Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations - Wagner, Ines; Schefzyk, Daniel; Pruschke, Jens; Schöfl, Gerhard; Schöne, Bianca; Gruber, Nicole; Lang, Kathrin; Hofmann, Jan; Gnahm, Christine; Heyn, Bianca; Marin, Wesley M.; Dandekar, Ravi; et al. (Frontiers in Immunology, 2018)
The killer-cell immunoglobulin-like receptor (KIR) genes regulate natural killer cell activity, influencing predisposition to immune mediated disease, and affecting hematopoietic stem cell transplantation outcome. Owing to the complexity of the KIR locus, with extensive gene copy number variation and allelic diversity, high-resolution characterization of KIR has so far been applied only to relatively small cohorts. Here, we present a comprehensive high-throughput KIR genotyping approach based on next generation sequencing. Through PCR amplification of specific exons, our approach delivers both copy numbers of the individual genes and allelic information for every KIR gene. Ten-fold replicate analysis of a set of 190 samples revealed a precision of 99.9%. Genotyping of an independent set of 360 samples resulted in an accuracy of more than 99% taking into account consistent copy number prediction. We applied the workflow to genotype 1.8 million stem cell donor registry samples. We report on the observed KIR allele diversity and relative abundance of alleles based on a subset of more than 300,000 samples. Furthermore we identified more than 2,000 previously unreported KIR variants repeatedly in independent samples, underscoring the large diversity of the KIR region that awaits discovery. This cost-efficient high-resolution KIR genotyping approach is now applied to samples of volunteers registering as potential donors for hematopoietic stem cell transplantation. This will facilitate the utilization of KIR as additional selection criterion to improve unrelated donor stem cell transplantation outcome. In addition, the approach may serve studies requiring high-resolution KIR genotyping, like population genetics and disease association studies.