Cargando…
QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population
Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-relate...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938492/ https://www.ncbi.nlm.nih.gov/pubmed/24586932 http://dx.doi.org/10.1371/journal.pone.0089645 |
_version_ | 1782305611035181056 |
---|---|
author | Zhang, Zhanhui Liu, Zonghua Hu, Yanmin Li, Weihua Fu, Zhiyuan Ding, Dong Li, Haochuan Qiao, Mengmeng Tang, Jihua |
author_facet | Zhang, Zhanhui Liu, Zonghua Hu, Yanmin Li, Weihua Fu, Zhiyuan Ding, Dong Li, Haochuan Qiao, Mengmeng Tang, Jihua |
author_sort | Zhang, Zhanhui |
collection | PubMed |
description | Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F(2) (IF(2)) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%). |
format | Online Article Text |
id | pubmed-3938492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39384922014-03-04 QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population Zhang, Zhanhui Liu, Zonghua Hu, Yanmin Li, Weihua Fu, Zhiyuan Ding, Dong Li, Haochuan Qiao, Mengmeng Tang, Jihua PLoS One Research Article Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F(2) (IF(2)) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%). Public Library of Science 2014-02-28 /pmc/articles/PMC3938492/ /pubmed/24586932 http://dx.doi.org/10.1371/journal.pone.0089645 Text en © 2014 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zhang, Zhanhui Liu, Zonghua Hu, Yanmin Li, Weihua Fu, Zhiyuan Ding, Dong Li, Haochuan Qiao, Mengmeng Tang, Jihua QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title | QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title_full | QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title_fullStr | QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title_full_unstemmed | QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title_short | QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F(2) Population |
title_sort | qtl analysis of kernel-related traits in maize using an immortalized f(2) population |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938492/ https://www.ncbi.nlm.nih.gov/pubmed/24586932 http://dx.doi.org/10.1371/journal.pone.0089645 |
work_keys_str_mv | AT zhangzhanhui qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT liuzonghua qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT huyanmin qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT liweihua qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT fuzhiyuan qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT dingdong qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT lihaochuan qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT qiaomengmeng qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population AT tangjihua qtlanalysisofkernelrelatedtraitsinmaizeusinganimmortalizedf2population |