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Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into...

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Autores principales: Tolley, Seth A., Brito, Luiz F., Wang, Diane R., Tuinstra, Mitchell R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501150/
https://www.ncbi.nlm.nih.gov/pubmed/37719703
http://dx.doi.org/10.3389/fgene.2023.1221751
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author Tolley, Seth A.
Brito, Luiz F.
Wang, Diane R.
Tuinstra, Mitchell R.
author_facet Tolley, Seth A.
Brito, Luiz F.
Wang, Diane R.
Tuinstra, Mitchell R.
author_sort Tolley, Seth A.
collection PubMed
description Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.
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spelling pubmed-105011502023-09-15 Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models Tolley, Seth A. Brito, Luiz F. Wang, Diane R. Tuinstra, Mitchell R. Front Genet Genetics Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction. Frontiers Media S.A. 2023-08-31 /pmc/articles/PMC10501150/ /pubmed/37719703 http://dx.doi.org/10.3389/fgene.2023.1221751 Text en Copyright © 2023 Tolley, Brito, Wang and Tuinstra. 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
Tolley, Seth A.
Brito, Luiz F.
Wang, Diane R.
Tuinstra, Mitchell R.
Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title_full Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title_fullStr Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title_full_unstemmed Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title_short Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
title_sort genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501150/
https://www.ncbi.nlm.nih.gov/pubmed/37719703
http://dx.doi.org/10.3389/fgene.2023.1221751
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