Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors.

ABSTRACT

Accurate human leukocyte antigen (HLA) genotyping is critical in studies involving the immune system. Several algorithms to estimate HLA genotypes from whole-exome data were developed. We compared the accuracy of seven algorithms, including Optitype, Polysolver and PHLAT, as well as investigated patterns and possible causes of miscalls using 12 clinical samples and 961 individuals from the 1000 Genomes Project. Optitype showed the highest accuracy of 97.2% for HLA class I alleles at the second field resolution, followed by 94.0% in Polysolver and 85.6% in PHLAT. In Optitype, 34 (21.1%) of 161 miscalls were across different serological types, and common miscalls were HLA-A*2601 to HLA-A*2501, HLA-B*4501 to HLA-B*4415 and HLA-C*0802 to HLA-C*0501 with error rates of 4.1%, 10.0% and 4.1%, respectively. In Polysolver, 193 (55.9%) of 345 miscalls occurred across different serological alleles, and a specific pattern of genotyping error from HLA-A*2501 to HLA-A*2601 was observed in 93.3% of HLA-A*2501 carriers, due to dropping of HLA-A*2501 sequence reads during the extraction process of HLA reads. In PHLAT, 147 (59.8%) of 246 miscalls in HLA-A were due to erroneous assignment of multiple alleles to either HLA-A*0122 or HLA-A*0181. These results suggest that careful considerations needed to be taken when using exome-based HLA class I genotyping data and applying these results in clinical settings.