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Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis

BACKGROUND: Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling co...

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Autores principales: Jung, Woo Joo, Lee, Yong Jin, Kang, Chon-Sik, Seo, Yong Weon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436466/
https://www.ncbi.nlm.nih.gov/pubmed/34517837
http://dx.doi.org/10.1186/s12870-021-03180-6
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author Jung, Woo Joo
Lee, Yong Jin
Kang, Chon-Sik
Seo, Yong Weon
author_facet Jung, Woo Joo
Lee, Yong Jin
Kang, Chon-Sik
Seo, Yong Weon
author_sort Jung, Woo Joo
collection PubMed
description BACKGROUND: Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed. RESULTS: We observed major quantitative agronomic traits of wheat; the winter survival rate (WSR), days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles. CONCLUSIONS: From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-021-03180-6.
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spelling pubmed-84364662021-09-13 Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis Jung, Woo Joo Lee, Yong Jin Kang, Chon-Sik Seo, Yong Weon BMC Plant Biol Research BACKGROUND: Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed. RESULTS: We observed major quantitative agronomic traits of wheat; the winter survival rate (WSR), days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles. CONCLUSIONS: From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-021-03180-6. BioMed Central 2021-09-13 /pmc/articles/PMC8436466/ /pubmed/34517837 http://dx.doi.org/10.1186/s12870-021-03180-6 Text en © The Author(s) 2021 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
Jung, Woo Joo
Lee, Yong Jin
Kang, Chon-Sik
Seo, Yong Weon
Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title_full Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title_fullStr Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title_full_unstemmed Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title_short Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis
title_sort identification of genetic loci associated with major agronomic traits of wheat (triticum aestivum l.) based on genome-wide association analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436466/
https://www.ncbi.nlm.nih.gov/pubmed/34517837
http://dx.doi.org/10.1186/s12870-021-03180-6
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