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Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE

BACKGROUND: Natural killer (NK) cells represent a critical component of the innate immune system’s response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize...

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Autores principales: Gao, Galen F., Liu, Dajiang, Zhan, Xiaowei, Li, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400285/
https://www.ncbi.nlm.nih.gov/pubmed/36002830
http://dx.doi.org/10.1186/s12915-022-01392-2
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author Gao, Galen F.
Liu, Dajiang
Zhan, Xiaowei
Li, Bo
author_facet Gao, Galen F.
Liu, Dajiang
Zhan, Xiaowei
Li, Bo
author_sort Gao, Galen F.
collection PubMed
description BACKGROUND: Natural killer (NK) cells represent a critical component of the innate immune system’s response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. However, NK cells exhibit great diversity in their mechanism of activation, and the outcomes of their activation are not yet understood fully. Just like the HLAs they bind, KIR receptors exhibit high allelic diversity in the human population. Here we provide a method to identify KIR allele variants from whole exome sequencing data and uncover novel associations between these variants and various molecular and clinical correlates. RESULTS: In order to better understand KIRs, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately sixty-thousand patient samples in The Cancer Genome Atlas (TCGA) and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals’ KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher’s exact FDR = 7.64e−51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher’s exact FDR = 0.0429) and age of onset of atopy (Mann-Whitney U FDR = 0.0751). CONCLUSIONS: We show that KIRCLE is able to infer KIR variants accurately and consistently, and we demonstrate its utility using data from approximately sixty-thousand individuals from TCGA and UK Biobank to discover novel molecular and clinical correlations with KIR germline variants. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their “classical” realm of anti-tumor and anti-viral responses. This tool may be used both as a benchmark for future KIR-variant-inference algorithms, and to better understand the immunogenomics of and disease processes involving KIRs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-022-01392-2.
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spelling pubmed-94002852022-08-25 Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE Gao, Galen F. Liu, Dajiang Zhan, Xiaowei Li, Bo BMC Biol Software BACKGROUND: Natural killer (NK) cells represent a critical component of the innate immune system’s response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. However, NK cells exhibit great diversity in their mechanism of activation, and the outcomes of their activation are not yet understood fully. Just like the HLAs they bind, KIR receptors exhibit high allelic diversity in the human population. Here we provide a method to identify KIR allele variants from whole exome sequencing data and uncover novel associations between these variants and various molecular and clinical correlates. RESULTS: In order to better understand KIRs, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately sixty-thousand patient samples in The Cancer Genome Atlas (TCGA) and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals’ KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher’s exact FDR = 7.64e−51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher’s exact FDR = 0.0429) and age of onset of atopy (Mann-Whitney U FDR = 0.0751). CONCLUSIONS: We show that KIRCLE is able to infer KIR variants accurately and consistently, and we demonstrate its utility using data from approximately sixty-thousand individuals from TCGA and UK Biobank to discover novel molecular and clinical correlations with KIR germline variants. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their “classical” realm of anti-tumor and anti-viral responses. This tool may be used both as a benchmark for future KIR-variant-inference algorithms, and to better understand the immunogenomics of and disease processes involving KIRs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-022-01392-2. BioMed Central 2022-08-24 /pmc/articles/PMC9400285/ /pubmed/36002830 http://dx.doi.org/10.1186/s12915-022-01392-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Gao, Galen F.
Liu, Dajiang
Zhan, Xiaowei
Li, Bo
Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title_full Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title_fullStr Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title_full_unstemmed Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title_short Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE
title_sort analysis of kir gene variants in the cancer genome atlas and uk biobank using kircle
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400285/
https://www.ncbi.nlm.nih.gov/pubmed/36002830
http://dx.doi.org/10.1186/s12915-022-01392-2
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