qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes.
Journal of visualized experiments : JoVE
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Jayaraman, J., Kirgizova, V., Di, D., Johnson, C., Jiang, W., & Traherne, J. (2019). qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes.. Journal of visualized experiments : JoVE, (145)https://doi.org/10.3791/58646
Killer cell immunoglobulin-like receptors (KIRs) are a set of inhibitory and activating immune receptors, on natural killer (NK) and T cells, encoded by a polymorphic cluster of genes on chromosome 19. Their best-characterized ligands are the human leukocyte antigen (HLA) molecules that are encoded within the major histocompatibility complex (MHC) locus on chromosome 6. There is substantial evidence that they play a significant role in immunity, reproduction, and transplantation, making it crucial to have techniques that can accurately genotype them. However, high-sequence homology, as well as allelic and copy number variation, make it difficult to design methods that can accurately and efficiently genotype all KIR genes. Traditional methods are usually limited in the resolution of data obtained, throughput, cost-effectiveness, and the time taken for setting up and running the experiments. We describe a method called quantitative KIR semi-automated typing (qKAT), which is a high-throughput multiplex real-time polymerase chain reaction method that can determine the gene copy numbers for all genes in the KIR locus. qKAT is a simple high-throughput method that can provide high-resolution KIR copy number data, which can be further used to infer the variations in the structurally polymorphic haplotypes that encompass them. This copy number and haplotype data can be beneficial for studies on large-scale disease associations, population genetics, as well as investigations on expression and functional interactions between KIR and HLA.
Humans, Haplotypes, Linkage Disequilibrium, Automation, Software, Receptors, KIR, DNA Copy Number Variations
European Commission Horizon 2020 (H2020) ERC (695551)
External DOI: https://doi.org/10.3791/58646
This record's URL: https://www.repository.cam.ac.uk/handle/1810/297475
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/