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Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 singl...

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Autores principales: Muhammad, Ali, Hu, Weicheng, Li, Zhaoyang, Li, Jianguo, Xie, Guosheng, Wang, Jibin, Wang, Lingqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460857/
https://www.ncbi.nlm.nih.gov/pubmed/32781752
http://dx.doi.org/10.3390/ijms21165649
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author Muhammad, Ali
Hu, Weicheng
Li, Zhaoyang
Li, Jianguo
Xie, Guosheng
Wang, Jibin
Wang, Lingqiang
author_facet Muhammad, Ali
Hu, Weicheng
Li, Zhaoyang
Li, Jianguo
Xie, Guosheng
Wang, Jibin
Wang, Lingqiang
author_sort Muhammad, Ali
collection PubMed
description Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.
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spelling pubmed-74608572020-09-14 Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS Muhammad, Ali Hu, Weicheng Li, Zhaoyang Li, Jianguo Xie, Guosheng Wang, Jibin Wang, Lingqiang Int J Mol Sci Article Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat. MDPI 2020-08-06 /pmc/articles/PMC7460857/ /pubmed/32781752 http://dx.doi.org/10.3390/ijms21165649 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Muhammad, Ali
Hu, Weicheng
Li, Zhaoyang
Li, Jianguo
Xie, Guosheng
Wang, Jibin
Wang, Lingqiang
Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title_full Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title_fullStr Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title_full_unstemmed Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title_short Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS
title_sort appraising the genetic architecture of kernel traits in hexaploid wheat using gwas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460857/
https://www.ncbi.nlm.nih.gov/pubmed/32781752
http://dx.doi.org/10.3390/ijms21165649
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