P130 An algorithm to detect and quantify low frequency HLA-genotypes in NGS data

Aim Next-generation sequencing (NGS) provides a cost and time efficient approach for routine HLA typing. Analysis software typically presumes a diploid genotype with counts of sequencing reads from homozygous or heterozygous genotypes. However, chimeric samples with more than two alleles per locus and imbalanced read counts may occur, for instance, after partially HLA-mismatched hematopoietic stem cell transplantation. Detection of alleles at even very low allele frequencies can then become diagnostically relevant in the context of post-transplant relapse. Here, we describe and validate a novel algorithm for rare HLA allele detection using NGS data. Methods Sequencing reads are mapped against expected HLA-alleles in a chimeric DNA-sample. Only polymorphic nucleotide positions differentiating the expected allele sequences are considered for further analysis. Reads are classified based on the frequency of discriminatory di-nucleotides. The classification algorithm was validated using in vitro admixtures of DNA samples down to allelic frequencies of 0.5 percent based on NGS data targeting exons 2 and 3 for the HLA genes A, B, C, DQB1, DRB1, and DPB1. Results We found that sensitivity and specificity are allele and locus-specific and depend on the number of sequence differences between the alleles. In each of the analyzed admixture samples a subset of the 12 amplicons harbored sufficient sequence differences to detect genotypes at dilutions as low as 0.5 percent. Fig. 1 shows an example for 3 amplicons. Conclusions The proposed algorithm may support the application of cost effective NGS for sensitive detection of HLA microchimerism in different settings including transplantation and pregnancy. Download high-res image (200KB) Download full-size image