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An imputation platform to enhance integration of rice genetic resources
As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of inte...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6115364/ https://www.ncbi.nlm.nih.gov/pubmed/30158584 http://dx.doi.org/10.1038/s41467-018-05538-1 |
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author | Wang, Diane R. Agosto-Pérez, Francisco J. Chebotarov, Dmytro Shi, Yuxin Marchini, Jonathan Fitzgerald, Melissa McNally, Kenneth L. Alexandrov, Nickolai McCouch, Susan R. |
author_facet | Wang, Diane R. Agosto-Pérez, Francisco J. Chebotarov, Dmytro Shi, Yuxin Marchini, Jonathan Fitzgerald, Melissa McNally, Kenneth L. Alexandrov, Nickolai McCouch, Susan R. |
author_sort | Wang, Diane R. |
collection | PubMed |
description | As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set. |
format | Online Article Text |
id | pubmed-6115364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61153642018-08-31 An imputation platform to enhance integration of rice genetic resources Wang, Diane R. Agosto-Pérez, Francisco J. Chebotarov, Dmytro Shi, Yuxin Marchini, Jonathan Fitzgerald, Melissa McNally, Kenneth L. Alexandrov, Nickolai McCouch, Susan R. Nat Commun Article As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set. Nature Publishing Group UK 2018-08-29 /pmc/articles/PMC6115364/ /pubmed/30158584 http://dx.doi.org/10.1038/s41467-018-05538-1 Text en © The Author(s) 2018 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Diane R. Agosto-Pérez, Francisco J. Chebotarov, Dmytro Shi, Yuxin Marchini, Jonathan Fitzgerald, Melissa McNally, Kenneth L. Alexandrov, Nickolai McCouch, Susan R. An imputation platform to enhance integration of rice genetic resources |
title | An imputation platform to enhance integration of rice genetic resources |
title_full | An imputation platform to enhance integration of rice genetic resources |
title_fullStr | An imputation platform to enhance integration of rice genetic resources |
title_full_unstemmed | An imputation platform to enhance integration of rice genetic resources |
title_short | An imputation platform to enhance integration of rice genetic resources |
title_sort | imputation platform to enhance integration of rice genetic resources |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6115364/ https://www.ncbi.nlm.nih.gov/pubmed/30158584 http://dx.doi.org/10.1038/s41467-018-05538-1 |
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