kir-mapper: A Toolkit for Killer-Cell Immunoglobulin-Like Receptor (KIR) Genotyping From Short-Read Second-Generation Sequencing Data

Abstract

Killer cell immunoglobulin-like receptors (KIRs) regulate natural killer (NK) cell responses by activating or inhibiting their functions. Genotyping KIR genes from short-read second-generation sequencing data remains challenging as cross-alignments among genes and alignment failure arise from gene similarities and extreme polymorphism. Several bioinformatics pipelines and programs, including PING and T1K, have been developed to analyse KIR diversity. We found discordant results among tools in a systematic comparison using the same dataset. Additionally, they do not provide SNPs in the context of the reference genome, making them unsuitable for whole-genome association studies. Here, we present kir-mapper, a toolkit to analyse KIR genes from short-read sequencing, focusing on detecting KIR alleles, copy number variation, as well as SNPs and InDels in the context of the hg38 reference genome. kir-mapper can be used with whole-genome sequencing (WGS), whole-exome sequencing (WES) and sequencing data generated after probe-based capture methods. It presents strategies for phasing SNPs and InDels within and among genes, reducing the number of ambiguities reported by other methods. We have applied kir-mapper and other tools to data from various sources (WGS, WES) in worldwide samples and compared the results. Using long-read data as a truth set, we found that WGS kir-mapper analyses provided more accurate genotype calls than PING and T1K. For WES, kir-mapper provides more accurate genotype calls than T1K for some genes, particularly highly polymorphic ones (KIR3DL3 and KIR3DL2). This comparison highlights that the choice of method has to be considered as a function of the available data type and the targeted genes. kir-mapper is available at the GitHub repository (https://github.com/erickcastelli/kir-mapper/).