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Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population’s hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced bre...

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Autores principales: Sethi, Mehak, Saini, Dinesh Kumar, Devi, Veena, Kaur, Charanjeet, Singh, Mohini Prabha, Singh, Jasneet, Pruthi, Gomsie, Kaur, Amanpreet, Singh, Alla, Chaudhary, Dharam Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440565/
https://www.ncbi.nlm.nih.gov/pubmed/37609038
http://dx.doi.org/10.3389/fgene.2023.1248697
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author Sethi, Mehak
Saini, Dinesh Kumar
Devi, Veena
Kaur, Charanjeet
Singh, Mohini Prabha
Singh, Jasneet
Pruthi, Gomsie
Kaur, Amanpreet
Singh, Alla
Chaudhary, Dharam Paul
author_facet Sethi, Mehak
Saini, Dinesh Kumar
Devi, Veena
Kaur, Charanjeet
Singh, Mohini Prabha
Singh, Jasneet
Pruthi, Gomsie
Kaur, Amanpreet
Singh, Alla
Chaudhary, Dharam Paul
author_sort Sethi, Mehak
collection PubMed
description Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population’s hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.
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spelling pubmed-104405652023-08-22 Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.) Sethi, Mehak Saini, Dinesh Kumar Devi, Veena Kaur, Charanjeet Singh, Mohini Prabha Singh, Jasneet Pruthi, Gomsie Kaur, Amanpreet Singh, Alla Chaudhary, Dharam Paul Front Genet Genetics Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population’s hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing. Frontiers Media S.A. 2023-08-07 /pmc/articles/PMC10440565/ /pubmed/37609038 http://dx.doi.org/10.3389/fgene.2023.1248697 Text en Copyright © 2023 Sethi, Saini, Devi, Kaur, Singh, Singh, Pruthi, Kaur, Singh and Chaudhary. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Sethi, Mehak
Saini, Dinesh Kumar
Devi, Veena
Kaur, Charanjeet
Singh, Mohini Prabha
Singh, Jasneet
Pruthi, Gomsie
Kaur, Amanpreet
Singh, Alla
Chaudhary, Dharam Paul
Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title_full Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title_fullStr Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title_full_unstemmed Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title_short Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)
title_sort unravelling the genetic framework associated with grain quality and yield-related traits in maize (zea mays l.)
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440565/
https://www.ncbi.nlm.nih.gov/pubmed/37609038
http://dx.doi.org/10.3389/fgene.2023.1248697
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