Allele imputation for the Killer cell Immunoglobulin-like Receptor KIR3DL1/S1
Authors: Harrison Genelle F , Leaton Laura Ann , Harrison Erica A , Viken Marte K , Shortt Jonathan , Gignoux Christopher R , Lie Benedicte A , Vukcevic Damjan , Leslie Stephen , Norman Paul J 
Published: May 2021

AbstractHighly polymorphic interactions of KIR3DL1 and KIR3DS1 with HLA class I ligands modulates the effector functions of natural killer (NK) cells and some T cells. This genetically determined diversity affects severity of infections, immune-mediated diseases, and some cancers, and impacts the course of cancer treatment, including transplantation. KIR3DL1 is an inhibitory receptor, and KIR3DS1 is an activating receptor encoded by the KIR3DL1/S1 gene that has more than 200 diverse and divergent alleles. Determination of KIR3DL1/S1 genotypes for medical application is hampered by complex sequence and structural variation that distinguishes individuals and populations, requiring targeted approaches to generate and analyze high-resolution allele data. To overcome these obstacles, we developed and optimized a model for imputing KIR3DL1/S1 alleles at high-resolution from whole-genome SNP data, and designed to represent a substantial component of human genetic diversity. We show that our Global model is effective at imputing KIR3DL1/S1 alleles with an accuracy ranging from 89% in Africans to 97% in East Asians, with mean specificity of 99.8% and sensitivity of 99% for named alleles >1% frequency. We used the established algorithm of the HIBAG program, in a modification named Pulling Out Natural killer cell Genomics (PONG). Because HIBAG was designed to impute HLA alleles also from whole-genome SNP data, PONG allows combinatorial diversity of KIR3DL1/S1 and HLA-A and B to be analyzed using complementary techniques on a single data source. The use of PONG thus negates the need for targeted sequencing data in very large-scale association studies where such methods might not be tractable. All code, imputation models, test data and documentation are available at https://github.com/NormanLabUCD/PONG.Author SummaryNatural killer (NK) cells are cytotoxic lymphocytes that identify and kill infected or malignant cells and guide immune responses. The effector functions of NK cells are modulated through polymorphic interactions of KIR3DL1/S1 on their surface with the human leukocyte antigens (HLA) that are found on most other cell types in the body. KIR3DL1/S1 is highly polymorphic and differentiated across human populations, affecting susceptibility and course of multiple immune-mediated diseases and their treatments. Genotyping KIR3DL1/S1 for direct medical application or research has been encumbered by the complex sequence and structural variation, which requires targeted approaches and extensive domain expertise to generate and validate high-resolution allele calls. We therefore developed Pulling Out Natural Killer Cell Genomics (PONG) to impute KIR3DL1/S1 alleles from whole genome SNP data, and which we implemented as an open-source R package. We assessed imputation performance using data from five broad population groups that represent a substantial portion of human genetic diversity. We can impute KIR3DL1/S1 alleles with an accuracy ranging from 89% in Africans and South Asians to 97% in East Asians. Globally, imputation of KIR3DL1/S1 alleles having frequency >1% has a mean sensitivity of 94% and specificity of 99.8%. Thus, the PONG method both enables highly sensitive individual-level calling and makes large scale medical genetic studies of KIR3DL1/S1 possible.