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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #386539

Research Project: Improving Crop Efficiency Using Genomic Diversity and Computational Modeling

Location: Plant, Soil and Nutrition Research

Title: Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

Author
item WU, XI - Huazhong Agricultural University
item FENG, HUI - Huazhong Agricultural University
item WU, DI - Huazhong Agricultural University
item YAN, SHIJUAN - Guangdong Academy Of Agricultural Sciences
item ZHANG, PEI - Huazhong Agricultural University
item WANG, WENBIN - Huazhong Agricultural University
item ZHANG, JUN - Huazhong Agricultural University
item YE, JUNLI - Huazhong Agricultural University
item DAI, GUOXIN - Huazhong Agricultural University
item FAN, YUAN - Huazhong Agricultural University
item LI, WEIKUN - Huazhong Agricultural University
item SONG, BAOXING - Cornell University - New York
item GENG, ZEDONG - Huazhong Agricultural University
item YANG, WANLI - Huazhong Agricultural University
item CHEN, GUOXIN - Huazhong Agricultural University
item QIN, FENG - China Agricultural University
item TERZAGHI, WILLIAM - Wilkes University
item STITZER, MICHELLE - Cornell University - New York
item LI, LIN - Huazhong Agricultural University
item XIONG, LIZHONG - Huazhong Agricultural University
item YAN, JIANBING - Huazhong Agricultural University
item Buckler, Edward - Ed
item DAI, MINGQIU - Huazhong Agricultural University

Submitted to: Genome Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/10/2021
Publication Date: 6/24/2021
Citation: Wu, X., Feng, H., Wu, D., Yan, S., Zhang, P., Wang, W., Zhang, J., Ye, J., Dai, G., Fan, Y., Li, W., Song, B., Geng, Z., Yang, W., Chen, G., Qin, F., Terzaghi, W., Stitzer, M., Li, L., Xiong, L., Yan, J., Buckler IV, E.S., Dai, M. 2021. Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance. Genome Biology. 22(185):1-26. https://doi.org/10.1186/s13059-021-02377-0.
DOI: https://doi.org/10.1186/s13059-021-02377-0

Interpretive Summary: Most maize grown today is sensitive to drought, and varieties that can retain high yields in the face of high temperature and low rainfall are needed. To improve drought tolerance, we must first understand the genetic basis of drought response. Here, we took specialized photographs throughout maize development to compare the response of 368 maize varieties to drought stress. We identified genetic variants associated with drought tolerance, including genes involved in sugar metabolism. We identified individual genes and regions that can be targeted when breeding for drought tolerance. Further, this high-throughput photography based phenotyping approach generated over 10,000 phenotypes in a non-destructive manner, allowing comparison of drought response across time. The images, genotypes, and phenotypes produced here are publicly available, and can be used when testing new hypotheses about candidate drought responsive genes.

Technical Abstract: Background Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98'days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.