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Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds

BACKGROUND: Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the au...

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Autores principales: Qu, Yanzhi, Liu, Zonghua, Zhang, Yazhou, Yang, Jiwei, Li, Haochuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789371/
https://www.ncbi.nlm.nih.gov/pubmed/33407640
http://dx.doi.org/10.1186/s13007-020-00703-4
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author Qu, Yanzhi
Liu, Zonghua
Zhang, Yazhou
Yang, Jiwei
Li, Haochuan
author_facet Qu, Yanzhi
Liu, Zonghua
Zhang, Yazhou
Yang, Jiwei
Li, Haochuan
author_sort Qu, Yanzhi
collection PubMed
description BACKGROUND: Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the automatic haploid sorting based on nuclear magnetic resonance (NMR) using a single threshold method has been realized. However, embryo-aborted (EmA) kernels are usually produced during in vivo haploid induction, and both haploids and EmA kernels have lower oil content and are separated together using a single threshold method based on NMR. This leads to a higher haploid false discrimination rate (FDR) and requires secondary manual sorting to select the haploid kernels from the mixtures, which increases the sorting cost and decreases the haploid sorting efficiency. In order to improve the correct discrimination rate (CDR) in sorting haploids, a method to distinguish EmA kernels is required. RESULTS: Single kernel weight and oil content were measured for the diploid, haploid, and EmA kernels derived from three maize hybrids and nine inbred lines by in vivo induction. The results showed that the distribution of oil content showed defined boundaries between the three types of kernels, while the single kernel weight didn't. According to the distribution of oil content in the three types of kernels, a double-threshold method was proposed to distinguish the embryo-aborted kernels, haploid and diploid kernels based on NMR and their oil content. The double thresholds were set based on the minimum oil content of diploid kernels and the maximum content of EmA kernels as the upper and lower boundary values, respectively. The CDR of EmA kernels in different maize materials was > 97.8%, and the average FDR was reduced by 27.9 percent. CONCLUSIONS: The oil content is an appropriate indicator to discriminate diploid, haploid and EmA kernels. An oil content double-threshold method based on NMR was first developed in this study to identify the three types of kernels. This methodology could reduce the FDR of haploids and improve the sorting efficiency of automated sorting system. Thus, this technique represents a potentially efficient method for haploid sorting and provides a reference for the process of automated sorting of haploid kernels with high efficiency using NMR.
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spelling pubmed-77893712021-01-07 Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds Qu, Yanzhi Liu, Zonghua Zhang, Yazhou Yang, Jiwei Li, Haochuan Plant Methods Methodology BACKGROUND: Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the automatic haploid sorting based on nuclear magnetic resonance (NMR) using a single threshold method has been realized. However, embryo-aborted (EmA) kernels are usually produced during in vivo haploid induction, and both haploids and EmA kernels have lower oil content and are separated together using a single threshold method based on NMR. This leads to a higher haploid false discrimination rate (FDR) and requires secondary manual sorting to select the haploid kernels from the mixtures, which increases the sorting cost and decreases the haploid sorting efficiency. In order to improve the correct discrimination rate (CDR) in sorting haploids, a method to distinguish EmA kernels is required. RESULTS: Single kernel weight and oil content were measured for the diploid, haploid, and EmA kernels derived from three maize hybrids and nine inbred lines by in vivo induction. The results showed that the distribution of oil content showed defined boundaries between the three types of kernels, while the single kernel weight didn't. According to the distribution of oil content in the three types of kernels, a double-threshold method was proposed to distinguish the embryo-aborted kernels, haploid and diploid kernels based on NMR and their oil content. The double thresholds were set based on the minimum oil content of diploid kernels and the maximum content of EmA kernels as the upper and lower boundary values, respectively. The CDR of EmA kernels in different maize materials was > 97.8%, and the average FDR was reduced by 27.9 percent. CONCLUSIONS: The oil content is an appropriate indicator to discriminate diploid, haploid and EmA kernels. An oil content double-threshold method based on NMR was first developed in this study to identify the three types of kernels. This methodology could reduce the FDR of haploids and improve the sorting efficiency of automated sorting system. Thus, this technique represents a potentially efficient method for haploid sorting and provides a reference for the process of automated sorting of haploid kernels with high efficiency using NMR. BioMed Central 2021-01-06 /pmc/articles/PMC7789371/ /pubmed/33407640 http://dx.doi.org/10.1186/s13007-020-00703-4 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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 Methodology
Qu, Yanzhi
Liu, Zonghua
Zhang, Yazhou
Yang, Jiwei
Li, Haochuan
Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title_full Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title_fullStr Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title_full_unstemmed Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title_short Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds
title_sort improving the sorting efficiency of maize haploid kernels using an nmr-based method with oil content double thresholds
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789371/
https://www.ncbi.nlm.nih.gov/pubmed/33407640
http://dx.doi.org/10.1186/s13007-020-00703-4
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