comparable to the current state-of-the-art database-matching methods. We also demonstrate that our method can type novel alleles by experimenting on various data including simulated, Illumina Platinum Genomes, and 1000 Genomes data.
High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs
