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Genomic selection for grain yield in maize (Zea mays L.) genotypes under non- stress and phosphorous deficiency stress conditions
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Mina Gholamiasl   , Reza Darvishzadeh , Amir Fayaz Moghaddam , Hadi Alipour |
| Urmia University, Urmia, Iran & Urmia University, Urmia, Iran |
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Abstract: (33 Views) |
Introduction: Maize is the third most important grain crop in the world for food and feed and high grain and biomass production is of importance to ensure food supply and security. Phosphorus deficiency stress in the soil reduces the root access to phosphorus, resulting in disrupttion of photosynthesis and reduction in plant performance. Due to the lack of availablity of adequate phosphorus for plants, the production and development of crop cultivars tolerant to phosphorus deficiency is very important objective in crop breeding programs. Since phenotypic selections are time-consuming with low efficiency, genotypic selections have been proposed as an alternative approach. In genomic selection, with the help of genome wide markers, the breeding value is estimated and selections are facilitated (Robertsen et al., 2019).
Materials and Methods: In this experiment, 93 maize genotypes were evaluated for grain yield using completely randomized design with three replications under non-stress and phosphorus deficiency treatments in potted conditions in an open area in Urmua University, Iran during the spring of 2022. The molecular profile of maize lines was prepared with SNP markers. The genomic breeding value for the yield was estimated with different statistical methods such as Genomic Best Linear Unbiased Prediction (GBLUP), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Bayesian Rigde regression, bayes A, bayes B, and bayes C. Correlation criteria was used to select the best model. According to the results, a high correlation was observed between the estimated breeding values obtained from rrBLUP and GBLUP and seed yield under both non-stress and phosphorus deficit stress conditions. Therefore, these methods were the best for predicting genomic breeding value (Estimated Genomic Breeding Values; EGBVs) in both conditions.
Results: Analysis of variance showed significant differences among maize genotypes for grain yield under both non-stress and low phosphorus stress conditions, indicating genetic variation in the studied maize genotypes for grain yield. Genetic variation in maize genotypes plays vital role in enhancing phosphorus use efficiency and grain yield under phosphorus deficit conditions. In predicting genomic breeding values for grain yield by using various statistical models under both non-stress and phosphorus deficiency stress conditions, high correlation coefficient was observed between the estimated breeding values by using Ridge Regression Best Linear Unbiased Prediction (rrBLUP) and Genomic Best Linear Unbiased Prediction (GBLUP) with grain yield in both conditions. Therefore, these methods are the best approaches for predicting genomic breeding values (EGBV) in both conditions. Based on the best methods, Ma064 had the highest genomic breeding value under non-stress conditions, while Ma022 had the highest genomic breeding value under phosphorus deficit conditions. Based on the results of factor analysis on genomic breeding values estimated by using statistical models, under non-stress conditions, the models were grouped into four main components, and under phosphorus deficiency stress conditions, they were grouped into three main components. Cluster analysis divided maize genotypes into three groups. The first group showed high breeding values and grain yield under both non-stress and phosphorus deficiency stress conditions. In the genome-wide association analysis for grain yield under non-stress and phosphorus deficiency stress conditions, four and five SNP markers were identified using the MLM method, respectively. Gene ontology analysis was performed for the gene associated with the identified SNPs, and the relationships of the genes were examined in the KEGG database. The pathways of Proteasome, Steroid biosynthesis, Ribosome, Porphyrin metabolism, and ABC transporters were identified to be associated with significant SNPs that potentially play a role in controlling grain yield under stress conditions. The genes located in these pathways were found on chromosomes 1, 2, 6, and 8.
Conclusion: In phosphorus deficiency stress conditions, the plant's roots have less access to phosphorus, consequently, photosynthesis and transpiration processes are affected, and ultimately leading to a significant decrease in plant performance. This issue was confirmed by comparing maize genotypes performance under non-stress and phosphorus deficiency conditions. Based on the coefficient of variation (CV), high variation was observed among the maize genotypes, and the genotypes under stress conditions exhibited different responses and showed high variability. Based on the resuts of genomic selection study, the Ridge Regression Best Linear Unbiased Prediction (rrBLUP) and Genomic Best Linear Unbiased Prediction (GBLUP) were the best methods for predicting genomic breeding values under both non-stress and phosphorus deficiency stress conditions. To identify the genes involved in controlling grain yield, the sequences of significant SNP were aligned againest the maize genome. Based on the gene ontology studies for potential genes, Proteasome, Steroid biosynthesis, Ribosome, Porphyrin metabolism, and ABC transporters pathways were identified to controlling the trait. The results of this experiment can be useful in selecting parental lines as well as effective genes for manipulation in maize breeding programs.
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| Keywords: Gene ontology, Genomic breeding value, Maize, Phosphorus deficiency and Single nucleotide polymorphism |
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Full-Text [PDF 1042 kb]
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Type of Study: Scientific & Research |
Subject:
Special
Received: 2025/01/31 | Accepted: 2025/05/19 | Published: 2025/07/1
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Gholamiasl M, Darvishzadeh R, Fayaz Moghaddam A, Alipour H. Genomic selection for grain yield in maize (Zea mays L.) genotypes under non- stress and phosphorous deficiency stress conditions. Iranian Journal of Crop Sciences. 2025; 26 (4) :369-389 URL: http://agrobreedjournal.ir/article-1-1405-en.html
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