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Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population

BACKGROUND: Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize. RESULTS: Here, we measured KRN in four environments using a nest...

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Autores principales: Fei, Xiaohong, Wang, Yifei, Zheng, Yunxiao, Shen, Xiaomeng, E, Lizhu, Ding, Junqiang, Lai, Jinsheng, Song, Weibin, Zhao, Haiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380338/
https://www.ncbi.nlm.nih.gov/pubmed/35971070
http://dx.doi.org/10.1186/s12864-022-08793-1
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author Fei, Xiaohong
Wang, Yifei
Zheng, Yunxiao
Shen, Xiaomeng
E, Lizhu
Ding, Junqiang
Lai, Jinsheng
Song, Weibin
Zhao, Haiming
author_facet Fei, Xiaohong
Wang, Yifei
Zheng, Yunxiao
Shen, Xiaomeng
E, Lizhu
Ding, Junqiang
Lai, Jinsheng
Song, Weibin
Zhao, Haiming
author_sort Fei, Xiaohong
collection PubMed
description BACKGROUND: Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize. RESULTS: Here, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN. CONCLUSIONS: This study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08793-1.
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spelling pubmed-93803382022-08-17 Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population Fei, Xiaohong Wang, Yifei Zheng, Yunxiao Shen, Xiaomeng E, Lizhu Ding, Junqiang Lai, Jinsheng Song, Weibin Zhao, Haiming BMC Genomics Research BACKGROUND: Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize. RESULTS: Here, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN. CONCLUSIONS: This study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08793-1. BioMed Central 2022-08-15 /pmc/articles/PMC9380338/ /pubmed/35971070 http://dx.doi.org/10.1186/s12864-022-08793-1 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 Research
Fei, Xiaohong
Wang, Yifei
Zheng, Yunxiao
Shen, Xiaomeng
E, Lizhu
Ding, Junqiang
Lai, Jinsheng
Song, Weibin
Zhao, Haiming
Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title_full Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title_fullStr Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title_full_unstemmed Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title_short Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population
title_sort identification of two new qtls of maize (zea mays l.) underlying kernel row number using the hnau-nam1 population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380338/
https://www.ncbi.nlm.nih.gov/pubmed/35971070
http://dx.doi.org/10.1186/s12864-022-08793-1
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