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Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations

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 (HSCT) outcome. Owing to the complexity of the KIR locus, with extensive gene copy number varia...

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Autores principales: 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, Hollenbach, Jill A., Schetelig, Johannes, Pingel, Julia, Norman, Paul J., Sauter, Jürgen, Schmidt, Alexander H., Lange, Vinzenz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288436/
https://www.ncbi.nlm.nih.gov/pubmed/30564239
http://dx.doi.org/10.3389/fimmu.2018.02843
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author 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
Hollenbach, Jill A.
Schetelig, Johannes
Pingel, Julia
Norman, Paul J.
Sauter, Jürgen
Schmidt, Alexander H.
Lange, Vinzenz
author_facet 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
Hollenbach, Jill A.
Schetelig, Johannes
Pingel, Julia
Norman, Paul J.
Sauter, Jürgen
Schmidt, Alexander H.
Lange, Vinzenz
author_sort Wagner, Ines
collection PubMed
description 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 (HSCT) outcome. Owing to the complexity of the KIR locus, with extensive gene copy number variation (CNV) 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 HSCT. 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.
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spelling pubmed-62884362018-12-18 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 Hollenbach, Jill A. Schetelig, Johannes Pingel, Julia Norman, Paul J. Sauter, Jürgen Schmidt, Alexander H. Lange, Vinzenz Front Immunol Immunology 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 (HSCT) outcome. Owing to the complexity of the KIR locus, with extensive gene copy number variation (CNV) 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 HSCT. 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. Frontiers Media S.A. 2018-12-04 /pmc/articles/PMC6288436/ /pubmed/30564239 http://dx.doi.org/10.3389/fimmu.2018.02843 Text en Copyright © 2018 Wagner, Schefzyk, Pruschke, Schöfl, Schöne, Gruber, Lang, Hofmann, Gnahm, Heyn, Marin, Dandekar, Hollenbach, Schetelig, Pingel, Norman, Sauter, Schmidt and Lange. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
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
Hollenbach, Jill A.
Schetelig, Johannes
Pingel, Julia
Norman, Paul J.
Sauter, Jürgen
Schmidt, Alexander H.
Lange, Vinzenz
Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title_full Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title_fullStr Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title_full_unstemmed Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title_short Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations
title_sort allele-level kir genotyping of more than a million samples: workflow, algorithm, and observations
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288436/
https://www.ncbi.nlm.nih.gov/pubmed/30564239
http://dx.doi.org/10.3389/fimmu.2018.02843
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