The Human Leukocyte Antigen (HLA) is the most polymorphic region of the genome, with a key role in the immunological response. In humans, HLA genes are contained within the major histocompatibility complex (MHC) locus, consisting ofmore than 200 genes located on chromosome 6. Determining individual HLA types is of critical importance in many aspects of medicine, including transplantation, where donor and recipient tissue types must be matched in order to avoid rejection.
The enormous genetic heterogeneity of this region poses significant challenges in HLA typing. Traditionally, HLA typing has been performed using either serological testing (using white blood cells), or DNA testing (extracting DNA from the white blood cells), and relies upon amplification of the specific region to be studied.
Two recent papers published in Genome Medicine describe computational methods for obtaining an individual’s HLA type using Next Generation Sequence (NGS) reads. Rene Warren and colleagues present HLAminer, a computational method to identify HLA alleles directly from shotgun sequence datasets. Sebastian Boegel and colleagues present seq2HLA, a software tool to determine HLA alleles from RNA-Seq reads. The advantage of both methods is evident; HLA calls can be made directly from sequence reads, reducing the need to generate HLA-specific data, and can be used for both existing and future datasets with no additional laboratory protocols. As the specificity and resolution of these methods increases, these techniques could save valuable time and money in the clinic, as well as unlocking valuable information from datasets with no additional experimentation.