Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS

Abstract Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Th...

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Autores principales:	Shufang Li, Chunxiao Zhang, Deguang Yang, Ming Lu, Yiliang Qian, Fengxue Jin, Xueyan Liu, Yu Wang, Wenguo Liu, Xiaohui Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b1963bf1b779438780174d09d690190b
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spelling	oai:doaj.org-article:b1963bf1b779438780174d09d690190b2021-12-02T10:49:29ZDetection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS10.1038/s41598-020-80391-12045-2322https://doaj.org/article/b1963bf1b779438780174d09d690190b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80391-1https://doaj.org/toc/2045-2322Abstract Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.Shufang LiChunxiao ZhangDeguang YangMing LuYiliang QianFengxue JinXueyan LiuYu WangWenguo LiuXiaohui LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shufang Li Chunxiao Zhang Deguang Yang Ming Lu Yiliang Qian Fengxue Jin Xueyan Liu Yu Wang Wenguo Liu Xiaohui Li Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
description	Abstract Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.
format	article
author	Shufang Li Chunxiao Zhang Deguang Yang Ming Lu Yiliang Qian Fengxue Jin Xueyan Liu Yu Wang Wenguo Liu Xiaohui Li
author_facet	Shufang Li Chunxiao Zhang Deguang Yang Ming Lu Yiliang Qian Fengxue Jin Xueyan Liu Yu Wang Wenguo Liu Xiaohui Li
author_sort	Shufang Li
title	Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
title_short	Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
title_full	Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
title_fullStr	Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
title_full_unstemmed	Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
title_sort	detection of qtns for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus gwas
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b1963bf1b779438780174d09d690190b
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Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS

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