Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs
Chilling injury during the germination stage (CIGS) of maize significantly hinders production, particularly in middle- and high-latitude regions, leading to slow germination, seed decay, and increased susceptibility to pathogens. This study dissects the genetic architecture of CIGS resistance expres...
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MDPI AG
2024-11-01
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| Series: | Agriculture |
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| author | Shiliang Cao Tao Yu Gengbin Yang Wenyue Li Xuena Ma Jianguo Zhang |
| author_facet | Shiliang Cao Tao Yu Gengbin Yang Wenyue Li Xuena Ma Jianguo Zhang |
| author_sort | Shiliang Cao |
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| description | Chilling injury during the germination stage (CIGS) of maize significantly hinders production, particularly in middle- and high-latitude regions, leading to slow germination, seed decay, and increased susceptibility to pathogens. This study dissects the genetic architecture of CIGS resistance expressed in terms of the relative germination rate (RGR) in maize through association mapping using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). A natural panel of 287 maize inbred lines was evaluated across multiple environments. The results revealed a broad-sense heritability of 0.68 for chilling tolerance, with 12 significant QTLs identified on chromosomes 1, 3, 5, 6, and 10. A genomic prediction analysis demonstrated that the rr-BLUP model outperformed other models in accuracy, achieving a moderate prediction accuracy of 0.44. This study highlights the potential of genomic selection (GS) to enhance chilling tolerance in maize, emphasizing the importance of training population size, marker density, and significant markers on prediction accuracy. These findings provide valuable insights for breeding programs aimed at improving chilling tolerance in maize. |
| format | Article |
| id | doaj-art-f5bbb04dcd074331a965b0e0f893ad8a |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-f5bbb04dcd074331a965b0e0f893ad8a2025-08-20T02:26:51ZengMDPI AGAgriculture2077-04722024-11-011411204810.3390/agriculture14112048Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPsShiliang Cao0Tao Yu1Gengbin Yang2Wenyue Li3Xuena Ma4Jianguo Zhang5Maize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaMaize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaMaize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaMaize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaMaize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaMaize Research Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, ChinaChilling injury during the germination stage (CIGS) of maize significantly hinders production, particularly in middle- and high-latitude regions, leading to slow germination, seed decay, and increased susceptibility to pathogens. This study dissects the genetic architecture of CIGS resistance expressed in terms of the relative germination rate (RGR) in maize through association mapping using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). A natural panel of 287 maize inbred lines was evaluated across multiple environments. The results revealed a broad-sense heritability of 0.68 for chilling tolerance, with 12 significant QTLs identified on chromosomes 1, 3, 5, 6, and 10. A genomic prediction analysis demonstrated that the rr-BLUP model outperformed other models in accuracy, achieving a moderate prediction accuracy of 0.44. This study highlights the potential of genomic selection (GS) to enhance chilling tolerance in maize, emphasizing the importance of training population size, marker density, and significant markers on prediction accuracy. These findings provide valuable insights for breeding programs aimed at improving chilling tolerance in maize.https://www.mdpi.com/2077-0472/14/11/2048maizechilling tolerancegerminationgenomic predictionSNPassociation mapping |
| spellingShingle | Shiliang Cao Tao Yu Gengbin Yang Wenyue Li Xuena Ma Jianguo Zhang Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs Agriculture maize chilling tolerance germination genomic prediction SNP association mapping |
| title | Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs |
| title_full | Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs |
| title_fullStr | Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs |
| title_full_unstemmed | Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs |
| title_short | Genome-Wide Analysis and Genomic Prediction of Chilling Tolerance of Maize During Germination Stage Using Genotyping-by-Sequencing SNPs |
| title_sort | genome wide analysis and genomic prediction of chilling tolerance of maize during germination stage using genotyping by sequencing snps |
| topic | maize chilling tolerance germination genomic prediction SNP association mapping |
| url | https://www.mdpi.com/2077-0472/14/11/2048 |
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