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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1221751/full |
| _version_ | 1797730751006900224 |
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| author | Seth A. Tolley Luiz F. Brito Diane R. Wang Mitchell R. Tuinstra |
| author_facet | Seth A. Tolley Luiz F. Brito Diane R. Wang Mitchell R. Tuinstra |
| author_sort | Seth A. Tolley |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T11:48:47Z |
| format | Article |
| id | doaj.art-85169c5b282f437aabc55cba4c034191 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-12T11:48:47Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-85169c5b282f437aabc55cba4c0341912023-08-31T11:45:39ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-08-011410.3389/fgene.2023.12217511221751Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm modelsSeth A. Tolley0Luiz F. Brito1Diane R. Wang2Mitchell R. Tuinstra3Department of Agronomy, Purdue University, West Lafayette, IN, United StatesDepartment of Animal Sciences, Purdue University, West Lafayette, IN, United StatesDepartment of Agronomy, Purdue University, West Lafayette, IN, United StatesDepartment of Agronomy, Purdue University, West Lafayette, IN, United StatesGenotype-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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1221751/fullgenomic predictiongenome-wide association studyGenomes to Fieldsmulti-environment trialgenotype-by-environment interaction |
| spellingShingle | Seth A. Tolley Luiz F. Brito Diane R. Wang Mitchell R. Tuinstra Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models Frontiers in Genetics genomic prediction genome-wide association study Genomes to Fields multi-environment trial genotype-by-environment interaction |
| 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 | genomic prediction genome-wide association study Genomes to Fields multi-environment trial genotype-by-environment interaction |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1221751/full |
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