|XU, YADONG - Huazhong Agricultural University
|SAWKINS, MARK - International Maize & Wheat Improvement Center (CIMMYT)
|ZHANG, XUEHAI - Huazhong Agricultural University
|SETTER, TIM - Cornell University
|XU, YUNBI - International Maize & Wheat Improvement Center (CIMMYT)
|GRUDLOYMA, PICHET - Nakhon Sawan Field Crops Research Center
|GETHI, JAMES - Kenya Agricultural Research Institute
|RIBAUT, JEAN-MARCEL - International Maize & Wheat Improvement Center (CIMMYT)
|LI, WANCHEN - Sichuan University
|ZHANG, XIAOBO - Huazhong Agricultural University
|ZHENG, YONGLIAN - Huazhong Agricultural University
|YAN, JIANBING - Huazhong Agricultural University
Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/12/2013
Publication Date: 7/25/2013
Citation: Xu, Y., Warburton, M.L., Sawkins, M., Zhang, X., Setter, T., Xu, Y., Grudloyma, P., Gethi, J., Ribaut, J., Li, W., Zhang, X., Zheng, Y., Yan, J. 2013. Genome-wide association analysis for non agronomic traits in maize under well-watered and water-stressed conditions. Theoretical and Applied Genetics. 126(10):2587-96. DOI 10.1007/s00122-013-2158-x.
Interpretive Summary: Drought is the biggest problem in maize production worldwide, and drought resistant maize would be a boon to farmers and food security in many countries of the world. Breeding for drought tolerance is very hard, because the positive allele of many genes must be selected for at the same time; in addition, maize plants that look very resistant one year or in one field may not look good in a different field or year. Therefore, selecting for drought resistant plants based on molecular markers, which do not change from year to year, would be a more efficient way for plant breeders to improve this trait. The current study uses association mapping to find SNP molecular markers associated with drought tolerance. Several markers were identified in the current study, and the genes they fall within are presented along with potential drought resistance mechanisms. In addition, the authors present a way to use all the markers that were genotyped in the course of the study to select for drought tolerance in a practical breeding program, in a more efficient manner than selecting on the phenotype of the plant; selecting random markers; or selecting using only the most highly associated markers. This will allow all data generated during the course of this study to be utilized in the most efficient manner possible.
Technical Abstract: Drought is the most serious environmental stress facing maize production, and strongly threatens crop yields. Changes in agronomic traits in response to this stress have been extensively studied using biparental mapping populations and inbred lines, but little is known about the related genes and underlying mechanisms. To dissect this complex trait and identify superior alleles, 350 tropical and subtropical maize inbred lines were genotyped using an 1536-SNP array developed from drought-related genes and an array of 56,110 random SNPs. The inbred lines were crossed with a common tester, CML312, and the F1s were phenotyped for nine traits under well-watered and water-stressed conditions in seven environments. Using genome-wide association mapping with correction for population structure, we identified 275 associated SNPs (P = 2.25 x 10-5 | 1/N), representing 221 genes. Of these genes, 38 were co-localized to drought-related QTL regions. Gene GRMZM2G087186 was strongly associated with timing of male and female flowering (an indicator of drought tolerance), and encodes pyruvate decarboxylase subunit 3 (PDC3), a rate-limiting enzyme of carbon flux into anaerobic fermentation. Three genes that play a key role in seed development and maturation—GRMZM2G021051, GRMZM2G067646, and GRMZM2G109405—were associated with grain-yield related traits under water-stressed conditions. Understanding the function of these genes should illuminate mechanisms of drought tolerance and provide tools for designing drought-tolerant maize cultivars tailored to different environmental scenarios. Computations of the optimal number and quality of SNPs for genome prediction analysis are presented, to aid in the identification of improved maize inbred line and hybrid parents.