Improved HLA typing of Class I and Class II alleles from next-generation sequencing data

Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.